This guide compares seven options available to UK businesses, with pricing checked in October 2026, so you can match the right one to your call volume, budget and tech stack.

TL;DR

The best virtual receptionist depends on who should answer: a person, an AI, or both. Live services in this guide start from £15 a month, with per-call or per-minute charges once you exceed your bundle. AI receptionists answer 24/7, cost less per call, and hand complex or sensitive calls to your team.

• Best premium live + AI: Moneypenny, for brand-sensitive firms that want a dedicated UK team.

• Best quote-based live answering: alldayPA, for businesses that want a human team with per-call or per-minute billing.

• Best AI receptionist inside your phone system and CRM: Aircall, for teams that want calls answered, booked and logged in the tools they already use.

• Best flat-rate AI for small businesses: Hey Jodie, for tradespeople and local firms that want unlimited minutes.

• Best AI + human backup for legal intake: Smith.ai.

• Best 24/7 live answering with web chat: AnswerConnect.

• Best budget message-taking: ReceptionHQ.

Virtual receptionist services compared

What are the best virtual receptionist services in the UK?

Each service below is grouped by the job it does best. Prices are monthly list prices in GBP and usually exclude VAT.

1. Moneypenny

Moneypenny is one of the UK's best-known answering brands, offering dedicated live receptionists alongside an AI receptionist.

Founded in Wrexham in 2000, Moneypenny assigns a small team of receptionists who learn your business, and now sells an AI receptionist for routine calls. It's the premium choice for firms where the voice on the phone is part of the brand.

Best for: Law firms, estate agents and professional services that want a recognisably premium front desk.

Key features:

• Dedicated receptionist team: A small UK-based team learns your business and callers.

• AI receptionist: Handles routine calls, with the option to bring in the human team.

• Live chat: Website chat handled by the same team.

Pricing:

• AI receptionist: From £49/month, with full pricing by quote.

• Live receptionists: Quote-based. Moneypenny doesn't publish a UK rate card.

Watch out for: Pricing is opaque until you request a quote, and 24/7 cover and human backup for the AI can cost extra.

2. alldayPA

alldayPA is a UK live answering service that prices by quote, with per-call or per-minute billing.

Its receptionists answer in your business name, take messages and transfer calls. It suits businesses that want a human team rather than the lowest flat fee.

Best for: Small and mid-sized UK firms that want a human team and are comfortable with a contract.

Key features:

• Live receptionists: Calls answered in your company name.

• Flexible billing: Per-call or per-minute billing, agreed in your quote.

• Call transfers: Callers put through to the right person.

Pricing: Quote-based. alldayPA doesn't publish a rate card.

Watch out for: Pricing isn't published, and third-party reviews report minimum contract terms, so confirm the term before you sign.

3. Aircall

Aircall's AI receptionist answers every call, books appointments on the call and routes callers to the right teammate with full context, all inside the phone system and CRM your team already uses.

Most answering services stop at taking a message. Aircall's AI receptionist resolves the request when it can, then logs the call, transcript and outcome in your CRM. When a caller needs a person, it transfers them to your team with the full conversation attached, so no one repeats themselves.

It's one of several AI voice agents on the platform. Teams can add an AI Sales Agent to qualify inbound leads, an AI Support Agent to resolve common questions, or an AI Order Agent for ecommerce order calls.

Best for: Sales and support teams that want an AI receptionist connected to their CRM, helpdesk and intelligent routing, not a separate answering service.

Key features:

• Natural conversation and intelligent routing: Callers say what they need in their own words, no phone menu, and the AI resolves the request or routes the call with context.

• Appointment booking and reminders: Schedules, reschedules and cancels appointments on the call, and rings ahead to confirm attendance.

• 38 languages: Answers in the caller's language at any hour.

• CRM logging: Verifies the caller, updates records and logs every call in tools like HubSpot, Salesforce and Zendesk.

• Set up in minutes: Start from a template, add your business information and knowledge sources, and go live without IT.

• Agent Performance dashboard: Tracks resolution, goal completion and transfer rates, and flags fixes ranked by impact.

Integrations: 250+ native integrations, including HubSpot, Salesforce, Zendesk, Shopify, Calendly and Stripe.

Pricing (UK):

• With an Aircall phone plan: Plans from £25/licence/month (billed annually), including 50 free AI Voice Agent minutes every month plus 100 at sign-up.

• AI Voice Agents on their own: £76/month minimum, no Aircall phone system needed.

• Usage beyond free minutes: £0.37/minute up to 2,500 minutes, then £0.29/minute.

• Bundles: £130 for 500 minutes, £550 for 2,500 minutes, custom rates from £0.14/minute above 10,000 minutes.

See the full Aircall pricing page for phone system plans.

Results from teams already live: SJWD Water District handles 90% of after-hours calls automatically, and Aircall measured 95% caller satisfaction across 18,000 AI routing calls.

Watch out for: Per-minute billing means long calls cost more, and standalone use has a monthly minimum. Clear transfer rules keep complex calls short and with your team.

4. Hey Jodie

Hey Jodie is a UK AI receptionist with flat monthly pricing and unlimited minutes, built for small businesses and trades.

Jodie answers calls 24/7, captures job details and sends them on by text or email. Flat pricing makes costs predictable for businesses with busy, unpredictable phones.

Best for: Tradespeople, estate agents and local businesses that want AI answering without a per-minute meter.

Key features:

• Unlimited minutes: Every plan includes unlimited call minutes.

• Job capture: Collects caller details and sends notifications.

• Free trial: Seven days free, with no setup fees.

Pricing:

• Basic: £49/month

• Professional: £99/month

• Premium: £199/month, with integrations via Zapier

• Enterprise: From £500/month

Watch out for: It's a standalone AI answering tool, so there's no phone system, call centre tooling or native CRM integration behind it.

5. Smith.ai

Smith.ai combines an AI receptionist with North America-based live receptionists, so routine calls stay automated and sensitive calls reach a person.

The AI answers first, handles FAQs and intake, and escalates to a human when a call needs judgement. It's popular with law firms for its intake tools and Clio integration.

Best for: Legal and professional services firms that want detailed intake with a human safety net.

Key features:

• Lead intake and qualification: Custom intake questions screen callers before they reach you.

• Legal-specific add-ons: Conflict checks, payment collection and third-party intake forms.

• Free AI tier: Test the AI receptionist before paying.

Integrations include: HubSpot, Salesforce, Clio and Zapier.

Pricing (billed in USD):

• AI Receptionist: Free for 25 calls/month, Pro from $150/month for 75 calls, Enterprise from $500/month.

• Live receptionists: From $300/month for 30 calls.

Watch out for: Billing is in US dollars and live receptionists work from North America, which can matter for UK callers and accents.

6. AnswerConnect

AnswerConnect provides 24/7 live receptionists who answer calls and web chats on your behalf.

Receptionists handle routing, appointment booking and lead qualification, with call logs and recordings in a client portal.

Best for: Businesses that want round-the-clock live answering plus website chat.

Key features:

• 24/7 live answering: Calls answered day and night.

• Appointment booking: Receptionists schedule and manage appointments.

• Client portal: Call logs, transcripts and recordings on web and mobile.

Pricing: From £45/month. Full plans are shown after you submit your details.

Watch out for: Per-minute billing above your bundle adds up quickly in a busy month.

7. ReceptionHQ

ReceptionHQ offers low-cost live answering plans, from simple message-taking to call transfers and diary management.

Its tiered plans let small businesses start with message-taking and add transfers, lead capture or appointment booking as they grow.

Best for: Sole traders and small businesses that mainly need messages taken professionally.

Key features:

• Message-taking: Calls answered in your business name, with messages sent by email or text.

• Call transfers: Callers put through to you when you're available.

• Appointment scheduling: Bookings and cancellations managed in your diary.

Pricing:

• MessageExpress: From £15/month

• MyReceptionist: From £16/month, with call transfers

• MyAssistant and MyDiary: From £30/month

Watch out for: Plans are low-cost entry points, and per-call charges apply on top as volume grows.

How we picked these services

We shortlisted services that appear most often in UK comparisons of "virtual receptionist" and "AI receptionist" searches, then compared them on four things:

• Who answers: AI, live person, or both.

• Published pricing: Every price was checked on each provider's own website in October 2026. Where a provider doesn't publish prices, we say so rather than guess.

• Integrations: Native CRM, helpdesk and calendar connections.

• Handoff to humans: How cleanly a call reaches your team when it needs to.

Aircall makes this list, so weigh our pick accordingly. We've placed it where it fits best, not first.

Should you choose an AI receptionist or a live virtual receptionist?

Choose an AI receptionist if most of your calls are routine: bookings, FAQs, routing and lead capture. Choose a live receptionist if your calls are emotional, high-value or need judgement every time. Many UK firms now use AI first and send exceptions to a person.

Read more on AI voice agents vs. human agents and what an AI voice agent is.

How much does a virtual receptionist cost in the UK?

Published plans in this guide range from £15 to £199 a month before usage charges, with quote-based live teams priced separately. The pricing model matters as much as the headline price:

To estimate your cost, multiply your monthly calls by your average call length, then compare that against each plan's included minutes, overage rate and minimum term.

What should you look for in a virtual receptionist service?

• Handoff quality: The caller should reach a person with full context, not start over.

• Integrations: Calls, transcripts and outcomes should land in your CRM and helpdesk automatically.

• Appointment booking: Look for live diary access, not just message-taking.

• Out-of-hours cover: Check whether evenings, weekends and bank holidays cost extra.

• Contract terms: Many UK services set minimum terms and notice periods.

• Data protection: Check encryption, access controls and how call data is handled under UK GDPR. Aircall's approach is on its security page.

• Overage terms: Check what happens when you exceed your plan, since that's where costs spike.

Answer every call with Aircall's AI receptionist

Missed calls are missed revenue. Aircall's AI receptionist picks up every call, books the appointment, logs it in your CRM and brings your team in only when a caller needs a person. New accounts get 100 free AI minutes at sign-up, plus 50 every month, with no long-term contract.

Hear how it sounds and set one up in minutes. See the AI receptionist.

Frequently asked questions about virtual receptionist services

What is a virtual receptionist?

A virtual receptionist is a remote service that answers your business calls. It can be a live person working off-site or an AI receptionist that answers, books and routes calls automatically. Both greet callers, take messages and transfer calls to your team.

What is the difference between an AI receptionist and a virtual receptionist?

A virtual receptionist is usually a live person answering remotely and billed by the minute or call. An AI receptionist is software that answers instantly, handles many calls at once, works 24/7 in multiple languages and costs less per call. It hands complex calls to your team.

What is the best virtual receptionist service in the UK?

It depends on what you need. Moneypenny is the premium live choice, alldayPA offers quote-based live answering, Aircall suits teams that want an AI receptionist connected to their phone system and CRM, and Hey Jodie suits small businesses that want flat-rate AI answering.

How much does a virtual receptionist cost per month in the UK?

Live virtual receptionists in this guide start from £15 to £45 a month for entry plans, with per-minute or per-call charges as volume grows. AI receptionists range from flat plans around £49 a month to usage-based pricing from about £0.29 to £0.37 per minute.

Is a virtual receptionist worth it?

Yes, for most small businesses that miss calls. A virtual receptionist costs a fraction of a full-time hire, answers outside office hours and captures leads that would otherwise go to voicemail. AI receptionists make the maths stronger for high call volumes.

What is the best virtual receptionist for solicitors?

Moneypenny and Smith.ai are strong fits for law firms: Moneypenny for a premium UK team, Smith.ai for legal intake tools and Clio integration. For professional services firms that want intake logged in their CRM automatically, an AI receptionist like Aircall's is worth testing.

Can an AI receptionist book appointments?

Yes. Aircall's AI receptionist checks your calendar and schedules, reschedules or cancels appointments during the call. It can also ring ahead to confirm attendance and reschedule no-shows.

Does an AI receptionist work with my existing phone system?

It can. Aircall's AI agents run through your existing Aircall numbers or the phone system you already have, so there's nothing to migrate or rip out.

Here’s what shipped in August and September.

AI Voice Agent campaigns: turn contact lists into conversations at scale

AI Voice Agents can already call leads, follow up with customers, and handle routine conversations. Now, Outbound Campaigns lets you put an entire contact list to work, automatically.

Build a campaign once, upload your contacts, choose your AI Voice Agent, and launch. It works through the list for you, handling conversations end to end, from answering questions and qualifying leads to booking meetings, handling next steps and bringing in your team when needed. Track connection rates and outcomes as your campaign runs, so you can see how each outreach effort performs.

Run follow-ups, reminders, renewals or other repeatable outreach workflows at any volume, without adding more work for your team. Whether you’re reaching 10 contacts or 5,000, you can turn a list into thousands of conversations with a few clicks.

Outbound Campaigns are available on every plan and use your existing AI Voice Agent minutes.

A new way to create AI Voice Agents

You can now create an AI Voice Agent from a ready-made template, built around common use cases like FAQs and lead qualification.

Pick the use case you need and start with an agent that already knows how that conversation should work. The prompts, flow and configuration are already in place, so you can add your business information, review the agent, make it your own, and test it before going live.

That means you can start with a working foundation and spend your time refining the experience, rather than figuring out where to begin.

More templates are coming soon, including a Shopify-integrated Order Management Agent for e-commerce and retail.

Templates are available for inbound voice agents in English-speaking markets today, with more languages and use cases coming soon.

Put Salesforce at the heart of every conversation

Aircall has always been deeply integrated with Salesforce. Now, we’re taking that integration further, so your AI Voice Agents can start conversations with customer context, use your Salesforce content to answer questions and take action in Salesforce as the call unfolds, helping resolve more requests from start to finish.

Use Salesforce Knowledge

Connect your Salesforce Knowledge articles directly to your agent’s knowledge base, so it can answer questions using the same information your team relies on.

Know who’s calling

New AI Actions can look up the caller in Salesforce as the call begins, giving your agent relevant customer context before the conversation gets underway.

Take action in Salesforce

During the call, your agent can read and write directly into Salesforce, looking up and creating cases, opportunities, and more.

Keep your team in the loop

After the conversation finishes, the call is logged in Salesforce and Aircall Workspace, so your reps can seamlessly pick up where the AI Voice Agent left off.

Together, these updates bring Salesforce into the heart of the AI Voice Agent experience, helping your agent understand more, do more and resolve more on every call.

Reach more customers with 13 new languages

AI Voice Agents now support 38 languages, with 13 new additions this month including Hebrew, Catalan, Mandarin, Japanese, Korean, Hindi, Tamil, Malay, Cantonese, Vietnamese, Lithuanian, Latvian and Estonian.

We’ve also made choosing your agent’s voice much easier. The new voice library lets you filter by language and accent, listen to voices before choosing one, and find new accents and dialects including Austrian German, Quebecois French and Irish English.

Filler phrases and background noise can also be switched on or off, giving you more control over how your agent sounds.

Keep customer context moving

Three updates this month make AI Voice Agent conversations more connected, giving your agents the knowledge and context they need to deliver better experiences from start to finish.

First, your AI Agents can now tap into the knowledge your team already uses. Connect Zendesk, Salesforce, HubSpot or Document360 to your AI Agent’s knowledge base, and it can use your existing business documentation, product information and policies to answer customers accurately. When that content changes, your AI Agent picks up the latest information automatically, keeping answers current without manual migration or upkeep. And because knowledge is shared across voice and messaging, you can keep your AI Agents aligned across every customer channel.

Next, your agent can get to know the caller before the conversation even starts. Contact lookups in HubSpot, Salesforce and Zendesk now happen in the background during the greeting, so the agent can start with relevant customer details already in hand and get straight to helping.

Then, that context can follow the conversation wherever it goes. With Agent Memory, when one AI Voice Agent transfers a call to another, the receiving agent now gets the caller’s details, the reason for the transfer and everything shared so far. The next agent can pick up with the full picture and keep the conversation moving toward a resolution. In our pilot, that meant 69% less handling time for the second agent.

Together, these updates make every AI Voice Agent interaction feel like part of one continuous conversation, from the first hello to the final resolution.

That’s September

We’ve had a busy month!  With new ways to scale outbound conversations, get agents live faster, reach more customers, put Salesforce context to work, and keep multi-agent conversations moving, we’ve rounded out the summer with a real flourish!

And stay tuned; we can’t wait to show you what the autumn will bring. See you in next month’s roundup.

Tl;dr

Three capabilities are open through the Aircall Developer Portal: AI Assist for conversation intelligence, AI Voice Agents, and AI Messaging Agents across SMS and WhatsApp. You can read structured intelligence out of every conversation, trigger agents from your own workflows, and expose your system's actions to those agents over MCP. Connect a tool once, and every product in the Aircall AI suite can use it. Building on Aircall used to mean reading reference docs and reverse-engineering the build process, and most teams stalled before they shipped. The rebuilt portal documents the full path from design to publish for the first time.

AI Assist: every conversation becomes structured data

AI Assist turns every call and message into machine-readable intelligence: transcripts, summaries, sentiment, action items, QA results, and playbook outcomes. It’s available through REST APIs and webhooks, so you can pull or receive it wherever you need it.

The pattern is straightforward:

• Subscribe to a webhook, and the moment a conversation ends you receive a structured payload.

  • No polling, no scraping recordings, no manual tagging. 

• From there, it’s your logic:

  • Write the summary back to a CRM record

  • Route a negative-sentiment call to a managerPopulate a coaching dashboard

  • Feed action items into a ticketing queue. 

• AI Assist also pulls context from your connected tools to surface conversation history to your reps, then logs the conversation back into your CRM, data warehouse, or QA stack.

This is the layer that makes products smarter with every conversation. CRMs, helpdesks, QA tools, and coaching platforms can all consume the same intelligence and improve the more their users talk to customers.

Agents that act, not just answer

AI Assist tells you what happened. AI Voice and Messaging Agents do something about it while it’s still happening.

AI Voice Agents run calls end to end, follow the workflows you define, hold a natural conversation, and connect to external systems to take action. Inbound AI Voice Agent calls can be handled entirely inside Aircall, and outbound AI calls can be triggered from any product you build. AI Messaging Agents apply the same agent model to messaging across SMS and WhatsApp, on the same ecosystem and the same integrations, so there are no silos between channels.

The point of an agent has become resolution, not deflection. A conversation is only resolved when the agent can look up an order, qualify a lead, open a ticket, or book a meeting in real time. That requires your systems to be reachable mid-conversation, which is where MCP and AI Actions come in.

MCP and AI Actions: one light build, multiple possibilities

AI Actions is how Aircall's agents connect to and act within external tools. MCP, the Model Context Protocol, is how you make your system's capabilities available to those agents. Instead of building bespoke integration logic for each workflow, you describe your tools and let the agent decide when to call them.

The build is intentionally light:

• Stand up an MCP server that exposes your actions as tools.

• Write clear schemas and tool descriptions. The agent reasons over these to decide what to call and when.

• Register your MCP URL through the Technology Partner Program.

That is the integration. Aircall's agents discover your tools automatically and invoke them during conversations. One MCP server works across AI Agents and AI Assist automatically, not just in one product. Because MCP is becoming the standard for AI agent interoperability, the same server also works with every other MCP-speaking platform your customers use. It takes one investment and one build for a broad payoff.

MCP integrations are declarative, which lowers maintenance. When a workflow changes, you update your schema and descriptions rather than rewriting integration code. There is no brittle glue to keep in sync every time the conversation logic shifts.

The most integrated, end-to-end AI platform

When an Aircall agent reads and acts across several systems at once, it does a job rather than just answering a question. A retail Order Concierge can pull order details from Shopify, process a refund through Stripe, and log the outcome in the CRM inside one call. A Lead Qualifier can answer inbound, enrich HubSpot, and book a meeting in Calendly before the caller hangs up. The more systems you connect in a single workflow, the more the agent can resolve without a handoff.

A documented path from idea to marketplace

The rebuilt Developer Portal documents the entire path, from design to build to test to publish, across three tracks: AI integrations covering AI Assist, AI Voice and Messaging Agents, and MCP; classic REST integrations using the existing REST APIs and webhooks, now documented end to end; and embedded experiences that surface your product inside the Aircall workspace. Whichever track you pick, you get a defined route to a live, listed integration, plus marketplace visibility, co-marketing, and tier benefits across Aircall's 400+ integration base and 23,000 customers. Partners integrating now are visible to Aircall's customers from day one, while the ecosystem is still small enough to stand out in.

This isn’t a beta running on synthetic traffic. Our own Product Support team deployed AI Voice Agents on inbound calls before the platform opened to partners, and the agent exceeded its deflection target within the first week, with customer experience scores rising measurably over the weeks that followed. The capabilities you build on are the same ones already resolving live conversations.

Where to start

If you want intelligence out of conversations, start with AI Assist. Subscribe to a webhook and you’ll have structured data flowing in minutes. If you want your product to act inside conversations, stand up an MCP server and register the URL. If you need classic call and contact sync, the REST APIs are documented end-to-end for the first time.

Voice and messaging are becoming programmable in a way they never were before. The conversation is no longer a black box that ends in a recording. It’s a stream of structured events and a surface your code can act on in real time.

Head to the Aircall Developer Portal to read the docs, explore the AI Assist and MCP reference examples, and ship your first AI-ready integration.

What you can build with Aircall

Our AI ecosystem gives developers three things to build with:

• AI Assist turns every call and message into structured intelligence: transcripts, summaries, sentiment, action items, and QA results, delivered over REST APIs and webhooks.

• AI Voice Agents run voice calls end-to-end. 

• AI Messaging Agents do the same across SMS and WhatsApp. 

The connective tissue is the MCP, which lets those agents discover and call your product's capabilities during a live conversation.

The important shift for developers is what an integration now does. Historically, you logged a call or synced a contact after the fact, for a human to read. Now your tool can be something an AI agent reaches for mid-conversation, to look up an order, update a record, or complete a task, so the conversation gets resolved rather than passed along. Connect your product once, and every Aircall AI product can use it.

For example, a retail team could build an Order Concierge; an AI Voice Agent that pulls order details from your commerce platform, processes a refund through a payments provider, and logs the outcome in the CRM, all inside one call. A sales team could build a Lead Qualifier that answers inbound, enriches the record in their CRM, and books a meeting in a scheduling tool before the caller hangs up. Each of those is several connected tools working together in a single conversation, and each tool is something a developer exposed to the agent.

The mental model: connect once, used everywhere

Before writing code, it helps to hold one idea in mind. You aren’t building three separate integrations for three products. You expose your capabilities once, and they become available across AI Voice and Messaging Agents, and AI Assist automatically.

There are two ways in, and most substantial integrations use both. MCP is how you expose actions an agent can take: you describe your tools declaratively with clear schemas and descriptions, stand up an MCP server, and share its URL. Aircall's agents reason over those descriptions and call the right tool at the right moment. REST APIs and webhooks are how you handle classic, event-based work: react to call and contact events in real time, log activity, and sync data into your systems of record.

A quick principle from the planning guide: build event-based integrations. Set up webhooks after user authorisation so you react to Aircall events as they happen, rather than polling. And use OAuth for public apps, which gives you secure, scalable authentication and clean installation across many Aircall accounts.

Step 1: Plan your integration

Good integrations start with a plan, not a pull request. Aircall's plan your integration guide lays out four steps.

1. First, explore what Aircall's API and AI capabilities can do, and pick the ones relevant to your product and users. The feature set ranges from quick, high-impact wins to deeper capabilities. Call logging, insight cards, tag sync, logging AI Voice Agents calls, and triggering AI outbound calls are all low effort and popular. Conversation intelligence such as transcriptions, summaries, playbooks, evaluations, and sentiment, along with SMS and MMS messaging, contact sync, and call management, sits in the higher-value tier.

2. Second, talk to customers. Identify which Aircall workflows people handle manually today, where the gaps are, and where an agent that can act would save the most time.

3. Then, write an integration plan: the use cases you want to support, the features you will use, and how it all fits together. 

4. Lastly, share it with us. Once your partner application is approved, submit the plan as a shared document such as a Google Doc, Notion page, or PDF. Expect review feedback within one to five business days, after which you receive developer and OAuth credentials.

Step 2: Build and launch

With credentials in hand, you’ll move into setup and development. The build and launch guide breaks this into clear phases.

Setup comes first. You get a developer account, valid as a trial for four months (and extendable), which becomes permanent once your app reaches its first customers. You also receive two OAuth applications, one for development and one for production, with support for up to five redirect URIs each. One practical note that saves time: localhost URIs aren’t supported, so use a tunneling service such as ngrok while you develop.

A few OAuth specifics are worth knowing up front. An admin install grants a single account-level access token for the whole account. You match Aircall users to your own by email using the List all Users endpoint. And admins choose which phone numbers your integration can access during the OAuth flow.

Then you build. It helps to know the two shapes an app can take:

• A public app is designed for many Aircall customers, uses OAuth, and goes through review. 

• A private app is a single-account integration that can use API keys and needs no review. 

For the AI ecosystem, the MCP work is deliberately light: expose your actions as tools, keep schemas and descriptions clean, and let the agents do the rest. There’s no brittle glue to maintain, because the integration is declarative. When a workflow changes, you update the schema, not a pile of code.

Aircall asks that integrations reach completion within about six months of receiving credentials, with a short progress update at least once every couple of months. That cadence keeps momentum and keeps your credentials active.

Step 3: Publish your app

Publishing is how your integration becomes discoverable to Aircall's customers. Our how to publish an app guide sets out the requirements and the assets you will need.

To be eligible to publish a public app, you need a finished, live product with a company website, and an existing customer base using it. Meeting the bar doesn’t guarantee approval, since Aircall keeps a discretionary review, but it’s the baseline. You apply through the technology partner form, and applications are reviewed within about five business days.

When you’re ready to list, prepare four assets: a 5-10 minute demo that walks through the integration; at least two help centre articles covering installation and your main features; a logo in SVG format using Aircall's template; and 3-5 screenshots that show the value of the integration. You submit these through the official form, then demo and asset review takes one to five business days, and publication follows in three to five.

There’s one more milestone worth planning for: new apps live in the Aircall Dashboard until they reach 10 customers. Once you cross that mark, you email Aircall to get listed in the public marketplace, and your developer account becomes permanent.

Why it’s worth building here

The economics favour building early. One MCP server works across every Aircall AI product at once, so you aren’t maintaining three integrations. Because MCP is becoming the interoperability standard, the same server you build for Aircall works with any other MCP-speaking platform, so your effort isn’t locked to one vendor. Your tool stops being a passive integration and becomes infrastructure that other companies' AI agents depend on mid-conversation.

Publishing adds the distribution. A marketplace listing brings discoverability and the credibility of a reviewed, maintained integration, plus access to co-marketing, technical guidance, and early API features. Partners who integrate now are visible to Aircall's customers from day one, while the ecosystem is still small enough to stand out in!

Start building

Everything begins in the Developer Portal, and the fastest path is to plan while your partner application is under review, so you can move quickly once approved.

• Plan your integration

• Build and launch

• How to publish an app

Apply as a technology partner, map your first use case to the capabilities above, and ship an integration that doesn’t just watch conversations, but helps resolve them.

The next advantage is a more connected AI

The next competitive advantage in sales and customer service isn't a better AI model, it's a more connected one. Most teams think they have an AI problem, but they don’t; they have a connection problem. Their AI stops at the edge of the conversation because it has nowhere to go.

A decade ago, Aircall built an ecosystem to connect customer conversations to the tools a team runs on: CRMs, helpdesks, and e-commerce platforms. Today, that ecosystem links 23,000 businesses through more than 250 integrations, built on one belief: connected tools outperform isolated ones. That ecosystem is now open to AI.

The integration era was built for people

Aircall's 250+ integrations were designed around a single assumption: that a person sits at the centre of every conversation, reading the data and deciding the next step. Those integrations log calls, sync contacts, and update records after every conversation. They work, and they run in production across tens of thousands of businesses today.

AI changes the requirement. AI agents now handle a growing share of customer conversations, and every conversation produces something new: structured intelligence such as summaries, sentiment, and action items, generated automatically and at scale. That intelligence is valuable only if it reaches the rest of your stack. And an AI agent needs more than intelligence. It needs to act; look up an order, update a record, or book a meeting mid-conversation, without a handoff. That calls for a new kind of integration, one built for AI rather than for human teams alone.

Aircall: The most integrated AI platform

Aircall is opening its AI ecosystem to partners and developers, so its AI agents become the most connected customer communication agents in the market. Until now, the intelligence Aircall generated from every call flowed only to a handful of selected partners, and its agents could act only inside a fixed set of tools. Three releases change that.

AI Actions, live today, lets Aircall's AI agents connect to and act within external tools over MCP, including Shopify, HubSpot, and Zendesk. The Developer Portal is the public, documented entry point to the ecosystem, engineered so that building on Aircall is faster and easier. Partner MCP Registration lets external partners register their own MCP servers, so their tools surface as available AI Actions inside Aircall.

Three products are now openly buildable:

• AI Assist turns every call and message into structured intelligence, available through REST APIs and webhooks.

• AI Voice Agents run calls end to end, follow workflows, and take actions in connected systems.

• AI Messaging Agents bring the same capabilities to SMS and WhatsApp.

The connective layer is MCP, the emerging standard that lets AI agents discover and use external tools. The mechanics are deliberately simple: share an MCP URL, keep your schemas and descriptions clean, and Aircall's agents discover your tools automatically. 

Connect a system once, and every Aircall AI product can use it, that’s what makes Aircall the most integrated AI platform.

What a connected agent can do

Generic AI responds, but truly connected AI resolves. So, when an Aircall agent reads and acts across several systems at once, it stops being a responder and starts doing a job.

A retail business can configure an Order Concierge that pulls order details from Shopify, processes a refund through Stripe, and logs the outcome in your CRM, all inside a single call. A sales team can run a Lead Qualifier that answers inbound, qualifies the lead, enriches the record in HubSpot, and books a meeting in Calendly before the caller hangs up. The role is yours to define, and the more systems you connect, the more complete the job your agent can do.

Built on infrastructure that already works

This isn’t a rebuild; it’s a new AI layer on the same ecosystem Aircall has operated for ten years. The tools your team uses today, such as HubSpot, Shopify, and Zendesk, become accessible to your AI agents in a fundamentally different way; not just receiving data after a call, but being acted on during one. Your stack becomes AI-ready at every opportunity.

Open by default, and compounding

Attio, HelpScout, Front, HubSpot, and Calendly are the first partners to go live in the ecosystem. Attio, the AI-native CRM, is the first MCP-native CRM connected to AI Voice Agents at launch. Later this summer, any partner will be able to self-register their MCP and join them.

But the beauty is, that design compounds. Every partner that connects adds a capability every Aircall customer's agent can use, automatically, without the customer doing anything. Connect once, and reach every Aircall customer from day one. For customers, the shift is just as direct: technical teams can sync AI Assist outputs into any internal system, trigger AI Voice Agents and AI Messaging Agents from their own workflows, and build their own integrations to CRMs, helpdesks, and data warehouses, without filing a support ticket.

Why now?

The market is deciding which communication and CX tools will emerge as genuinely AI-native platforms rather than tools with AI bolted on. Opening the ecosystem is how we answer that question; not by adding more features, but by becoming the infrastructure that AI voice and messaging agents are built on and connected to.

For ten years, Aircall has connected human agents to the tools they need. The same ecosystem now does it for AI agents, and it’s open to everyone who wants to build.

Start building on the Aircall Developer Portal with AI Assist, AI Voice Agents, and AI Messaging Agents, or register your MCP to connect your tools to Aircall's AI agents.

It's here.

Outbound Campaigns is now live, giving sales, support, and success teams a single, structured way to run coordinated outreach directly from Aircall. And every campaign you build can be run by your reps, by your AI Voice Agent, with the option to hand off to a person mid-call, all from the same system.

From spreadsheets to a shared queue

Most outbound today is still stitched together by hand: a spreadsheet for the list, a Slack thread to divide it up, a shared doc to track who called whom. It works, until it doesn't. Contacts get double-dialled. Outcomes live in someone's notes instead of a dashboard. A manager can't tell what's working until the campaign is already over.

Outbound Campaigns replaces that with one workflow. A manager builds a campaign once: uploads a contact list, sets calling rules, decides who runs it, and launches. From there, contacts flow automatically into a shared, conflict-free queue, no duplicate calls, no manual coordination between reps, no switching between tools. Supervisors can watch progress in real time and adjust the campaign, mid-run, without stopping it.

That's the foundation: Campaign Orchestration. It's built and priced for teams that have outgrown ad hoc dialling, and it's now live for anyone on the Professional plan, at no extra cost.

One engine, two ways to run a campaign

Here's what's actually new. Until now, orchestration meant orchestrating reps. Now, when you build a campaign, you decide who works it: your team, your AI Voice Agent, or a mix across your outbound program.

Point a campaign at your AI Voice Agent and it handles the call end-to-end: lead follow-up while intent is still fresh, payment and billing reminders, appointment confirmations, renewal check-ins, service follow-ups tied to something a customer just did. Upload a list, set the rules, and launch, the same way you'd launch a rep-led campaign.

A single campaign is either AI-led or rep-led, not both at once, but both run through the same Campaign Orchestration and show up in the same Campaign Analytics. You can test, optimise, and compare how AI-led and human-led outreach perform instead of guessing which one to trust. And when an AI Voice Agent call needs a person, it hands off to your team with full context attached, so nothing gets repeated and nothing gets lost.

Neither mode drops a call to get there. A rep-led campaign only dials when a rep is on the line and ready, so a contact never answers to silence or a dead connection. An AI-led campaign works the same way in principle: the AI agent takes every call it connects, instead of placing more than it can handle and hoping someone's free. No abandoned calls, by design, whoever or whatever is on your end of the line. That's not a small detail: it's what keeps your numbers off spam lists, and it's the difference between a customer who feels handled and one who's left wondering why your company called and hung up on them.

If Outbound Campaigns turns your team's calling activity into a managed, measurable revenue engine, that's now just as true for the calls your team doesn't have the headcount to make. Reminders, renewals, and time-sensitive follow-ups stop competing with your reps' calendars, because they're not on it anymore.

What's included at launch

• Campaign Orchestration: build and schedule multi-step outreach sequences from the Aircall dashboard, and assign each one to reps or to your AI Voice Agent.

• Campaign Analytics: track call attempts, connection rates, and campaign-level outcomes in one view, including a live, real-time look at campaigns while they're still running.

• Local Presence: automatically matches your outbound number to the recipient's area code, so calls show up as local instead of unknown.

This is the same trust layer we described in April: a local-looking number by default, and number health that's monitored rather than left to chance. Outbound only works if the call gets answered, and none of that happens by accident.

Who this is built for

Outbound Campaigns was designed around teams that already feel the cost of fragmented outbound:

• SDR and BDR teams running coordinated cold and warm outreach, where a manager owns the list and reps focus entirely on the calls.

• Sales follow-up teams working post-webinar, post-demo, and pipeline re-engagement motions.

• Customer success and renewals teams running proactive, structured re-engagement with the same outcome tracking as a sales campaign.

• Support and operations teams running outbound callbacks, reminders, confirmations, and service follow-ups tied to a customer action.

If your team is already running outbound through Power Dialer, Salesloft, or Outreach and feels the limits of a per-rep list with no shared view, this is the upgrade path. If your team is already using AI Voice Agents inbound, this is what happens when that same agent starts making the first call, not just answering one.

For all of these except that first one, cold outreach still calls for a person, this is also a decision about who runs the campaign. Post-webinar follow-up, renewal check-ins, billing reminders, service confirmations: any of it can now run as an AI-led campaign instead of a rep-led one, without waiting on headcount to free up.

Orchestration was never about doing more. It's about doing it on purpose.

The conviction hasn't changed since April: outbound isn't a volume game anymore, it's a relevance test, and reputation is the thing standing between a dial and a real conversation. What's changed is that the system built to act on that conviction is no longer something we're describing. It's something you can turn on, and now it doesn't care whether the call is made by a person or an AI Voice Agent, only whether it's the right call to make.

Outbound Campaigns, human-led or AI-led, is live now on the Professional plan.

The problem your customers are facing

Every online shopper eventually asks the same thing: where is my order? It sounds small, but at scale it swamps retail teams and defines whether a customer buys again.

One question dominates the queue. Order status questions drive between 25 and 35% of contact centre interactions, spiking to 50% during peak seasons. Each one asks the same thing, yet each one still takes an agent time to verify the caller, find the order, and read back the details.

Customers expect an answer now, at any hour. 43% of consumers expect 24/7 service from online retailers, and 80% have switched brands after a poor experience. A voicemail or a next-day callback isn’t an answer to "where is my package?", but a reason to shop elsewhere.

Manual lookups burn out the team. Answering the same question hundreds of times a day is draining work. Average contact centre turnover sits at 38%, and a new hire takes six months or more to reach full productivity. A five-minute manual lookup for a 30-second answer is not a good use of anyone's time.

Aircall AI Voice Agents: Your new AI order agent

AI Voice Agents are the buffer between the hordes of simple customer calls and the human agents whose time you're trying to protect. They pick up instantly, find the order, and give the customer a real, accurate answer without a human ever needing to touch the call.

Automatic caller verification. The agent recognises the caller by their phone number, so there is no security interrogation before it can help. The customer is already known before they start the conversation.

Real-time order lookup. Connected to Shopify, the AI agent retrieves live order details the moment the customer asks: status, items, shipping method, and tracking. It reads them back in plain language, so a five-minute interaction becomes a 30-second automated resolution.

Post-purchase journey, handled. Returns, exchanges, and product or stock questions end-to-end, so "where is my order?" and everything around it never reaches your team unless it’s particularly complex.

Payment reminders that land before the account goes overdue. AI Voice Agents call ahead of a due date, not after it's missed, so a payment issue gets caught and cleared without ever turning into a collections call.

Proactive customer contact. Outbound AI Voice Agents confirm orders, flag delivery issues proactively, and re-engage shoppers who left something in the cart, turning a support cost into a retention channel.

Works on every channel. The same AI order agent resolves order questions over voice, SMS, and WhatsApp, so customers get help wherever they reach out.

Capabilities

Our AI Voice Agents work out-of-the-box. Take a look at what you can do with your new AI order agent, immediately:

• Answer every order call instantly, 24/7, with no hold time

• Verify the caller automatically using their phone number

• Look up live order status, items, shipping, and tracking from Shopify

• Handle returns, exchanges, and product or stock questions end to end

• Send proactive order confirmations and delivery updates

• Re-engage shoppers who left items in their cart

• Resolve order questions across voice, SMS, and WhatsApp

• Escalate complex cases to a teammate with the full conversation attached

• Connect to Shopify, Magento, and 250+ integrations

Plus, it works with the tools you already sell on. Because the AI order agent runs on your existing Aircall number, connects straight to your store, and integrates with your CRM, there’s no separate bot to build or maintain.

Results in the numbers

Order status is the highest-volume, most repetitive request in retail, which makes it the fastest win for automation.

• Food Cycle Science cut time to first human response by 78%, from an average of 29 hours to 12.

• TripCity now handles 54% of its calls with AI Voice Agents, with 87% resolved without a live rep.

• 43% of consumers expect 24/7 service, which the agent delivers with zero added headcount.

When the AI order agent absorbs the "where is my order" wave, your team gets its time back for the conversations that actually need a person, and your customers get an instant answer at any hour.

Get started in your existing store

Point it at your store and go live. AI Voice Agents connect to Shopify or Magento, read live order data, and answer on your existing Aircall number, with no new system to run.

Every answer stays accurate. Because it pulls order details in real time rather than from a static script, customers always hear the current status, and complex cases pass to your team with the full context attached.

If "where is my order" is filling your queue, see how AI Voice Agents can answer it for you.

Built for support teams already running on Aircall, connected to whichever help desk they use.

The problem support teams face today

Queues grow faster than headcount. Ticket volume climbs with every new customer, but hiring never keeps pace, so tickets sit longer and customers wait longer for a reply.

Repetitive questions crowd out real problems. Employees switch between apps up to 1,200 times a day just to find the context behind one ticket, time that never goes toward the harder cases that actually need it.

After-hours coverage is expensive or absent. A customer with an urgent issue at 9pm either waits until morning, or gets routed to an on-call rep, and neither option feels like support.

From first contact to full resolution

Routine FAQ requests get resolved on the spot. AI Voice Agents answer from your knowledge base in real time; order status, account details, policy questions, so common issues never wait for a rep.

Support reaches out before the customer has to call in. AI Voice Agents place outbound calls too: a service update when something breaks, a delay notice before a customer notices, or a follow-up when a ticket has sat open too long.

Complex cases reach a rep with full context. When a request needs judgment, an AI Voice Agent escalates immediately and hands over the full transcript and case history, so the customer never repeats themselves.

The same resolution logic works over voice and messaging. AI Voice Agents apply the same knowledge base and escalation rules whether the customer calls, texts, or messages on WhatsApp.

Capabilities

Take a look at is what sets AI Voice Agents apart as a support tool, not just an AI bolt-on:

• Ticket creation and escalation: creates and updates tickets automatically, then escalates with full context

• Knowledge Base resolution: answers policy and how-it-works questions straight from your

• knowledge base

• Proactive outreach: places outbound calls for service updates, delay notices, and unresolved ticket follow-ups

• Full-context escalation: hands off complex cases to a rep with the full transcript and case history attached

• In-call text: sends a text during the call for a tracking link, portal link, or confirmation, no follow-up email required

Results in the numbers

Aircall's AI Voice Agents have already handled 1.7 million calls this year, measured on response time, resolution, and CSAT.

• Food Cycle Science cut time to first human response by 78%, from 29 hours to 12

• SJWD Water District resolved calls 65% faster, cutting average handling time from 150 seconds to 55

• TripCity absorbed 30% of total call volume within two and a half months, with 87% resolved without escalation

• Unbiased lifted its service level to about 93%, without adding headcount

• Ausmed held a 20% voluntary deflection rate steady for six months, covering staff leave without temporary hires

For example, a support team handling 5,000 calls a month with account lookups could save close to 190 hours of agent time a month and see a return of roughly 2.7 times its cost, based on Aircall's internal ROI modelling for this use case. This is an illustrative example, not a customer result, and actual returns vary by call volume, knowledge base coverage, and case complexity.

Set up your AI support agent in 3 steps

1. Pick the job. Choose the Support Resolution agent template and Aircall pre-fills it with sample account and order questions. Every plan already includes AI Voice Agents minutes, so there’s a working agent to review from day one, not a blank project.

2. Confirm what it answers and when it escalates. Connect your knowledge base and your external tools so the agent can pull real answers, and review the escalation triggers, laid out visually, including where it hands off to a rep.

3. Test it, then go live. Make a test call, connect your support number, and activate. Most teams go from template to a live, resolving agent in minutes.

Proactive outreach is the natural next step. Once your inbound agent is live and resolving, adding outbound service updates and ticket follow-ups turns it into a full support motion, the same way it went live the first time.

Get started with AI Agents: one system, multiple capabilities

Most AI support tools create a separate system to manage. Aircall's AI Voice Agents run on the same phone system and number your team already uses, not a bot bolted on from another vendor, so a resolved or escalated case never means switching tools to find it.

When AI Voice Agents escalate, reps see the full transcripts and the tickets already updated, so nobody has to ask the customer to start over. Nothing about the case gets lost in the handoff, whether it moves from resolution to escalation or from an inbound call to an outbound follow-up.

AI Voice Agents connect to your external system such as HubSpot or Help Scout, with more help desk integrations on the roadmap, so resolved and escalated cases land where your team already works.

If growing queues or after-hours gaps are costing you resolution time, let’s see how AI Voice Agents can handle it.

Aircall's AI Voice Agents don't just pass along information, they take action: qualifying, booking, and updating your CRM, so your reps focus on the conversations that need them.

Every lead you're too slow for is a lead you lose

Hot leads cool off fast. A prospect calls or fills out a form ready to talk. If nobody picks up in the first few minutes, that readiness is gone, and a competitor answers next.

Reps spend more time on process than pipeline. Sales reps spend up to 70% of their time on non-selling work: logging calls, updating records, qualifying leads that were never a fit. Gartner found that 41% of sellers already say AI has freed up exactly that kind of time. The question is what replaces it.

Qualification depends on who picks up the phone. Ask ten reps the same question and you'll get ten different answers logged, or none at all. The next rep inherits a lead with no reliable data behind it.

None of this is a headcount problem. It's a response-time problem.

AI Voice Agents: No lead waits. No question gets skipped

No lead is lost to inconsistent qualification. Every call asks the same questions, in the same order, whether it's the first call of the day or the five-hundredth, and logs the same clean data to the contact record, so pipeline quality never depends on which rep picked up or how their day is going.

Qualified leads turn into booked meetings, not follow-up tasks. The AI Voice Agent checks your rep's calendar and books the meeting before the call ends, so qualification converts to pipeline immediately, not a to-do for later.

No lead waits on rep availability. The moment a lead shows intent, whether from a form fill or a CRM trigger, the AI Voice Agent calls and hands a warm, qualified lead to a rep with full context attached.

Reps only spend time where it counts. Complex conversations still escalate immediately, with the full transcript and context attached, so reps step in only when a lead actually needs judgment.

Automate targeted outreach campaigns. Coming soon, AI Voice Agents will call your lead list directly inside Aircall — re-engaging warm leads, confirming meetings, and keeping every call compliant with built-in consent tracking and calling-hours enforcement.

What it actually does

Take a look at what sets AI Voice Agents apart as a sales tool, not just a phone line:

• Inbound lead qualification: qualifies every lead with the same questions, every time, and logs answers straight to the contact record

• Autonomous booking: checks rep availability, books the meeting into your CRM, and sends a confirmation by text before the call ends

• Follow-up and pipeline management: follows up on new leads the moment they show intent, and leaves a personalised voicemail if nobody picks up

• Objection handling and multilingual support: answers common objections from your knowledge base and speaks 23 languages, so no lead is lost to routine pushback or a language barrier

• AI Outbound Campaigns (coming soon): calls your lead list directly inside Aircall, with built-in consent tracking and calling-hours enforcement, re-engaging warm leads and confirming meetings before they go cold

Every call sharpens your pipeline data. Structured qualification answers and call outcomes write back to your CRM automatically, so it stays clean and your sales dashboards reflect what actually happened on the call, not what a rep remembered to type up afterward.

Set up your AI SDR in 3 steps

1. Pick the job. Choose the Lead Qualification agent template and Aircall pre-fills it with sample qualifying questions and contact fields. Every plan already includes AI Voice Agents minutes, so there's a working agent to review from day one, not a blank project.

2. Confirm what it asks and how it talks. Review the qualification questions, connect your CRM so the agent can check calendars and book meetings, and check the conversation flow, laid out visually, including where it escalates to a rep.

3. Test it, then go live. Make a test call, connect your sales number, and activate. Most teams go from template to a live qualifying agent in minutes.

Automatic follow-up is the natural next step. Once your inbound agent is live and qualifying, adding follow-up on new leads turns it into a full pipeline motion, the same way it went live the first time.

Get started today, with a single system

Most AI SDR tools speed up work your team already does. Aircall's AI Voice Agents do something different: they pick up the leads your team can't get to in time, the after-hours form fill, the inbound spike your reps can't answer fast enough, the lead that goes cold in the minutes before someone calls back. That's not automating an existing workflow, it's pipeline that would have been lost.

It works because it isn't a separate system to manage. Aircall is the phone system trusted by more than 23,000 businesses, and AI Voice Agents run on that same phone system, number, and CRM connection your reps already use, not a bot bolted on from another vendor. When a lead moves from qualification to booking, or from an inbound call to a follow-up, full context passes automatically, so nothing gets re-asked and no rep opens a conversation cold.

AI Voice Agents connect to HubSpot today, with Salesforce and more integrations on the roadmap, so qualified leads and booked meetings land exactly where your reps already work.

If slow follow-up or inconsistent qualification is costing you pipeline, let's see how AI Voice Agents can handle it!

A missed call is never free

Calls don’t wait for business hours. Customers call after your team logs off, during lunch, and in the middle of your busiest week of the year. A front desk or support queue can only handle so many calls at a time, so every extra call becomes a voicemail, a long hold, or an abandoned call.

Traditional phone trees make it worse. Callers press through menus that rarely match why they’re calling, then wait for a teammate who may already be on another line. The result is missed leads, slower support, and a team that spends its day repeating the same answers instead of solving harder problems.

The cost adds up fast. A missed sales call is a lead that goes to a competitor. A missed support call is a customer who calls back angrier the second time. And every minute your team spends on a routine question is a minute they’re not spending on the call that actually needs them.

Aircall AI Voice Agents: Your full-time AI receptionist

Every caller gets routed correctly, not just answered. An Aircall AI Voice Agent greets the caller in plain language, understands what they need, and either resolves it or routes it to the right teammate with full context. No phone tree, keypad, or hold music.

After-hours never means unanswered In healthcare alone, 42% of appointments get booked outside standard business hours. The AI Voice Agent answers at 11pm on a Sunday the same way it does at 11am on a Tuesday, so coverage doesn't stop when your team logs off.

Booking doesn't wait for a callback. The AI Voice Agent checks your calendar and books, reschedules, or cancels the appointment on the call itself, no hold, no callback, no double-booking.

Reminders happen before the no-show, not after. The AI Voice Agent calls ahead of a scheduled appointment to confirm attendance, and reschedules on the spot if someone can't make it. Phone reminders alone can cut missed appointments by up to 39%.

Complex calls still reach a person. The AI Voice Agent picks up on urgency or frustration in a caller's voice and hands off to a teammate the moment the call needs judgment, not just information.

Capabilities

Here's what sets Aircall's AI Voice Agents apart from voicemail, phone trees, and stand-alone answering services:

• Answers every call, including nights, weekends, and peak periods

• Understands natural, open-ended requests, no keypad, no menu

• Books, reschedules, and cancels appointments directly into your calendar, and calls ahead to confirm and reduce no-shows

• Resolves routine questions and hands off complex calls to a teammate who already has the context

• Speaks 23 languages, so a caller gets the same answer from a friendly voice, in their own language, with no wait

Every call sharpens your AI Voice Agent. The Agent Performance dashboard shows exactly which questions it should be handling better, so you fix the gap before it costs you the next caller.

Results in numbers

Call routing and reception is already Aircall's largest AI Voice Agent use case by volume: 346,000 calls handled across hundreds of companies.

• SJWD Water District resolves about 90% of after-hours calls with an AI Voice Agent, and cut average resolution time from 150 seconds to 55 seconds

• TripCity now handles 54% of its calls with their AI Voice Agent, resolving 87% of them without escalating to a live rep

• Unbiased grew service level by 23% without adding headcount

• Phone reminders alone can cut missed appointments by up to 39% (ZocDoc), an industry benchmark for what automated confirmation calls can recover

An AI Voice Agent works best on the calls that repeat: intake, triage, routing, and routine questions. Calls that need judgment or empathy still go to your team, and nothing about the call gets lost in the handoff. And the best part is, the setup is simple, so getting there takes less time than you would think!

Set up your AI receptionist in 3 steps

1. Pick the job. Head to your dashboard, and choose the Receptionist Agent template. Aircall pre-fills it with your business name, hours, and content. Every plan already includes AI Voice Agents minutes, so there’s a working agent to review from day one, not a blank project.

2. Confirm what it knows and how it talks. Review the profile, voice, and tone, confirm the knowledge sources it should answer from, and check the conversation flow, laid out visually, including where it branches.

3. Test it, then go live. Make a test call, connect your number, and activate. Most teams go from template to live agent in minutes.

Call routing is the natural next step. Once your receptionist is live and answering, adding intent-based routing turns it into a full front desk, the same way it went live the first time.

How long does setup actually take? Most teams go from template to live agent in minutes. Plus, call routing and appointment scheduling are both easy to add once the receptionist is live.

Get started today, with a single system

In most cases, AI answering services run separately from your phone. Aircall's AI Voice Agents are built into the same phone system, number, and interface your team already runs calls through, not a bot bolted on from another vendor.

The handoff carries the whole conversation. When the AI hands a call to a teammate, they see the full call history in the same tool, so the caller never has to repeat themselves.

It It fits into the tools you already use. AI Voice Agents connect to 250+ native integrations, including HubSpot, Salesforce, and Zendesk, so call outcomes land where your team already looks for them.

If missed calls, long queues, or a stretched front desk sound familiar, let's see how AI Voice Agents can handle your call volume:

As expectations for speed, personalisation, and omnichannel support rise, businesses are turning to customer communication management (CCM) tools to scale their service without compromising quality. 

From speeding up time to resolution to surfacing key insights, CCM platforms help sales and customer support teams turn every interaction into an opportunity to build trust and loyalty.

In this article, we’ll break down seven essential features to look for in a CCM tool and explain how the right platform can transform your customer engagement strategy.

Do I need a standalone CCM tool?

Without dedicated, connected CCM software, you may struggle with fragmented customer data spread across email, voice, chat platforms, and CRM systems. Support agents waste time switching between tabs, while customers have to repeat themselves every time they contact you. This leads to frustrating experiences, slower response times, and inflated support costs. 

An integrated CCM tech stack made up of the right tools solves this by centralising customer-facing interactions and giving teams full visibility into each customer’s journey. Agents can access complete conversation histories, automate repetitive tasks, and personalise communication at scale. 

Let’s say you’re using a disconnected business phone system, live chat tool, and CRM. Your team might know who the customer is, but not what they’ve already discussed on other channels. Here, a CCM tool can eliminate the disconnect and provide a smoother customer experience.

7 top features to look for in CCM tools

61% of SMBs prioritise customer experience over aggressive growth, and to put customer relationship management first, you need the right tools.

But not all CCM platforms deliver the same value to your business; the best solutions combine multiple communication channels to provide a centralised platform for highly personalised communications. Here are the must-have features that separate powerful platforms from basic tools.

1. Unified, omnichannel conversation centre

Customers often start a conversation via live chat, follow up through email, then call for urgent help. Without a unified view, agents lose context between these touchpoints, leading to repetitive questions and fragmented experiences. 

A unified conversation centre brings multiple channels, like voice, SMS, email, and live chat, into one connected system. This provides agents with the context they need to respond more quickly and facilitate seamless communication regardless of how a customer reaches out. 

Real-life example:  

• RateMyAgent needed a way to manage communications across multiple platforms without losing context. The support team was struggling to deliver fast, personalised responses while switching between tools. 

• To solve this, they integrated Aircall with Intercom to give agents a unified view of every customer interaction. Now, their team can move seamlessly between chat, voice, and SMS to ensure continuity and speed in every conversation. 

• This multichannel setup has become a core part of their high-touch customer experience, building trust and reducing resolution times.

2. Advanced voice features

Even with the growth of chat and messaging, 65% of inbound support queries are still handled via voice. That’s because customers want quick resolutions from someone who understands their issues. 

Your CCM stretch stack should include advanced voice capabilities that reduce friction and drive faster query resolution.

Key voice features to look for:

• Interactive voice response (IVR): Reduce wait times by creating a custom IVR flow that connects callers with the right agent as quickly as possible.

• Smart call routing: Match calls to the best-suited agents based on skill, location, or business hours.

• Call tags and disposition codes: Categorise and track call outcomes for smarter follow-ups and analytics.

• Call coaching and whispering: Coach agents in real time and monitor quality without interrupting calls.

Discover an integrated customer communications platform that leads to better conversations and reduces pressure on your team. Explore Aircall's features. 

3. Time-saving AI functionalities

45% of support teams that use AI report time savings, and over four in ten report faster and more efficient resolution of customer issues. To see these kinds of results, a customer communication management software needs built-in AI capabilities designed to streamline everyday workflows.

When choosing customer communication management software, look for AI features that analyse customer conversations to point out trending topics, customer sentiment, and key takeaways. These insights significantly reduce the time it takes to review interactions so you can pinpoint where to improve quicker.

And AI voice agents and chatbots take pressure off your team and free up their time for higher-value conversations by solving routine customer issues. Aircall’s AI Voice Agents, for example, handles frequently asked questions, qualifies leads, and escalates complex cases so they reach the correct agent.

Real-life example:  

• Puls was juggling three different phone systems, each with its own limitations that complicated internal communications and customer-facing interactions.

• Aircall emerged as the singular platform solution that covered all their needs, significantly reducing overhead costs and simplifying their call management processes.

• The true game-changer for Puls was the introduction of Aircall AI to their CCM tech stack, as the tool has drastically reduced the time spent on call reviews and empowered the team with valuable insights into customer interactions.

4. Integrations that connect your communications tech stack

“One of the biggest customer communication challenges is data fragmentation,” says Gauri Manglik, CEO and Co-Founder of Instrumentl. “When customer information lives in separate systems, you create disjointed experiences.” 

Disconnected systems lead to slow, frustrating workflows: agents lose time entering data manually, switching between tools, or chasing down context across platforms. This not only reduces team productivity but also weakens the overall customer experience.

Integrations solve this by syncing all your customer communication data, from voice calls and emails to helpdesk tickets and CRM notes. Smooth data flow enables you to eliminate information silos and manage customer conversations in context. 

Real-life example:  

• As RapidG’s customer base expanded, their 17-person support team found it increasingly difficult to manage high-volume queries across disconnected platforms. Manual updates and context-switching slowed resolution times and impacted consistency.

• The integration of Aircall with Zendesk enabled RapidG to build a unified support workflow, where all call data, such as transcripts, tags, and customer history, synced automatically. 

• This eliminated manual tasks, improved collaboration, and helped the team deliver a “big-company-level” experience while supporting thousands of B2B clients efficiently.

Aircall offers 250+ integrations, from HubSpot and Salesforce to Slack and Zendesk, to keep your CCM tech stack connected. Explore all integrations. 

5. Automation capabilities

Support teams lose hours each week to repetitive tasks like logging calls, setting reminders, and following up manually. A strong customer communication management tool uses automation to reduce this workload and keep response times fast.

“Automation tools for triggered communications and workflow management are indispensable for efficiency and consistency,” underlines Guari. These tools also reduce errors and enable agents to focus on more valuable conversations. The result: faster resolutions that help you exceed customer expectations.

Key automation features to look for:

• Voicemail transcriptions and summaries: Automating this process allows for quicker call reviews and analysis.

• Automated follow-up reminders and notifications: Ensure your agents never miss a follow up by automating reminders. 

• Workflow triggers based on call outcomes or tags: Automated workflows move customer conversations along seamlessly and minimise manual effort. 

• Auto-responses for routine queries: When your AI agent or chatbot answers common questions, your customer service team has more time to focus on complex issues that need their attention.

6. Personalisation features

Personalised communication based on customer history, behaviour, or preferences turns basic support into genuine connection. A strong CCM tool taps into CRM data to tailor greetings, anticipate needs, and trigger personalised follow-up flows.

These features include screen pops that automatically display caller information (like name, company, and order details) before agents pick up, as well as triggered follow-up journeys that send tailored messages or offers based on previous interactions.

Real-life example: 

• Trussle wanted to make sure sales reps were asking all the right questions and were clear on which documents customers needed to provide.

• The company integrated Aircall with HubSpot to personalise every call. Agents receive contextual information before answering, which enables them to address customers by name and reference past interactions. 

• This made conversations feel more human and frictionless, strengthening trust and improving the customer experience.

7. Analytics and reporting

Analytics turn day-to-day activity into actionable insights. A strong CCM tool should offer both real-time and historical reporting to help teams optimise performance and customer experience.

Some key metrics to track include first-contact resolution (FCR), average response and handling time, customer satisfaction (CSAT), and agent activity and workload balance. These insights help managers identify bottlenecks, monitor individual and team performance, and guide training and staffing decisions.

Aircall’s Analytics+ dashboard, for example, gives teams visibility into everything from missed call rates to individual agent productivity. Custom filters and live metrics make it easy to course-correct quickly.

Real-life example: 

• Go Global Travel’s customer support team often navigates complicated problems over the phone. Before Aircall, the company didn't have a way to know whether customer support teams were achieving their goal of providing the optimal and ultimate solutions as quickly and as well as possible.

• Adopting Aircall has enabled Go Global Travel to streamline workflows and deliver a more seamless customer experience, thanks to advanced call centre analytics and strategic integrations.

• The organisation can now measure call activity and track KPIs at a global level, including missed calls, average wait times, and call duration. Operations managers use analytics to assess individual performance and find ways to help agents improve.

How to choose a CCM tool: Expert tips and top options

Choosing the right CCM solution starts with understanding two things: how your team works and how your customers prefer to connect. Are your customers more likely to call, chat, or email? Do they expect instant answers or value proactive follow-ups?

A strong CCM solution should fit seamlessly into your existing workflows while helping you scale personalised, efficient support. Use the checklist below to guide your evaluation:

• Current channel coverage: Voice, SMS, email, chat, social

• Integration needs: CRM, helpdesk, eCommerce, sales automation

• Reporting requirements: Live dashboards, historical trends, team-level analytics

• Agent workload: Headcount, shifts, multichannel volume handling

• Automation/AI readiness: Call summaries, IVR, chatbots, AI assistants

With this in mind, let’s compare a few leading CCM tools across the key capabilities that matter most to growing support teams.

Discover Aircall, a voice-first customer communication management tool

Aircall brings together everything modern support and sales teams need in an integrated customer communication management tool. It’s voice-first by design but built for omnichannel execution, and it offers time-saving AI features, deep CRM and helpdesk integrations, advanced automations, real-time analytics, and effortless usability.

With over 250 integrations, Aircall connects seamlessly with the tools you already use, such as HubSpot, Salesforce, Pipedrive, Slack, and Zendesk, to make it easy to create unified workflows and scale without friction.

Whether you're aiming to scale your support team, speed up your sales cycle, optimise customer communications processes, or unify multichannel communication, Aircall provides the foundation to do it all, without siloed data or IT bottlenecks. 

Provide personalised support in every interaction with an integrated customer communications and intelligence platform. Learn more about Aircall.

Frequently asked questions about customer communication management tools

What is a customer communication tool?

A customer communication tool helps businesses manage and streamline conversations across channels like voice, SMS, chat, and email. These platforms centralise communication, improve response speed, and offer visibility into every customer interaction, helping teams deliver faster, more personalised support.

Which CCM tool is best?

The best tool for managing customer communications depends on your business needs. If voice is a core channel and you need strong integrations, automation, and analytics, Aircall is a top choice. It’s built for growing teams and offers 100+ integrations, AI-powered features, and an easy setup, making it ideal for fast-scaling support and sales operations.

What is a CCM vs CRM?

A CCM tool manages real-time conversations across support and sales channels. A CRM tracks long-term customer preferences and relationships, storing data like purchase history and your sales pipeline. Together, these tools create a complete customer engagement ecosystem—CCM for real-time interaction, CRM for context and follow-up—that help boost customer loyalty. 

Even once setup is done, bigger questions remain. Is your team getting full value from what was built? Are reps following the Playbook? Is customer satisfaction improving?

This release tackles both ends. Setup gets faster. Reps and their managers get the same view of how each call went.

Less set up. Faster to value.

Live Prompts that practically build themselves

Live Prompts give your reps real-time guidance during calls. The right answer, the right rebuttal surfaced the moment a rep needs it. Until now, every prompt card had to be built by hand: a title, a trigger phrase, keywords, a response guide, one at a time.

With this release, you point Aircall at your help centre, sales battlecard, or training doc, and Aircall produces editable prompt cards your reps see during calls. The cards reflect the content your team already trusts, in the tone your team already uses. When the source changes, the cards update.

Live Prompts now also trigger based on what your reps say on the call, not only what customers say. Useful for reinforcing talking points or reminding reps of key details in their discussion.

Setup drops from hours of writing to a single configuration step. Real-time guidance reaches your reps from day one of a rollout, not week three.

Scorecard Templates: A starting point to every call scored

AI call scoring evaluates each conversation against a scoring framework you define: criteria like discovery, objection handling, empathy, or product knowledge. Aircall scores every call against that framework so managers focus coaching on the calls that need it.

To make Call Scoring even faster to set up, three editable templates are now available: Sales, Support Quality, Soft Skills. Pick the one closest to your team, edit the questions if needed, hit publish.

Coach with evidence, not gut feel.

Visibility into how reps follow the assigned sales methodology

A Playbook is the structured guide your team follows during a call: discovery questions for sales, troubleshooting steps for support, the closing sequence you want every rep to hit. Aircall surfaces the right Playbook in real time during the call.

Until now, you had no way to see whether they followed it, and reps had no way to know how they did until a deep dive with their managers.

With this release, every evaluated call now shows an adherence score and a per-section coverage view. Managers spot which sections get skipped at a glance. Reps walk into their next call already knowing where to focus. 

Predicted CSAT puts a satisfaction signal on every support and success call.

Surveys reach a small share of conversations and come back days late. Predicted CSAT delivers a score on every eligible support or success call, with the transcript moments that explain why. No more surveys needed.

Filter your queue by low scores, jump straight to the moments that drove them, and act before the customer is gone. The score is grounded in your team's actual conversations, not a black box.

Bring AI to more of your team. In 10 more languages.

Aircall AI now extends to 10 more languages across the Middle East and Asia-Pacific:  

Hebrew ·  Mandarin ·  Cantonese ·  Japanese ·  Korean ·  Vietnamese ·  Hindi ·  Tamil ·  Malay ·  Thai

If your team handles calls in any of these languages, the AI features you use today now work on those calls too: transcription, summaries, real-time guidance, call scoring. That gives multi-region teams consistent AI coverage across every customer conversation.

These languages join the 25 other languages already available. 

What this means for your team

Starting to roll out Live Prompts, Call Scoring, or Playbooks? This release makes setup faster and value visible sooner. Less manual work to get started. A shared view of every call. And Aircall AI now reaches teams in 10 more languages.

Already running those features? This release deepens what they deliver. More prompt coverage with less effort. Scorecards that activate in one flow. Evidence on every call for both managers and reps to act on.

All in one tool. No migration project.

Adding AI to your customer conversations doesn't have to mean an IT project or hours of configuration. With Aircall, calling, AI, coaching, and analytics live in the same tool, ready to use from the dashboard your team already knows. 

No migration, no separate platform, no waiting on a system overhaul to see value. Just all the tools you need to prepare before the call, maximise the conversation while it’s happening, and reduce wasted time on toggling and admin after the call.

Get started today

All of these updates are live in AI Assist Pro right now.

Aircall Analytics surfaces call performance and revenue insights in real time so the manager walks into that conversation knowing which criteria drove the score down across all 63 calls, not two. AI call scoring does not change the coaching conversation. It changes what the manager walks into that conversation knowing. Instead of two calls and a feeling, the manager has scores for all 63 calls, a trend line on discovery question adherence, and three flagged calls where the score was lowest. That is the same coaching conversation, with a different starting point.

Key takeaways

• AI call scoring evaluates every call against a defined scorecard, giving managers performance data across 100% of interactions

• The score is the sum of individual question evaluations: each visible and reviewable, not an opaque number

• Sales and support scorecards use different criteria because the definition of a successful call differs between the two

• The score starts the coaching conversation: it identifies which call to review and which behaviour to discuss

What is AI call scoring and what does it produce?

AI call scoring is the automated evaluation of every call against a defined scorecard using AI, producing a numerical score that reflects how well the call met the criteria the manager configured. It covers every call the team makes, not a sampled fraction. The score is not a single opaque number: it is the aggregated result of individual question evaluations, each of which the manager can review to understand exactly what drove the score up or down.

AI call scoring is the process of automatically evaluating every sales or support call against a manager-defined scorecard using AI, assigning scores to each question on the rubric and aggregating them into an overall call score that reflects behavioural compliance, conversation quality, and outcome confirmation. It covers 100% of calls without requiring a human listener for each recording.

Three components make AI call scoring function. First, transcription: the call audio is converted to text, providing the AI with the call content to analyse. Second, the scorecard: a manager-configured set of questions that define what the AI evaluates on each call and how each question is weighted. Third, the score: the aggregated result of all question evaluations, visible question by question, and the starting point for a coaching conversation rather than its conclusion.

In Aircall, AI-evaluated and manager-evaluated questions can be combined in the same scorecard. Some criteria are scored automatically by the AI based on transcript analysis; others are completed by the manager after reviewing the flagged call. How Aircall's AI call scoring works and how managers use scorecard data to coach sales and support teams covers the full configuration and review workflow.

Salesforce research across 3,075 customer service professionals confirms that 70% of service organisations deploying AI observe measurable value within 60 days, with customer satisfaction as the number-one improved KPI. AI call scoring is not a monitoring tool. Its value is measured in agent development and customer experience outcomes, not in the number of calls evaluated.

What criteria does AI call scoring evaluate?

AI call scoring evaluates three categories of criteria: compliance and script adherence (binary criteria the AI scores with high accuracy), conversation quality and behavioural criteria (scored with moderate accuracy, requiring specific rubric definitions), and outcome and resolution criteria (whether the correct next step was confirmed and the resolution language was used). The accuracy of each category depends on how specifically the scorecard defines what the AI should look for.

Call Centre Helper's research on contact centre metrics confirms that quality scoring in most contact centres involves a quality team reviewing five to six calls per agent per month and completing a scorecard. AI call scoring automates that established process rather than introducing a new one. The criteria it evaluates are the same criteria experienced quality analysts have always assessed, applied consistently to every call rather than a sampled few.

Call scorecard is the structured set of questions a manager configures to define what the AI evaluates on each call, including the rating scale for each question (yes/no, 1-5), the weighting of each question in the overall score, and the threshold that triggers a coaching flag.

A well-designed scorecard with specific, binary questions produces scoring that managers trust; a generic scorecard produces scores that feel arbitrary.

Sentiment analysis is the AI-driven process of detecting emotional signals in a call transcript, positive, neutral, or negative tone indicators at the speaker level, that supplement the structured scorecard score with emotional context. It is most useful for identifying calls where the customer became frustrated mid-interaction, regardless of whether the agent's compliance and behavioural scores were otherwise strong. Sentiment signals do not replace scorecard criteria; they add a layer of context to help prioritise which flagged calls to review first.

The full AI feature set available in Aircall core plans, including call scoring, summaries, and sentiment analysis covers how scoring and sentiment work together as part of a broader call intelligence layer.

How does AI call scoring differ between sales and support teams?

The scoring tool is the same; the criteria differ because the definition of a successful call differs between sales and support. A successful sales call ends with a confirmed next step, a clear deal stage update, and adherence to the sales methodology. A successful support call ends with the issue resolved on first contact, the compliance script followed, and the customer's satisfaction confirmed before the call ends. The scorecard must reflect which success definition applies to the team being evaluated.

Sales team scorecard criteria:

• Did the rep open the call by setting a clear agenda? (binary)

• Did the rep ask at least three open-ended discovery questions? (count-based)

• Did the rep handle the primary objection raised without immediately discounting? (behavioural)

• Did the rep confirm the next step with a specific date and action before ending the call? (outcome)

• Did the rep follow the defined sales methodology framework questions? (compliance)

• Did the rep avoid disclosing pricing before the value proposition was established? (compliance)

Support team scorecard criteria:

• Did the agent use the required greeting and verify the customer's identity? (compliance)

• Did the agent follow the prescribed resolution flow for the issue type? (script adherence)

• Did the agent place the customer on hold fewer than three times? (behavioural)

• Did the agent confirm the issue was fully resolved before ending the call? (outcome)

• Did the agent deliver the required regulatory disclosure within the first two minutes? (compliance)

• Did the agent log the call disposition correctly before closing? (process compliance)

A sales manager implementing AI call scoring for the first time should start with the SPICED or BANT criteria their team already uses for deal qualification, those framework questions translate directly into scorecard questions the AI can evaluate. A support manager should start with the compliance and script adherence criteria from the existing manual QA form. Both should add outcome criteria as the third category and leave subjective empathy and tone questions for human review on flagged calls.

How AI voice analytics and call scoring work together to surface performance insights from every call covers the technical layer that makes both sales and support scoring work.

How do you use AI call scoring data for coaching?

AI call scoring changes three things in the coaching workflow. First, the starting point: instead of reviewing random calls to form an impression, the manager starts with the flagged calls the system identified as lowest-scoring on the criteria that matter most. Second, the specificity: instead of "you need to improve your discovery questions," the coaching conversation is "your discovery question score was 45% this month, compared to the team average of 72%, let's listen to this call together." Third, the tracking: the manager can measure whether coaching is working by comparing a rep's score on specific criteria month over month, not just whether overall performance feels better.

Coaching from data is the practice of grounding every agent development conversation in measurable, criterion-specific performance data from actual calls, rather than in the impression a manager formed from a manually reviewed sample. It requires consistent data from a representative set of interactions, which is exactly what AI call scoring provides at 100% coverage. The conversation is the same; the evidence it is built on is categorically different.

Before AI call scoring: "I listened to your call on Tuesday and felt you were rushing through discovery. Try to slow down and ask more questions before moving to the demo."

After AI call scoring: "Your score on discovery question adherence was 40% last month. The team average is 68%. I pulled up the three flagged calls from last week, let's listen to how you opened each one and where the discovery questions were skipped. Then we can practice a re-run on the first one."

Call Centre Helper's coaching strategies research confirms that modern contact centre platforms make it easy to deliver specific, data-grounded feedback using dashboards, transcripts, sentiment analysis, and historical performance data, and that agents perform better when feedback is specific rather than impression-based. The second coaching conversation above is specific, grounded in evidence, and trackable. The first is not.

Scoring threshold is the minimum acceptable score a manager sets on the scorecard, below which a call is automatically flagged for human review and added to the coaching queue. Setting the threshold too low produces too many flagged calls for the manager to meaningfully review; too high and the system misses the calls that most need attention. The optimal threshold is determined during the calibration period, after comparing AI scores to manual scores on 20-30 calls to understand where the AI consistently identifies the same calls a quality analyst would flag.

What conversation intelligence means in practice and how call scoring fits within it covers how scoring integrates with the broader coaching and performance infrastructure.

Frequently asked questions

What is AI call scoring?

AI call scoring automatically evaluates every sales and support call against a defined scorecard using AI, assigning a score based on how well the call met the manager's configured criteria, without a human listener reviewing each recording. Scores cover every call, not a sampled few.

What does AI call scoring evaluate?

AI call scoring evaluates three categories: compliance and script adherence (binary, high accuracy), conversation quality and behavioural criteria (moderate accuracy, requires specific rubric definitions), and outcome criteria (whether the agent confirmed a next step or used correct resolution language). The scorecard defines what the AI evaluates.

Is AI call scoring accurate enough for coaching?

Yes, when the scorecard uses specific, binary criteria the AI can evaluate reliably from the transcript. The score is the sum of individual question evaluations, each visible and reviewable. Managers use it to identify which calls to review and which criteria to focus coaching on.

How is AI call scoring different for sales calls and support calls?

Sales scorecards focus on methodology adherence, discovery questions, objection handling, and next step confirmation. Support scorecards focus on script compliance, empathy, first call resolution, and CSAT-correlated behaviours. The tool is the same; criteria differ because the definition of a successful call differs between teams.

How do you set up an AI call scoring system?

Define a scorecard with 8-12 criteria, mostly binary or specific enough for AI evaluation from the transcript. Validate by comparing AI scores to manual scores on 20-30 calls. Calibrate the coaching threshold after validation. Deploy to the full team only once the scorecard is validated.

What is the best call centre analytics software?

The best call centre analytics software surfaces call performance and revenue insights in real time, combining call scoring, transcription, and sentiment analysis in one interface. Aircall Analytics surfaces call performance and revenue insights in real time with AI-evaluated scorecards, flagged coaching queues, and performance trends across 100% of calls.

What we are

What surfaces when you have scores for every call?

The most significant change is not the number of calls evaluated. It is the quality of the coaching conversation that follows. A manager who reviews two calls a week forms an impression. A manager who reviews two flagged calls from a pool of 300 scored calls walks into the coaching conversation knowing which criteria drove the score down, which calls were outliers, and whether last month's coaching made a measurable difference on the criteria it targeted.

That specificity, the ability to say "your score on discovery question adherence improved from 40% to 65% this month" rather than "I felt like you were more engaged this week", is what AI call scoring produces that manual sampling cannot. Not because the AI is a better evaluator than a quality analyst. Because it evaluates every call, consistently, against the same criteria, and makes those evaluations reviewable.

The score is the starting point for the coaching conversation. The conversation is what improves performance. Both are required. The scorecard design is the decision that makes both useful.

Get started with Aircall today

Aircall coaches sales reps in real time with AI guidance during live calls, and extends that coaching infrastructure to QA with automated call scoring across 100% of interactions. AI quality assurance does not change the QA process. It changes the coverage. The scorecard is the same. The coaching conversation is the same. What changes is that the manager now has scoring data for all 300 calls, not 6.

Key takeaways

• Manual QA covers fewer than 5% of calls: AI QA scores every call automatically against a defined scorecard

• 41% of contact centres monitor fewer than 4 calls per agent monthly: AI QA eliminates that coverage ceiling

• AI QA accuracy depends entirely on scorecard configuration: validate against manual scores before deploying to the full team

• AI QA handles coverage; the manager handles coaching: neither replaces the other

What is AI quality assurance and how does it differ from manual QA?

AI quality assurance for call centres is the process of automatically scoring every call against a defined QA scorecard using AI, without a human reviewer listening to each call. The difference from manual QA is coverage: manual QA samples a fraction of calls because human review time is finite; AI QA scores every call because the scoring is automated. The process, scorecard, evaluation, coaching conversation, is the same. What changes is the number of calls it applies to.

AI quality assurance (AI QA) is the automated application of a defined QA scorecard to every call in a contact centre, using AI to evaluate each scorecard question against the call transcript and assign a score without a human reviewer, replacing the manual sampling step that limits most teams to fewer than 5% QA coverage.

Contact centre benchmarks compiled by Plivo in 2025, drawing on Call Centre Helper's industry research, confirm that the QA scoring standard is 4 calls per agent per month, and that a significant share of contact centres fall below even that level. A 15-agent team making 300 calls per day reviewed at that rate covers roughly 60 calls per month out of 6,000 made, 1% coverage. AI QA scores all 6,000.

Call Centre Helper's expert guidance on QA sample size confirms that the optimal manual QA sample is 5-6 calls per agent, but that setting a universal volume target without a strategic approach wastes resources. The value of AI QA is not simply that more calls are reviewed. It is that every call is scored consistently, and human review is focused on the calls AI flags as most relevant for coaching.

QA coverage rate is the percentage of all calls that are reviewed and scored against a QA rubric in a given period. For a team making 6,000 calls per month that reviews 60 manually, the QA coverage rate is 1%. AI QA raises that rate to 100% without increasing analyst headcount, which changes the entire basis of performance visibility and coaching prioritisation.

How does AI call scoring actually work?

AI call scoring works in four steps. First, the call is transcribed from audio to text. Second, the AI evaluates each question on the QA scorecard against the transcript: for binary questions, it determines whether a required phrase or action occurred; for rating questions, it evaluates the interaction against the defined criteria. Third, a score is calculated and written to the call record. Fourth, calls meeting a manager-defined threshold are flagged for human review and added to the coaching queue.

The accuracy of both question types depends on how precisely the scorecard defines what the AI should evaluate. A rubric that asks "was the agent professional?" gives the AI nothing specific to assess. A rubric that asks "did the agent avoid interrupting the customer during the first 30 seconds of the call?" gives the AI a specific, evaluable criterion it can identify in the transcript. Scorecard design is the most important step in AI QA implementation, more important than the tool itself.

Automated call scoring is the process of using AI to evaluate every call against a predefined QA rubric automatically when the call ends, producing a numeric score without a human reviewer. It makes QA coverage scalable by removing the per-call time cost of manual review while applying the same criteria consistently across every interaction.

How do you build a QA scorecard that AI can score reliably?

A QA scorecard that AI scores reliably has three design principles: most questions are binary or rule-based rather than subjective, each question defines precisely what the AI evaluates rather than describing an outcome, and subjective questions requiring human judgment are flagged for analyst review rather than AI scoring. The scorecard is the single biggest variable in AI QA accuracy, a well-designed scorecard produces scoring that managers trust; a poorly designed one produces scores that feel arbitrary and causes teams to abandon the tool within the first month.

QA scorecard is the structured evaluation framework used to assess call quality, consisting of specific questions each scored on a defined rating scale (yes/no, 1-5), applied consistently across every reviewed call; in an AI QA context, questions must be designed around criteria the AI can evaluate from a transcript rather than those requiring human judgment about tone or emotional nuance.

1. Binary compliance questions (high AI accuracy): "Did the agent confirm the customer's account number before discussing account details?" (yes/no), the AI searches the transcript for this step and scores accordingly

2. Specific behavioral questions (high AI accuracy): "Did the agent avoid interrupting the customer in the first 60 seconds?", evaluable against transcript timing and turn structure

3. Script adherence questions (high AI accuracy): "Did the agent use the required product disclosure language within the first 3 minutes?", evaluable against a defined phrase or phrase equivalents

4. Outcome questions (moderate AI accuracy): "Did the agent confirm the next step before ending the call?", evaluable but depends on how precisely the required outcome is defined in the rubric

5. Subjective quality questions (low AI accuracy, flag for human review): "Was the agent empathetic throughout the call?", reserve these for the flagged calls that a human reviewer follows up on

6. Validation step (required before full deployment): compare AI scores to analyst scores on 20-30 calls; the gap on each question reveals which criteria need refining before the AI scores are trusted

Calibration is the process of comparing AI QA scores to manual scores from an experienced analyst on the same set of calls, before the AI scoring is trusted for coaching decisions. Calibration reveals which scorecard questions are ambiguous or poorly defined, the questions where AI and analyst scores diverge most are the ones that need refinement before deployment.

What is the implementation sequence for AI QA in a call centre?

AI QA implementation has five steps in a defined sequence. Skipping or reordering any step produces unreliable output. The most common implementation failure is enabling the tool before completing steps 1 and 2, which produces automated scores without a validated rubric and creates the "the scores feel arbitrary" problem that causes teams to abandon AI QA before it has been properly configured.

• Step 1: Configure the scorecard. Define each QA question with a binary or specific rubric, assign a rating scale (yes/no or 1-5), and set a preliminary flag threshold. Do not enable AI scoring until the scorecard is complete. A scorecard with 8-12 questions, majority binary, takes approximately one day to build.

• Step 2: Validate on 20-30 calls. Have an experienced QA analyst score the same calls the AI will score, then compare. The gap on each question reveals which criteria are ambiguous and need refinement before the AI scores are trusted.

• Step 3: Calibrate the threshold. Set the coaching flag threshold based on validation data, not the default setting. The threshold should trigger a review on calls that the validation showed were genuinely below standard, not an arbitrary percentage of all calls.

• Step 4: Deploy and observe for 30 days. Run AI scoring across the full team without using the data for formal coaching yet. In a platform like Aircall, AI-powered call scoring and QA is configured in the same dashboard as the phone system and recordings, the scorecard, the transcript, and the AI score are all in one interface, which removes the integration complexity that slows deployment when QA is a separate tool. The first 30 days identifies systematic scoring anomalies and gives managers time to calibrate their interpretation of the scores before using them in performance conversations.

• Step 5: Use flagged calls as coaching starting points. The score identifies which call to review. The conversation with the agent is where coaching actually happens. How Aircall's AI call scoring helps managers coach teams faster and more consistently covers how to structure those conversations around scorecard data rather than subjective impressions.

What results should you expect from AI quality assurance?

AI QA produces two phases of results. In the first 30-60 days, managers have QA coverage data across 100% of calls for the first time. Coaching conversations shift from reactive (addressing the call someone happened to review this week) to proactive (addressing the pattern the AI flagged across dozens of calls this month). Aggregate QA scores often appear more consistent than manually-sampled scores suggested, because manual sampling inadvertently selected easier-to-review calls rather than representative ones.

In months 2-6, as coaching conversations become grounded in AI QA data, aggregate scores improve and performance gaps between top and bottom quartile agents narrow. BCG's research on AI agents in customer service confirms that AI-driven improvements in resolution accuracy reduced repeat inquiries by 25%, establishing that the coaching quality improvement enabled by higher QA coverage directly affects the customer-facing metrics managers are accountable for.

A concrete Phase 1 scenario: a manager who previously reviewed 6 calls per week now has AI scores for 300 calls per week. Her coaching conversations are no longer based on the calls she happened to pick. They are based on the calls the AI flagged as below the threshold on compliance or call quality criteria. A concrete Phase 2 scenario: agents receiving consistent, criterion-specific feedback on the same scorecard questions every week, rather than occasional impression-based feedback from the 2% of calls their manager had time to review, show measurable score improvement on those specific criteria within 60 days.

18 best practices for call centre quality assurance covers how to structure the full QA process around AI scoring data, including how to run calibration sessions, how to document coaching conversations, and how to track aggregate score improvement over time.

Frequently asked questions

What is AI quality assurance for call centres?

AI quality assurance for call centres automatically scores every call against a defined QA scorecard using AI, replacing the manual sampling step that limits most contact centres to fewer than 5% coverage. It gives managers coaching data across 100% of calls without additional QA analyst time.

How accurate is AI call scoring?

AI call scoring is highly accurate for binary, rule-based questions, required greeting, disclosure script, callback number captured. It is less reliable for subjective questions involving tone or empathy. Design the scorecard with specific, binary questions to maximise AI scoring accuracy.

How many calls should a call centre review for quality assurance?

The industry standard is 4-6 calls per agent per month for manual QA. Most contact centres fall below this, with 41% monitoring fewer than 4 calls per agent monthly. AI QA eliminates this ceiling by scoring every call automatically, giving managers full performance visibility without increasing QA analyst headcount.

Does AI QA replace manual call monitoring?

No. AI QA replaces the manual sampling step that limits coverage to a small fraction of calls. It does not replace the human judgment required to interpret scores, design coaching conversations, or handle complex performance issues. AI QA handles coverage; the manager handles coaching. Both are required for effective QA.

How long does it take to implement AI quality assurance?

Technical setup takes 1-2 days. Meaningful implementation, scorecard configuration, validating AI scores against manual scores on 20-30 calls, calibrating thresholds, takes 2-4 weeks. Deploy to the full team only after validation. The first 30 days produces calibration insights, not performance verdicts.

What is the best AI sales coaching software?

The best AI sales coaching software coaches reps in real time during live calls with sentiment signals, objection prompts, and script guidance, then extends that coaching to QA with automated call scoring after every call. Aircall coaches reps in real time with AI guidance alongside full QA coverage.

What we are

Where do you start with AI QA, and what coaches the team once it is live?

The teams that get useful output from AI QA in the first 30 days are the ones that built the scorecard before switching the tool on. The teams that disable it after a month are usually the ones that enabled it without a configured scorecard and got scores that felt disconnected from the coaching conversations they already knew how to have.

The starting point is not the tool. It is a scorecard with 8-12 questions, most of them binary, all of them specific enough that the AI knows exactly what to evaluate. That takes a day to build. The validation period takes two weeks. After that, the team has AI QA data across 100% of calls and a coaching process grounded in something more reliable than six randomly sampled calls per manager per week.

How to implement contact centre AI without large IT resources or budget covers the full AI rollout sequence for contact centres starting from minimal automation, including how to introduce QA scoring without disrupting existing workflows.

The company has a contact centre. It has a phone, email, and a helpdesk. The channels are not connected. The email thread is in one system. The phone queue is in another. The agent knows nothing about what happened before they picked up. Aircall routes every inbound call to the right agent instantly with AI, and connects that routing to the CRM context that tells the agent what has already happened before they say hello. The distinction between a call centre and a contact centre is not which channels a team operates. It is whether those channels share customer data. That is the test. This article applies it.

Key takeaways

• A call centre handles phone interactions only; a contact centre handles phone plus digital channels with shared customer data

• The defining difference is not the number of channels: it is whether customer context travels between all of them

• Voice accounts for over 55% of contact centre interactions: most contact centres are primarily call centres with digital supplements

• Adding channels without connecting them to shared customer data produces a multi-channel operation, not a contact centre

What is a call centre and what does it do?

A call centre is a customer service operation that handles interactions primarily over the telephone: routing inbound support calls to the right agent, managing outbound calling campaigns, and recording and monitoring calls for quality assurance. It is built and optimised for high-volume telephone interactions. Agents work from phone queues. Managers monitor calls, coach from recordings, and measure performance through telephony metrics: average handle time, first call resolution, and queue abandonment rate.

Call centre is a customer service operation that handles customer interactions primarily over the telephone, using inbound routing, outbound dialling, call recording, and queue management to process phone-based customer contact at scale. It is optimised for voice: every tool, metric, and workflow is designed around the telephone call as the primary and often only channel.

A call centre is not a lesser version of a contact centre. It is the correct model for operations where the phone is the primary channel that carries meaningful customer volume, where digital channels either do not exist or carry low enough volume to be managed from separate tools without causing agent context gaps. A 10-person inbound support team handling 400 calls per day with minimal email or chat volume does not need a contact centre model. Adding multi-channel infrastructure to a primarily voice operation creates overhead without proportionate benefit.

What a call centre requires operationally: a phone system with skills-based routing, IVR, call recording, and queue management; a CRM or helpdesk that logs call outcomes; and a manager interface for monitoring and coaching. Those are the components. The performance metrics that matter are AHT, first call resolution, queue abandonment rate, and CSAT. Everything in a well-run call centre is designed to make those four numbers better.

What is a contact centre and how is it different?

A contact centre is a customer service operation that handles interactions across multiple channels, phone, email, live chat, SMS, and social media, from a single platform with a shared customer record. The operational difference from a call centre is not the number of channels but whether customer context is shared between them. An agent starting a phone call can see the customer's previous email and chat interactions before answering. That shared context is what distinguishes a contact centre from a multi-channel call centre.

Contact centre is a customer service operation that manages customer interactions across multiple communication channels from a unified platform, with all interactions stored in a shared customer record so agents have full interaction history regardless of which channel the current contact arrives on. The defining feature is not the channel breadth but the shared data layer connecting all channels.

IBM's research on contact centre experience confirms that contact centres critically allow for CRM systems and cohesive knowledge bases that unify customer data from different channels, enabling agents to access complete interaction histories. The practical definition of a contact centre is the shared data layer, not the channel list.

Call Centre Helper's November 2024 research confirms that over 55% of contact centre interactions are conducted over voice, with email at 17% and live chat at 7%. Most operations that describe themselves as contact centres remain primarily voice operations with supplementary digital channels. The contact centre model is most valuable when those digital channels carry genuine volume and are connected to shared customer data, not when they are added to a phone-first operation as separate tools.

How do you know which model your team is actually running?

Apply the context test: can an agent who receives a phone call from a customer see every previous interaction that customer has had on every other channel before answering? If yes, the operation is a contact centre. If no, it is a call centre or a multi-channel operation, regardless of what it is called internally. Most operations that describe themselves as contact centres fail this test on at least one channel transition, typically when a customer moves from email or chat to a phone call.

A three-question operational audit to apply to your own team right now:

1. When a customer calls after emailing, does the agent see the email thread before picking up?

2. When a customer who previously used live chat calls back, does the agent see the chat transcript?

3. When a customer is transferred between agents or channels, do they have to re-explain their issue?

If the answer to any of these is no, the operation has a gap in its contact centre model regardless of the technology it runs. The gap is not a technology failure, it is a data connection failure. The channels exist. The shared record does not.

Call Centre Helper's July 2024 research on channel mix trends confirms that voice increased from 53.5% to 55.4% of interactions between 2022 and 2023, and that many operations are paying lip service to digital transformation without genuinely connecting channels. The operational differences between a contact centre and a call centre and how to choose the right model for your team covers this distinction in depth, including how to diagnose which model a specific operation is actually running.

Omnichannel is the service model where customer context, the complete history of every previous interaction across every channel, travels automatically with the customer into every new contact, so every agent on every channel has full context before responding. It is distinct from multichannel, which describes an operation that offers multiple contact channels but operates them independently without shared customer data. A contact centre is omnichannel when context travels. It is multichannel when it does not.

Multichannel is a customer service model where a business offers multiple channels for customer contact, such as phone, email, chat, and social media, but those channels operate independently without sharing customer interaction history. Each channel has its own workflow, data, and agent toolset, meaning an agent on one channel cannot see what happened on another. Multichannel is the necessary first step toward a contact centre, but it is not the destination.

When should a team use a call centre model and when does it need a contact centre?

The call centre model is right when phone interactions account for the majority of customer contact and other channels carry low or manageable volume that does not require shared customer data. The contact centre model is right when customers regularly switch between channels during a single issue, when agents frequently receive calls from customers who have already emailed or chatted, or when CSAT scores are low because agents lack context from previous interactions. The decision is driven by actual channel volume and customer behavior, not by which model sounds more sophisticated.

Call centre model is right if:

• 90%+ of customer interactions arrive through the phone channel

• Email and chat volumes are low enough to manage from separate tools without creating CRM gaps

• Customers rarely switch channels during a single issue

• The team is fewer than 10 agents and adding a unified platform would create more overhead than it solves

• The operation is heavily outbound and inbound volume is predictable and phone-only

Contact centre model is right if:

• Customers regularly contact the team on one channel and follow up on another

• Agents frequently receive calls from customers who have already emailed or chatted about the same issue

• CSAT scores are low because agents lack context from previous interactions on other channels

• Email or chat volume is high enough that a separate tool creates meaningful agent context gaps

• The team handles 20 or more agents across multiple channels and needs unified reporting

What does a genuine contact centre transition require?

A genuine transition from a call centre to a contact centre requires three operational changes, not just the addition of digital channels. First: a shared customer data layer connecting all channels to one CRM record. Second: a unified agent interface that handles phone, email, and chat from one platform without channel-switching. Third: a defined handoff protocol for channel transitions, so context transfers automatically when a customer switches channels. A team that adds email and chat tools without addressing all three has added channels but not built a contact centre.

CCaaS (Contact Centre as a Service) is the cloud-hosted delivery model for contact centre software, no hardware, subscription pricing, and multi-channel capability accessible from a browser or app. CCaaS makes the contact centre transition operationally viable for teams that previously faced the cost and complexity of on-premise multi-channel infrastructure. What CCaaS means and how cloud-based contact centre software differs from on-premise call centre infrastructure covers the technology model in detail.

Contact centre transition checklist

1. Shared customer data layer: every channel interaction writes to the same CRM record, and agents can see all previous interactions regardless of channel before responding

2. Unified agent interface: agents handle phone, email, live chat, and messaging from one platform without switching between tools or logging into separate systems

3. Handoff protocol defined: when a customer switches channels, context transfers automatically or through a defined process so they do not have to re-explain their issue

4. Routing parity: inbound contacts on all channels are routed to the right agent using the same skills and availability rules as phone calls, not handled first-come, first-served from a separate queue

5. Unified reporting: managers have a single dashboard showing performance across all channels, not separate reports from each tool

For the shared customer data layer specifically: a platform like Aircall handles 250+ native integrations with CRM and helpdesk tools, which means the phone call, the AI summary, and the agent's CRM context are all available in the same interface as digital channel connections. How Aircall is built as a voice-first platform that connects to digital channels for both call centre and contact centre operations shows what that connection looks like in practice.

How is AI changing the contact centre and call centre distinction?

AI is blurring the practical boundary between call centres and contact centres in 2026. AI voice agents can now handle routine inbound calls autonomously around the clock, which means a voice-first operation can offer 24/7 inbound handling without a full multi-channel contact centre infrastructure. AI call summaries make context transfer between channels possible without manual note-taking. AI routing can direct customers to the right channel as well as the right agent. The practical implication: a phone-first call centre can extend its reach into digital channels with AI tools that were not available three years ago, without the full infrastructure investment of a traditional contact centre.

Three specific ways AI changes the call centre vs contact centre decision in 2026:

• AI voice agents can handle after-hours inbound calls autonomously on the voice channel. A call-only operation can now offer 24/7 inbound resolution on its existing voice infrastructure without building out a full multi-channel contact centre. The call centre's coverage gap is partially closed by AI before the contact centre transition is needed.

• AI call summaries make context transfer between channels possible without a fully unified agent interface. If a customer's email and chat history are in the CRM and AI summaries from previous calls are written to the same record, a phone agent can see that history before answering, even if they are not operating from a unified multi-channel inbox.

• AI routing can direct customers intelligently between channels and agents without the full multi-channel queue management of a contact centre. A phone system with AI routing can reduce misroutes and first-contact failures before the full contact centre infrastructure is in place.

How AI is reshaping the contact centre model and what it means for teams transitioning from call centre operations covers these developments in detail.

Frequently asked questions

What is the difference between a contact centre and a call centre?

A call centre handles customer interactions over the phone only. A contact centre handles interactions across multiple channels from a single platform with shared customer data. The key difference is not the number of channels but whether customer context travels between them automatically.

What is a call centre?

A call centre handles customer interactions primarily over the phone: routing inbound calls to the right agent, managing outbound campaigns, and recording calls for quality assurance. It is optimised for high-volume telephone interactions and does not typically manage other channels from the same platform.

What is a contact centre?

A contact centre handles interactions across multiple channels, phone, email, live chat, SMS, and social media, from a single platform with a shared customer record. An agent on a phone call can see the customer's email and chat history before responding, without asking them to repeat themselves.

When should a business use a contact centre instead of a call centre?

When customers regularly switch channels to resolve a single issue, or when the team handles significant volume beyond the phone. If customers frequently call because their email went unanswered, a contact centre, where email and phone share the same customer record and queue visibility, directly addresses that failure.

Can a call centre become a contact centre?

Yes, but adding channels is not the same as becoming a contact centre. A genuine transition requires three things: a shared customer data layer connecting all channels to one CRM record, a unified agent interface for all channels, and a handoff protocol so context travels when customers switch channels.

What is the best inbound call centre software?

The best inbound call centre software routes every call to the right agent instantly using AI and skills-based routing, logs every interaction to the CRM, and gives managers real-time queue visibility. Aircall routes every inbound call to the right agent instantly with AI across call centre and contact centre setups.

What we are

Which model routes customers to the right experience for your team?

The answer is not determined by which term sounds more modern or which platform a vendor recommends. It is determined by how customers actually contact the team, whether they switch channels when their first contact fails, and whether agents have the context they need before every interaction, regardless of which channel that interaction arrives on.

If the phone accounts for more than 90% of customer interactions and that percentage has not shifted in three years, a well-operated call centre is the right model. If customers are emailing and chatting and then calling when those channels do not resolve their issue, and agents are starting every phone call by asking "how can I help you today?" without knowing what has already happened, the case for a contact centre model is not theoretical. It is already visible in CSAT scores and repeat contact rate.

The transition is not rebuilding the stack from scratch. It is connecting what already exists to a shared customer record, putting agents in one interface, and defining what happens to context when a customer switches channels. Those three steps are the contact centre. The rest is implementation.

Now there's no need to choose. We built Aircall for Apple CarPlay so you can do it all, safely. Your work calls now appear on your car's built-in display, and you handle them hands-free: answer, decline, or call someone back with a single tap or your steering-wheel controls, eyes where they belong.

Made for the way people actually work

So much of your business still happens by phone, and a lot of it happens away from a desk. Until now, taking a call in the car usually meant reaching for your phone or letting it ring out and calling back later. Neither is a good answer: one isn't safe, the other costs you the moment. Putting Aircall inside the car's native driving interface keeps your team reachable on their work number without compromising safety to do it.

Aircall on CarPlay

On CarPlay, the Aircall app appears right on your car's screen, organised into two simple tabs:

• Calls: your most recent calls, with missed ones clearly flagged, so you can ring someone back with one tap.

• Contacts: your recent contacts, ready to dial without searching or scrolling.

When a call comes in, it shows up on the display whether or not you're already in the app, and you answer or decline using the on-screen buttons or your steering wheel. Miss one, and a notification takes you straight to your recent calls so following up is quick and easy.

Every call still lands in Aircall

Whether a call happens in the car or at your desk, it's logged in Aircall the same way: full call details, the recording, and the conversation history attached to the right contact. Nothing slips through the cracks, managers keep their visibility, and the customer record stays complete. A call taken from the driver's seat looks exactly like one taken from the office, because to your business, it is.

Getting started

Aircall on CarPlay is part of the Aircall mobile app and available to every customer at no extra cost. To use it, you'll need:

• An iPhone on iOS 17 or later

• A CarPlay-compatible vehicle or head unit

• An active Aircall account with at least one phone number assigned

Connect your phone to your car (wired or wireless), open Aircall once on your device, and you're ready to hit the road.

The road doesn't have to be downtime, and it definitely shouldn't be a safety risk. With Aircall on CarPlay, your team keeps moving and keeps talking to customers: hands on the wheel, eyes on the road!

Update to the latest version of the Aircall mobile app to get started.

That discovery happens after signing because the evaluation was built around a feature comparison table. This guide structures the evaluation around four decisions that distinguish call centre platforms in practice, not on paper.

Key takeaways

• Feature comparison tables make every call centre platform look identical: the four criteria that distinguish them are invisible in those tables

• Evaluate on deployment model, use case fit, CRM integration at the tier being evaluated, and AI features included versus add-on

• AI is now a baseline call centre expectation: 74% of customers feel frustrated repeating information to different agents, and CRM integration depth determines whether that frustration is eliminated or replicated

• Total cost at month 12 is often 2-3x the quoted per-seat rate once usage fees, add-ons, and integration costs are included

• Aircall powers AI-driven customer conversations at enterprise scale by making call routing, CRM integration, and AI coaching available in the core plan, without the feature gating that turns a feature table into a negotiation.

What is call centre software and what does it actually do?

Call centre software is a platform that manages how a business makes and receives calls at scale: routing inbound calls to the right agent, enabling outbound dialling at volume, recording every interaction, logging call data to CRM automatically, and giving managers real-time visibility into queue performance and agent activity. In 2026, it also includes AI that summarises calls, coaches agents, and in some configurations handles entire interactions autonomously.

Call centre software, more specifically, is any platform that automates the operational layer of business calling: inbound routing, outbound dialing, call recording, CRM logging, and performance analytics. The category includes tools ranging from a basic IVR with call recording to a full contact centre platform with AI agents, workforce management, and real-time coaching. The meaningful distinctions between platforms are not in the feature list but in what is available at which pricing tier.

CCaaS (Contact Centre as a Service) is the cloud-hosted delivery model for call centre software. No hardware is required, agents access the platform via browser or softphone app, pricing is subscription-based per seat, and deployment is measured in days rather than months. CCaaS is now the standard delivery model for teams without data residency constraints requiring on-premise infrastructure.

Zendesk's customer service research confirms that 1 in 3 customers will leave a brand after one bad service experience and 92% will abandon a company after two to three negative interactions. Call centre software selection is not an operational efficiency decision, it is a customer retention decision. The platform determines whether calls are answered, routed correctly, and resolved on first contact.

Three call centre software categories a buyer typically encounters. Cloud CCaaS deploys in 1-3 days, requires no hardware, suits remote teams, and is the correct default for SMBs and mid-market companies without strict data residency requirements. On-premise software requires hardware procurement and IT-managed installation, gives full data control, and is the correct choice for regulated industries, healthcare, and teams with existing on-site infrastructure that must be used. Hybrid combines cloud interface with selective on-premise components, suits teams that need cloud speed with specific data control requirements, and deploys at a timeline that reflects whichever component takes longest.

Which deployment model fits your team's actual requirements?

The deployment model determines how long it takes to go live, who manages the infrastructure, and what the ongoing maintenance cost looks like. Cloud CCaaS is the right model for teams that need same-day deployment, remote agent support, and no hardware investment. On-premise is the right model for teams with strict data residency requirements, regulated industries, or existing on-site infrastructure that must be used. Hybrid fits teams that need cloud flexibility with selective on-premise control.

On-premise is not obsolete. For teams in healthcare, financial services, or regulated sectors where call recordings and customer data must remain within specific geographic or infrastructure boundaries, on-premise is the correct technical answer regardless of what cloud vendors claim about their security certifications. The evaluation question is not which model is better, it is which model matches the team's actual data residency, IT capacity, and go-live timeline requirements.

The most commonly underestimated variable in cloud CCaaS deployment is not the technical setup, it is the operational configuration. The platform is live in hours. The IVR structure, routing logic, skills assignments, CRM field mapping, and agent training take 1-2 weeks. Teams that treat "platform deployed" as "team ready" spend the first month fixing routing decisions that should have been made before go-live.

How do you match call centre software to your team's use case?

Use case fit determines which features must be in the core plan rather than available as add-ons. An inbound support team needs skills-based routing, queue management, helpdesk integration, and call recording. An outbound sales team needs a power dialer, CRM logging at the point of call, AI coaching, and talk time analytics. A blended team needs both without paying for two separate platforms or two separate pricing tiers.

Inbound call centre is a call centre software configuration designed to receive and route customer calls to the agent best equipped to resolve them, using skills-based routing, IVR, queue management, and helpdesk integration. The primary performance metric is first call resolution: how often a query is resolved without a callback or transfer.

Outbound call centre is a configuration of call centre software designed primarily to make calls at volume to prospects, customers, or leads. It includes power dialer, progressive dialer, voicemail drop, local presence dialling, CRM logging at call level, and AI coaching. The defining characteristic is that agents initiate calls and must maximise talk time per hour while maintaining CRM data quality across every interaction.

• Inbound support team: confirm skills-based routing, queue callback, helpdesk integration (Zendesk, Freshdesk), and call recording are all included at the base tier you are evaluating

• Outbound sales team: confirm power dialer, CRM logging at call level (not just contact level), AI call summaries, and talk time analytics are included at the tier you are signing

• Blended team: confirm the platform handles both inbound routing and outbound dialling without requiring two separate platform instances or two separate pricing tiers

• Remote or distributed team: confirm the softphone works on any device, routing logic applies regardless of agent location, and call data syncs to CRM without a VPN or on-site connection

• Regulated industry: confirm data residency, recording consent management, and compliance certifications (SOC 2, HIPAA, GDPR) before evaluating any features

The use case question to ask in every demo: "Show me how a [inbound support / outbound sales] team on the [base / standard] tier handles [the team's most common call type]." If the vendor cannot demo the use case at the tier being evaluated, that is signal about what gets discovered at implementation.

What CRM integration questions should you ask before signing?

CRM integration is the single most commonly misrepresented feature in call centre software evaluation. Every platform claims Salesforce, HubSpot, or Zendesk integration. The meaningful question is not whether the integration exists but what it logs, at which tier it is available, and whether it requires custom development or is available in native integration settings. These three questions eliminate more platforms from consideration than any other evaluation criterion.

CRM integration depth refers to what a call centre platform writes to the CRM automatically after every call: whether it logs only a timestamp, or the full interaction record including call duration, recording link, AI-generated summary, and outcome disposition to the correct CRM object, without any agent action required after the call ends.

Zendesk's CX Trends research confirms that 74% of customers feel frustrated when they have to repeat information to different agents. The CRM integration tier the team selects at signing determines whether the next agent sees the full interaction history before the call connects or asks the customer to start over.

CRM integration validation checklist

1. At which pricing tier is native integration with your specific CRM available: base plan, Pro, or Enterprise?

2. What data does the integration write to the CRM record after a call: only call timestamp, or also duration, outcome, recording link, and AI summary?

3. Is the integration bidirectional: does the call screen show CRM data before the agent picks up, or does data only flow one way after the call?

4. Does the integration require a custom API build, middleware, or a third-party connector, or is it a native, one-click setup available in the dashboard?

5. If the CRM requires field mapping to capture call outcome data, can that be configured in the call centre platform's settings, or does it require developer involvement?

A platform like Aircall resolves questions 1 and 4 specifically: native CRM integration with Salesforce, HubSpot, Zendesk and 250+ other tools is available at the base tier without a custom API project. A support manager who discovers during a demo that HubSpot integration requires the Enterprise plan has found the single most common contract regret in call centre software procurement, before signing, not after.

What AI features actually matter and which are add-ons?

AI call centre features split into two groups: features genuinely included in the base plan and features that appear in platform marketing but require a tier upgrade or separate add-on contract. The evaluation question is not whether a platform has AI, every major platform claims AI in 2026, but which AI capabilities are available at the pricing tier being evaluated, and whether they require an additional procurement process before the team can use them.

IBM's customer service trends analysis confirms that 51% of service leaders now prioritise revenue generation alongside cost reduction, and that consumers increasingly expect fast, AI-powered resolutions. AI is no longer a premium feature, it is a baseline expectation that the pricing tier determines whether the team actually receives.

Five AI capabilities to evaluate and where to probe in the demo:

• AI call summaries, automatically generated text summaries of each call, written to the CRM record when the call ends. Ask: "Is this included in the [base/standard] tier, or does it require a separate AI add-on?" If the answer is add-on, add the cost to the TCO model.

• Real-time transcription, live text of the conversation as it happens. Ask: "Is transcription live during the call, or only available as a post-call transcript?" The difference matters for supervisor monitoring and live coaching.

• Sentiment analysis, flags customer frustration or positive signals during calls for supervisor alerts. Ask: "At which tier does sentiment analysis appear, and does it require a separate conversation intelligence add-on?"

• AI call scoring, evaluates every call against a defined rubric without manual review. Ask: "Is call scoring automated or manual? At which tier is automated scoring available?" Most platforms gate automated scoring to their highest tier or a conversation intelligence add-on.

• AI routing, uses historical call data to match callers to the best available agent. Ask: "Does the routing engine use AI to predict agent fit, or is routing skills-based only?" AI routing is increasingly available at base plan level in modern CCaaS.

Aircall publishes exactly which AI capabilities sit in the core plan and which are add-ons. The full AI feature tier structure is a useful reference point when building the AI evaluation checklist for any vendor demo.

Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, leading to a 30% reduction in operational costs. The AI tier a team selects in 2026 has 24-36 month platform implications: a team that discovers AI scoring requires an enterprise add-on six months after signing faces either a budget conversation or a re-procurement process.

Annie Admin, a virtual assistant and admin services firm, described what the transcription clarity meant in practice after deploying. Erica Davis, Founder and CEO: "It's great because it spaces out the conversations and the pauses, and you know, what my team member said versus what the caller said." That separation, who said what, cleanly attributed, available after every call, is what structured call data produces for managers who need to coach without listening to every recording. 

What does the call centre software evaluation process actually look like?

A structured call centre software evaluation takes 3-6 weeks and follows five stages. The teams that complete all five before signing make a different decision from the teams that shortcut to the demo.

• Stage 1: Requirements definition (week 1). Document the team's use case (inbound, outbound, blended), the specific CRM that must integrate at the evaluated tier, the AI features required in the core plan, the budget ceiling and contract term limit, and the deployment timeline. Without this document, every demo is evaluated against different criteria by different stakeholders.

• Stage 2: Shortlist (week 1-2). Use the requirements document to eliminate platforms before requesting demos. A platform that gates Salesforce integration at Enterprise when the team is evaluating the Standard tier is eliminated from the shortlist, not from the demo shortlist. The shortlist should contain 3-5 platforms that pass all criteria at the tier being evaluated.

• Stage 3: Structured demo (weeks 2-3). Send the vendor a structured question list before the demo and ask them to demo against the team's questions, not their standard flow. Use the CRM integration validation checklist and AI feature evaluation questions from above. If a vendor cannot answer the CRM integration questions during the demo, that is a signal about what gets discovered during implementation.

• Stage 4: Trial on live calls (weeks 3-4). Validate CRM integration on real calls: make five calls, check that all five CRM records updated correctly with the expected fields. Validate AI features: confirm summaries are generated, check where they appear in the CRM record, confirm scoring runs without manual input. Routing logic: confirm that call routing behaves as configured, especially for overflow and out-of-hours scenarios.

• Stage 5: Total cost comparison (weeks 4-6). Build a month-12 cost model for each shortlisted platform: base per-seat rate plus usage fees (minutes, SMS, AI tokens), integration costs (any middleware or connector subscriptions), and professional services for advanced configuration. A platform quoted at $X per seat often reaches $2-3X at month 12 depending on usage patterns and which add-ons the team actually uses. Compare month-12 cost, not month-1 quote.

A comparison of the top call centre phone systems for 2026 provides a structured platform review for teams ready to apply the framework to a specific shortlist.

Frequently asked questions

What is call centre software?

Call centre software manages how a business makes and receives calls at scale: routing inbound calls to the right agent, recording every interaction, logging call data to CRM, and giving managers real-time visibility into queue performance and agent activity, including AI features that summarise and analyse calls.

What is the difference between call centre software and CCaaS?

Call centre software covers any platform that manages business calls. CCaaS (Contact Centre as a Service) is the cloud-hosted delivery model: no hardware, subscription pricing, browser or app access for agents. Most modern call centre software is delivered as CCaaS, but not all platforms are cloud-native.

How long does it take to implement call centre software?

Cloud CCaaS platforms deploy in 1-3 days for technical setup. Operational readiness, IVR configured, routing logic defined, CRM integration validated, takes an additional 1-2 weeks. On-premise call centre software typically requires 3-6 months including hardware procurement, installation, and custom integration work.

What features should I prioritise when choosing call centre software?

Prioritise four things before features: deployment model, CRM integration at the tier being evaluated, whether AI is included or add-on, and total cost at your actual usage pattern. Feature tables look identical across platforms, these four criteria reveal the differences that matter at implementation and month 12.

Is AI included in call centre software or is it a separate add-on?

It depends on platform and tier. Some cloud platforms include AI call summaries, transcription, and call scoring in the base plan. Others gate AI at higher tiers or sell it separately. Always confirm which AI capabilities are included at the tier being evaluated, not across all tiers.

What is the best AI call centre software for SMBs?

For SMBs, the best AI call centre software includes call summaries, CRM integration, and routing in the core plan without enterprise procurement overhead. Aircall powers AI-driven customer conversations at enterprise scale with cloud deployment, transparent pricing, and AI features available at the base tier rather than gated behind an add-on contract.

What we are

The four questions that power a structured call centre software decision

Every call centre software evaluation comes down to four questions. The teams that ask them before the demo make a different decision from the teams that discover the answers after signing.

• What deployment model fits the team's data residency, IT capacity, and timeline requirements? Cloud CCaaS for speed and flexibility. On-premise for full data control. Hybrid for both.

• Which use case is the platform being evaluated for, inbound support, outbound sales, or blended, and are the features that use case requires included at the tier being evaluated? Not at the next tier up.

• At which pricing tier does native CRM integration become available, and what data does it log to the CRM record after every call? Timestamp only, or full interaction record including AI summary and outcome disposition.

• Which AI capabilities are included in the core plan, which require a tier upgrade, and which require a separate add-on contract? Confirmed at the specific tier being evaluated, not across all tiers in the vendor's catalog.

A team that has answered all four before a demo has a structured evaluation. The demo confirms what is already expected. The contract reflects what was validated.

See how Aircall is built as a cloud call centre platform for sales and support teams for a reference point on what a platform that passes all four questions at the base tier looks like in practice.

The phone system is live. The integration is not working the way it was sold. Aircall reduces customer support response times with smart routing and AI so the agent who picks up already has the ticket history, the account context, and the last call summary visible before saying hello. This guide gives support leads the five criteria and the specific questions that separate platforms that improve support outcomes from those that simply add a calling channel.

Key takeaways

• Most support phone system demos look identical: routing depth, integration tier, and QA tools reveal the real differences

• FCR is the metric most correlated with CSAT: routing and helpdesk integration are the features that directly determine it

• AI summaries, QA tools, and routing depth are often gated at higher tiers: evaluate what you will actually sign, not what is listed

• Total 12-month cost is often higher than the base per-seat rate once AI, QA, and integration add-ons are factored in

What makes a phone system right for a customer support team?

A phone system is right for a customer support team when it does four things that generic VoIP cannot: routes calls to the right agent based on skills and context rather than just availability, surfaces the customer's ticket history before the agent answers, logs every call automatically to the helpdesk without manual entry, and gives managers quality monitoring data across 100% of interactions rather than the 2-3% they have time to sample manually.

First call resolution (FCR) is the percentage of support calls resolved on the first contact without a callback, transfer, or escalation, meaning the customer does not need to call back or be escalated to a different team. It is the metric most strongly correlated with CSAT in support operations: every percentage point improvement in FCR produces a measurable CSAT improvement. A phone system improves FCR when it routes the right agent to the right call and surfaces full ticket context before they answer.

Zendesk's call centre metrics research confirms that FCR is the primary indicator of support operation quality, and that every percentage point improvement in FCR produces a measurable improvement in CSAT. Routing capability and integration depth are not advanced features. They are the baseline requirements that determine whether the phone system produces better support outcomes.

What are the five criteria that distinguish customer support phone systems?

Five criteria separate support phone systems that improve team performance from those that simply add a calling channel: routing capability, helpdesk integration depth, AI feature availability, quality monitoring tools, and total cost at 12 months. Every platform on a shortlist will claim all five. The evaluation questions below reveal what each platform actually delivers at the tier being considered.

ICMI's contact centre trends research confirms that AI-powered routing, helpdesk integration, and quality monitoring automation are the top three investment priorities for contact centre leaders. The evaluation framework below is grounded in the same criteria that high-performing support operations are actively investing in.

1. Routing capability

Definition: The ability to direct each inbound call to the agent best matched to that customer's specific need , based on skills, language, account tier, issue type, or previous interaction history , without supervisor manual intervention for every queue change.

Demo question: "Show me how a queue rule is configured for skills-based routing. Then show me how a manager changes an overflow rule when volume spikes , without raising a support ticket."

What good looks like: The vendor demonstrates skills-based routing configured in the dashboard, shows a queue overflow rule that triggers after a defined wait time, and a manager changes a routing rule live without requiring vendor support. Aircall is built as a cloud phone system for customer support teams with skills-based routing, overflow rules, and after-hours flows configurable in the dashboard.

What poor looks like: The vendor says "yes, we have skills-based routing" but cannot demonstrate it in real time, or confirms that routing rule changes require a professional services engagement or vendor support ticket.

Skills-based routing is a call distribution method that connects each inbound call to the agent whose skill profile most closely matches the customer's requirement , rather than routing to the first available agent regardless of fit. It reduces misroutes, transfer rates, and handle time by ensuring the right agent handles the right type of call from the first connection.

2. Helpdesk integration depth

Definition: The completeness of data that flows automatically between the phone system and the helpdesk after every call, and whether the customer's ticket history is surfaced to the agent before they answer.

Demo question: "Show me the helpdesk ticket that is created or updated 60 seconds after a call ends. Confirm: call outcome, AI summary, recording link, duration, and agent disposition are all present. Then show me the screen that opens when an inbound call connects."

What good looks like: The helpdesk ticket updates automatically with all five data fields. The agent's screen shows the customer's open tickets and previous call history before they answer, surfaced from the helpdesk without the agent switching tabs. How Aircall integrates natively with Zendesk, Freshdesk, HubSpot, and other helpdesk tools shows what complete bi-directional integration looks like in practice.

What poor looks like: The ticket shows a timestamp and call duration only. The agent has to open the helpdesk manually during the call and search for the account. The integration is listed as available but requires an enterprise tier or a third-party connector.

Helpdesk integration is the connection between the phone system and the team's ticketing or customer service platform , determining whether agents have ticket context before calls and whether managers have complete call data in tickets after calls. A native integration writes call data directly when the call ends; a connector-based integration syncs on a delay and often omits outcome data.

3. AI feature availability

Definition: Whether AI call summaries, transcription, sentiment analysis, and real-time coaching are included in the plan being evaluated or require a tier upgrade or separate add-on purchase.

Demo question: "Confirm which AI features are included at the [Essentials / Standard / base] tier. Specifically: are AI call summaries, sentiment flags, and real-time coaching prompts in the plan we would sign, or do any of them require an add-on?"

What good looks like: AI call summaries, transcription, and sentiment analysis are confirmed as core plan inclusions at the tier being evaluated. The vendor demonstrates an AI summary appearing in the helpdesk ticket after a demo call, without enabling any add-on. AI call summaries, sentiment analysis, and real-time coaching available in Aircall core plans shows what plan-inclusive AI looks like.

What poor looks like: The vendor's sales materials feature AI prominently, but during the demo confirms that AI summaries require the Professional plan, and real-time coaching requires an AI Assist Pro add-on. The team signs the base plan and discovers after go-live that the AI features they planned to use are not included.

Average handle time (AHT) is the average duration of a support call from connection to completion, including any post-call work. It measures efficiency but not resolution quality. A short call with the wrong answer is worse than a longer call with the right one. A phone system with AI summaries reduces AHT by eliminating manual after-call logging, not by rushing agents through conversations.

4. Quality monitoring tools

Definition: The set of capabilities that allow managers to observe, coach, and score agent performance across live and recorded calls , including live listening, whisper coaching, call scoring, and AI-generated performance signals.

Demo question: "Show me a manager monitoring a live call. Then show me the AI call score for that call. Confirm these tools are available at the tier being evaluated , not the tier above."

What good looks like: The manager joins a live call silently, sends a whisper message the agent hears but the customer does not, and after the call accesses an AI-generated score against a defined rubric. All of this is demonstrated at the base or standard plan, not a premium tier.

What poor looks like: Live monitoring is listed as a feature but requires the Enterprise plan. Whisper coaching is available but call scoring requires a conversation intelligence add-on priced separately. The support manager who planned to use these tools for weekly coaching discovers they double the per-seat cost.

Call quality monitoring is the process of observing, recording, and evaluating support calls to assess agent performance, identify coaching opportunities, and ensure consistent customer experience. Effective quality monitoring requires both the technical capability to access calls in real time and the AI tools to generate structured performance signals across 100% of calls , not just the sample a manager has time to review manually.

5. Total cost at 12 months

Definition: The actual monthly cost at the team's seat count and usage profile, including base per-seat rate, usage fees, integration costs, and any AI or QA add-ons the team needs to run the support operation.

Demo question: "Give me the all-in monthly cost for [N] agents at [our usage profile] , base rate plus any AI features, QA tools, helpdesk integration, and usage fees. What does month 12 look like?"

What good looks like: The vendor provides a written cost breakdown at the team's actual seat count, with all required features included. The month-12 cost is within 20% of the quoted base plan rate. No feature the team needs daily is missing from the cost model.

What poor looks like: The base plan is £25/agent/month. After adding AI summaries (£8/agent add-on), conversation intelligence (£12/agent add-on), and Zendesk integration (Enterprise plan required at £45/agent), the actual monthly cost is three times the quoted rate. The team discovers this after signing an annual contract.

What integration questions should you ask before signing?

Three integration questions determine whether a support phone system actually improves agent performance or just adds another tool to the stack. Does the helpdesk screen open automatically when a call connects, or does the agent navigate there manually? What data does the integration write to the ticket when the call ends? At which tier is this integration available?

1. Which helpdesk integrations are native versus connector-based, and at which tier is the integration for your helpdesk available?

2. Does the customer's ticket history appear on the agent's screen automatically when a call connects, or does the agent open the helpdesk manually?

3. What data does the integration write to the ticket when a call ends: confirm call outcome, AI summary, recording link, duration, and disposition

4. Is the integration bi-directional: can agents create or update tickets from the phone interface without switching tabs?

5. If a call is transferred, does the receiving agent also get the helpdesk context, or does context transfer only on the first inbound call?

How do you evaluate a support phone system in a demo or trial?

The most effective demo for a support phone system is not the vendor's default demo flow. It is a structured evaluation where the vendor demonstrates the platform against the team's actual workflow. How to build a phone-first customer support operation that connects to your helpdesk and CRM covers the broader infrastructure context that a support phone system sits within.

1. Routing demonstration: show a call entering the queue, routing to the correct agent based on a skills rule, and overflowing after a defined wait time

2. Context demonstration: show the helpdesk ticket and customer history that appears on the agent's screen before they answer a simulated inbound call

3. Post-call data: show the helpdesk ticket immediately after a call ends , confirm outcome, AI summary, recording link, and disposition are all present

4. Manager monitoring: show a manager listening live to an active call, then sending a whisper message the agent hears but the customer does not

5. AI features: confirm AI call summaries, sentiment analysis, and call scoring are included at the base plan tier being evaluated, not at the next tier

6. Integration test: request a live demo using your helpdesk rather than the vendor's demo account , this surfaces integration gaps that a polished demo environment hides

What should you expect in the first 30 days with a new support phone system?

The first 30 days with a customer support phone system are an operational setup period, not a performance improvement period. By day 30, the team should have routing rules configured and validated, helpdesk integration confirmed on live calls, agents onboarded, and managers using the quality monitoring dashboard for at least one structured coaching conversation per agent. Performance improvements , FCR, AHT, CSAT , become measurable in months 2 and 3 as routing quality and coaching consistency compound.

ICMI's research on evolving contact centre metrics confirms that contact centres still measuring primarily on AHT consistently underperform on customer satisfaction, and that the metrics predicting CX quality are FCR, context availability, and resolution accuracy. The 30-day setup period is where those metrics find their baseline.

1. Routing configured: skills matrix defined, queue rules active, overflow and after-hours flows tested

2. Helpdesk integration validated: confirmed on 10-15 live calls that ticket data is writing correctly to the right fields

3. Agent onboarding complete: every agent has made and received calls, understands how to log disposition, and knows how to access AI summaries

4. Manager dashboard active: at least one manager has reviewed live call monitoring, accessed an AI call summary, and conducted one feedback session grounded in call data

5. Baseline metrics captured: FCR, AHT, queue abandonment rate, and CSAT documented before any coaching intervention so improvements are measurable in months 2-3

Frequently asked questions

What should I look for in a customer support phone system?

Prioritise five criteria: skills-based routing that handles your queue structure, helpdesk integration at the tier being evaluated, AI call summaries in the base plan, quality monitoring tools for managers, and total 12-month cost including add-ons. Feature tables look identical across platforms, these five criteria reveal the real differences.

How does a customer support phone system integrate with a helpdesk?

A native integration writes call data to the helpdesk ticket automatically when the call ends: outcome, duration, AI summary, and recording link. It also surfaces customer ticket history before the agent answers. Confirm both data directions and which tier the integration is available at before signing.

What is first call resolution and why does it matter for phone system selection?

First call resolution is the percentage of support calls resolved without a callback or escalation. It is the metric most correlated with CSAT. A phone system improves FCR when it routes calls to the right agent on the first attempt and gives that agent full context before they pick up.

How much does a customer support phone system cost?

Base plans typically range from $25 to $50 per agent per month. Total 12-month cost depends on which AI, QA, and integration features are included versus sold separately. Calculate cost against the full feature set your team needs, not the base plan rate , these numbers are often significantly different.

Can a customer support team set up a phone system without IT support?

Yes, with a cloud-based system. Most cloud support phone systems support self-service setup: create accounts, download the softphone app, configure routing and IVR in the dashboard, and connect the helpdesk. A team of 5 to 10 agents can be live in under a day with no hardware or IT support.

What is the best phone system for customer support?

The best phone system for customer support reduces response times with skills-based routing, surfaces ticket history before every call, and logs call data to the helpdesk automatically. Aircall reduces customer support response times with smart routing and AI, with native helpdesk integrations and AI summaries in the core plan.

What we are

The three questions that reduce the shortlist before the first demo

Every support phone system evaluation comes down to three questions the feature comparison table will not answer. The teams that ask them before the demo make a different decision from the teams that discover the answers after the contract is signed.

Does the routing capability match the team's queue complexity? Not "does it have skills-based routing" , "can it route by skill, availability, priority tier, and language simultaneously, and can a manager change routing rules without a support ticket to the vendor?"

What does the helpdesk ticket look like 60 seconds after a call ends? Not "does it integrate with Zendesk" , "what specific fields are written, at which tier, and does the agent have to do anything for it to happen?"

What is the total monthly cost at the team's seat count with the AI and QA features needed daily? Not the base plan rate , the rate with every required feature included.

A team that has answered all three before evaluating vendors has an evaluation framework, not a sales process. The demo confirms what is already expected. The contract reflects what was validated.

The email exists. The phone line exists. But from the customer's perspective, the support operation does not know who they are. Aircall unifies voice, SMS, and WhatsApp into one omnichannel business inbox so the agent who picks up that call already has the email thread, the account history, and the previous chat visible before saying hello. Omnichannel customer service is not about how many ways customers can contact you. It is about whether the agent who answers any of those contacts knows who the customer is and what has already happened.

Key takeaways

• Omnichannel is not about having multiple channels: it is about whether customer context travels across all of them

• 60% of consumers report repeating themselves when agents lack channel history, that is the multichannel failure omnichannel solves

• Build sequence: identify channels customers already use, connect them to CRM, configure unified inbox, then measure context carry rate

• Phone remains the primary channel for complex issues: an omnichannel operation without voice as a first-class channel is incomplete

What is omnichannel customer service, and how does it differ from multichannel?

Omnichannel customer service is a service model where customer context, the complete history of every previous interaction across every channel, travels with the customer into every new contact. Multichannel customer service offers multiple support channels but operates them independently, so customer history does not transfer between them. The distinction is not the number of channels. It is whether the agent answering any channel has access to everything that happened on every other channel before responding.

Omnichannel customer service is a support model in which every channel a customer uses to contact a business is connected through a shared data layer, so every agent on every channel has full context of the customer's previous interactions before the conversation begins. The operational test: if an agent on a phone call can see the email the customer sent yesterday, the operation is omnichannel.

Peer-reviewed research across 775 omnichannel customers confirms that consistency, context sensitivity, and connectivity across touchpoints are the primary drivers of omnichannel customer experience quality. Omnichannel is not a technology aspiration. It is a response to documented customer behavior: customers use multiple channels and expect each one to know what happened on the others.

Zendesk's omnichannel research confirms that 60% of consumers report having to repeat themselves when agents lack context from previous interactions, and that 71% expect companies to share information internally so they do not have to. That 60% figure is not a theoretical problem. It is the lived experience of customers whose support operation is multichannel rather than omnichannel, and the operational difference between omnichannel and multichannel customer service in practice makes clear why adding channels without connecting them produces a worse customer experience than fewer, better-connected channels.

Multichannel customer service is a support model where a business offers customers multiple ways to make contact, phone, email, chat, social media, but those channels operate independently of each other, without sharing customer history or context. Each channel has its own workflow, and agents on one channel cannot see what happened on another. Multichannel is the necessary first step. Omnichannel is what makes it useful.

Which channels belong in an omnichannel customer service stack?

The channels that belong in an omnichannel operation are the ones your customers already use to contact you, not the channels you want to be present on. For most B2B and SaaS support operations: phone for complex, high-stakes issues; email for non-urgent queries; live chat for quick clarifications; and SMS or WhatsApp for follow-ups and confirmations. Adding channels without connecting them to the CRM produces a wider multichannel operation, not an omnichannel one.

Channel selection must follow customer behavior data, not channel availability. Review the last 90 days of inbound contact volume by channel. The top three by volume are the ones to connect to the CRM first. Evaluating and prioritising which customer service channels belong in your omnichannel stack is the process that prevents the most common omnichannel mistake: adding channels because they are available, not because customers are using them.

PwC's Voice of the Consumer 2024 research confirms that 42% of consumers still prefer phone or in-store for their most important interactions, with 34% preferring smartphone channels. An omnichannel operation that treats voice as an add-on rather than a first-class channel is missing the channel customers choose when the stakes are highest. A platform like Aircall handles voice, SMS, and WhatsApp in one interface, connecting those channels to CRM and support tools natively so the context from a customer's WhatsApp message is visible when they call 10 minutes later.

What does an omnichannel operation require to build?

A true omnichannel operation requires three components: a CRM that stores all customer interactions regardless of which channel they came through, a unified agent interface that surfaces that CRM history before every interaction on every channel, and integrations that write every contact back to the CRM automatically when the conversation ends. Without all three, channels can exist in parallel but context cannot travel between them.

Unified agent inbox is a single interface where agents handle all channel conversations, phone, email, chat, SMS, from one screen, with the CRM contact record surfaced automatically before each interaction. The unified inbox is what makes omnichannel operationally viable: without it, agents switch between tools for different channels and the context that exists in the CRM is not visible at the moment it is needed.

The six-step build sequence:

• Step 1: Identify channels customers already use. Review 90 days of inbound contact volume by channel. The top three are the starting point. Everything else follows after those three are fully connected.

• Step 2: Connect those channels to the CRM. Every contact on every channel must write to the same CRM contact record. Not separate records by channel, one record per customer that every channel reads from and writes to.

• Step 3: Configure a unified agent interface. Every agent handles all channels from one screen, with the CRM record open before the interaction starts. This is the step that converts a technically omnichannel stack into an operationally omnichannel experience.

• Step 4: Set up routing rules that work across channels. The same agent who owns an account should be reachable on the phone if they handled the email. Skills-based routing and account ownership routing should apply across all channels, not just one.

• Step 5: Define the handoff protocol for channel escalations. When a chat conversation needs to become a phone call, three things must be defined: who initiates the switch, how context transfers (CRM note written, agent-to-agent message sent, or automatic transcript attached), and what the customer hears during the transition. This is the step most omnichannel operations skip, and the one that most commonly produces the repeat-yourself experience even in a technically connected stack.

• Step 6: Measure context carry rate before declaring the operation omnichannel. The readiness checklist below confirms what is in place. The measurement section confirms whether it is working.

Customer context is the complete record of every previous interaction a customer has had with a business across every channel: emails sent, chats initiated, calls made, tickets opened, and outcomes reached. In an omnichannel operation, customer context is stored in the CRM and surfaced to the agent before every new interaction on any channel. Without it, every interaction starts from zero regardless of what has happened before.

1. CRM connection: every channel writes contact history to the CRM automatically when the interaction ends, not manually, not optionally

2. Unified inbox: agents handle phone, email, chat, and messaging from one interface, with the CRM contact record surfaced before each interaction

3. Context on switch: when a customer escalates from chat to phone, the phone agent can see the chat transcript before answering

4. Channel routing: inbound contacts on any channel are routed to the right agent based on skills and account ownership, not just availability

5. Handoff protocol defined: the team has a written protocol for channel escalations that specifies how context transfers without the customer re-explaining the situation

6. Metrics tracked: context carry rate, cross-channel resolution rate, and channel escalation rate are measured and reviewed alongside CSAT and FCR

What omnichannel customer support means for support teams and how to build it covers the full implementation process with the specific technical and process decisions that determine whether the build produces a genuinely connected operation.

How do you measure whether an omnichannel operation is working?

Three metrics reveal whether an omnichannel operation is genuinely connected or simply multichannel with a unified inbox label. Context carry rate: the percentage of multi-channel contacts where the agent had full history before responding. Cross-channel resolution rate: the percentage of issues resolved within one connected journey without the customer needing to re-initiate. Channel escalation rate: the percentage of contacts that required switching channels to resolve, which signals where resolution is failing on the first channel attempted.

Channel consistency is the degree to which a customer receives the same information, experience, and quality of resolution regardless of which channel they use to contact the business. Low channel consistency, where phone agents give different answers from email agents, or where resolution times vary dramatically by channel, indicates that the omnichannel infrastructure exists but the processes running on top of it are not aligned.

Omnichannel performance metrics with benchmarks

Context carry rate, percentage of multi-channel contacts where the agent had full previous channel history before responding.

• 80%+: strong CRM integration; context is reaching agents consistently

• 50-79%: partial integration; some channels are writing to silos rather than the shared CRM

• Below 50%: channels are operating independently despite the unified inbox label

Cross-channel resolution rate, percentage of customer issues resolved within one connected journey without the customer needing to re-initiate contact.

• 70%+: effective context transfer; handoff protocol is working

• 50-69%: handoff protocol needs review; context is not consistently reaching the next agent

• Below 50%: the handoff protocol is failing; customers are restarting on every channel switch

Channel escalation rate, percentage of contacts that required switching channels to resolve.

• Below 15% on any single channel: that channel is resolving most issues it receives

• 15-25%: review whether the channel has the capability to resolve the issue types routed to it

• Above 25%: the channel is consistently failing to resolve; this is either a routing problem or a capability gap

Frequently asked questions

What is omnichannel customer service?

Omnichannel customer service is a service model where customer context, the history of every previous interaction across every channel, travels with the customer into every new contact. An agent answering a phone call can see the email and chat history before responding, without asking the customer to repeat.

What is the difference between omnichannel and multichannel customer service?

Multichannel means multiple support channels exist but operate independently. Omnichannel means those channels are connected through shared customer data. The test: can an agent on a phone call see a customer's previous email and chat history? If yes, omnichannel. If not, multichannel regardless of how many channels are offered.

What channels should be included in omnichannel customer service?

Start with the channels your customers already use. For most B2B support: phone for complex issues, email for non-urgent queries, live chat for quick clarifications, SMS or WhatsApp for follow-ups. Connect those to your CRM before adding more. Channel breadth without a CRM connection is multichannel, not omnichannel.

How do you measure omnichannel customer service performance?

Use three omnichannel-specific metrics: cross-channel resolution rate (issues resolved in one connected journey), context carry rate (interactions where the agent had full channel history), and channel escalation rate (contacts that required switching channels to resolve). These reveal operational gaps that CSAT and FCR alone do not surface.

What technology do you need for omnichannel customer service?

You need three things: a CRM storing all customer interactions regardless of channel, a unified agent interface that surfaces CRM history before every interaction, and integrations that write every contact back to the CRM automatically. The technology is the connective tissue, the strategy determines which channels to connect first.

What is the best omnichannel messaging platform for business?

The best omnichannel messaging platform for business unifies voice, SMS, and messaging in one agent interface with CRM context shared across all channels. Aircall unifies voice, SMS, and WhatsApp into one omnichannel business inbox, with routing and CRM sync built in so agents have full customer context before every interaction.

What we are

Is your operation truly omnichannel, or does it just unify the label?

The most reliable test is the agent test: can the agent who answers any channel contact see everything that has happened on every other channel before responding? If the answer is yes for 80%+ of contacts, the operation is omnichannel. If the answer is yes only for contacts on the same channel, the operation is multichannel, regardless of how many channels are active, how unified the inbox looks, or which vendor is providing the platform.

Most operations that describe themselves as omnichannel fail this test on phone calls. The phone is often the last channel to be integrated to the CRM rather than the first. It is also the channel where context matters most: it is where customers call when the email did not resolve the issue, and the agent who answers needs to know that before saying "how can I help you today?"

Building a true omnichannel operation does not require rebuilding the technology stack. It requires connecting the channels that already exist to a shared customer data layer, defining how context transfers when customers switch channels, and measuring the three metrics that reveal whether that context is actually reaching agents before interactions begin. The channels most teams already have. What most lack is the connective tissue.

The phone system is working. The CRM is not. The renewal is at risk not because the customer is unhappy but because the CS team has no structured call record the head of CS can act on. Aircall accelerates sales team call volume, connect rates, and revenue by making power dialer, AI call summaries, and CRM integration part of the core plan, not an upgrade the SaaS team discovers they need three months after signing.

Key takeaways

• SaaS companies have two distinct calling profiles: high-volume SDR outbound and high-stakes CSM calls, most phone systems serve only one well

• A power dialer, automatic CRM logging per call, and AI summaries are operational requirements for SaaS sales, not premium features

• CS team call data in the CRM determines renewal health visibility, a phone system without deep CRM integration makes churn invisible until it is too late

• Evaluate whether the platform that works at 5 seats also works at 50, a platform migration at Series A is a pipeline and retention risk

What makes a phone system right for SaaS sales and customer success teams?

A phone system is right for a SaaS company when it serves both calling profiles that drive SaaS revenue without requiring two separate platforms or enterprise pricing. For the sales team: a power dialer that eliminates manual dialing, automatic CRM logging per call, and AI coaching. For the CS team: account-context routing that surfaces ARR, renewal date, and last call summary before the CSM picks up, and an AI-generated call summary in the CRM record after every renewal and onboarding conversation.

Two operational scenarios that make the difference concrete.

1. The SDR scenario. A 10-person SDR team making 60 outbound calls per day each. Without a power dialer and automatic CRM logging, 20 minutes per rep per day goes on manual dialing and CRM entry. That is 200 minutes per day of non-selling time across the team. With both, the rep is in the next call before the previous AI summary has finished generating.

2. The CSM scenario. A 15-person CS team managing 300 accounts. Without account-context routing and AI summaries, every CSM starts every call by asking the customer to recap where they are with the product. With both, the CSM opens the call with: "I can see you've been using the onboarding playbook heavily since we last spoke. Is that working for the use case we discussed?" The customer experiences that call as informed. The standardised check-in, they do not.

Gainsight's Essential Guide to Customer Churn confirms that churn in SaaS can often be prevented with proactive customer success strategies including regular business reviews and strategic touchpoints. A phone system that does not write structured call data to the CRM after every CSM conversation is not a telephony gap. It is a retention infrastructure gap.

Novated Lease Australia, an operations-intensive financial services business, described the direction their team is moving with AI-powered calling. Kristy Lake, Chief Operating Officer: "AI tools are definitely the future of voice communication. They'll help us work smarter." The operational translation for a SaaS CS team: AI summaries after every renewal call mean the head of CS can assess account health from CRM data rather than asking every CSM for a manual status update.

How do you evaluate a phone system for a SaaS team?

Evaluate on five criteria in this order. Does the power dialer work at the base tier, or require a plan upgrade? What data does the CRM integration write to HubSpot or Salesforce after each call, timestamp only, or also outcome, AI summary, recording link, and disposition? Does CS team routing allow inbound calls to be directed to the account-owning CSM automatically? Are AI call summaries included in the core plan or sold as a conversation intelligence add-on? Does the platform handle both outbound sales volume and strategic CS calling in one interface?

Outreach's 2025 sales data analysis confirms that SaaS companies closing deals within 50 days achieve 2x the average market win rate (47% vs 21%), and that disconnected tools and drawn-out processes are the primary driver of extended sales cycles. A phone system with automatic CRM logging and a power dialer addresses the specific process overhead that lengthens SaaS sales cycles.

Power dialer is an outbound calling tool that automatically dials the next number the moment a rep ends the previous call, eliminating manual dialing between contacts. For SaaS SDR teams, this increases talk time from 15-20 minutes per hour to 40-50 minutes per hour, turning a 60-call day into productive conversation time rather than time spent dialing and waiting.

1. Power dialer: is it included at the base tier, or does it require a Pro or Enterprise upgrade?

2. CRM integration: what specific fields does the integration write to HubSpot or Salesforce after each call, confirm this before signing, not during onboarding

3. CS call routing: can inbound calls be routed to the CSM who owns the account, based on CRM account ownership data?

4. AI summaries: are AI call summaries included in the core plan or sold as a conversation intelligence add-on at a higher tier?

5. Two-team support: does the platform handle both high-volume outbound sales and strategic inbound CS calling without two separate instances or pricing tiers?

6. Scale readiness: does the platform that works at 5 seats also work at 50 seats without a migration?

Customer success call is a strategic, account-specific conversation between a CSM and a customer, covering product adoption, renewal intent, expansion opportunity, or escalation resolution. Unlike high-volume outbound sales calls, a customer success call requires the CSM to enter with full account context: ARR, renewal date, support history, and the summary of the last conversation, all surfaced from the CRM before the call connects.

What are the 5 best phone systems for SaaS companies in 2026?

Each platform below is evaluated against the two SaaS calling profiles: outbound sales and customer success. No platform is universally right for every SaaS company. The right fit depends on team composition, CRM stack, and which calling profile is primary.

1. Aircall

Aircall is a cloud phone system built for the two-team SaaS calling structure: the SDR team gets a power dialer and automatic CRM logging, the CS team gets account-context routing and AI-generated summaries on every renewal call, all within the same platform. Where Dialpad has 8 native integrations with Salesforce and HubSpot gated to higher tiers, Aircall's 250+ native integrations include HubSpot, Salesforce, Zendesk, and Intercom from the base tier. Power dialer is on the Essentials plan; AI Assist with call summaries is on Professional.

Best for: SaaS sales and customer success teams that need power dialer, account-context routing, and CRM integration in one platform, without two separate tools or enterprise contract terms.

Strengths:

• Account-context routing for the CS team: inbound calls can be directed to the account-owning CSM automatically based on CRM ownership data, so the customer reaches their CSM without a manual transfer and the CSM sees the account's renewal date, ARR, and last call summary before picking up

• 250+ native integrations from the base tier: Aircall integrates natively with HubSpot, Salesforce, Zendesk, and Intercom, writing call outcome, duration, AI summary, and recording link to the correct CRM record without any rep action after each call

• AI call summaries and conversation intelligence on the Professional plan give SDR managers and heads of CS a structured view of what happened on every call without listening to recordings, directly addressing the renewal health visibility problem described in the article's opening scenario

Limitations:

• Power dialer is on Essentials ($30/user/month annually) and AI Assist is on Professional ($50/user/month annually); a SaaS team that wants both profiles fully covered will be on the Professional plan, which is higher than Dialpad's $15 entry price for AI-only needs

• 3-user minimum on all plans; a 2-person founding sales team pays for 3 seats, which is the same floor as most platforms in this category but worth confirming against the seed-stage budget

2. Dialpad

Dialpad is an AI-first communications platform where Dialpad AI, live transcription, real-time coaching prompts, and post-call summaries, is included at the $15/user base plan, making it the lowest entry price for AI-inclusive calling in this list. For a SaaS startup whose primary friction is AI transcription being unavailable without an add-on, Dialpad solves that gap at a lower per-seat cost than Aircall's Professional tier. The trade-off is integration depth: 8 native integrations versus Aircall's 200+, with Salesforce and HubSpot gated to the Standard plan.

Best for: AI-first SaaS teams prioritising AI transcription and real-time coaching at the lowest per-seat entry price, where the CRM is not Salesforce and deep bidirectional sync is not a day-one requirement.

Strengths:

• Dialpad AI delivers live transcription, real-time prompts, and post-call summaries at $15/user, the most affordable AI-inclusive entry point in this comparison, directly relevant for seed-stage SaaS teams that cannot yet justify Aircall's Professional plan cost

• Unlimited SMS and strong mobile-first experience suited to distributed SaaS teams where reps are not always at a desk

• Published pricing and monthly contract options available, satisfying the evaluation criteria for transparency before a sales conversation

Limitations:

• 8 native integrations versus Aircall's 200+; Salesforce and HubSpot are gated to the Standard plan, meaning SaaS sales teams on Salesforce encounter the same tier-gating friction Dialpad's entry pricing initially obscures

• Power dialer is on the Standard plan, not the base plan; SaaS SDR teams that sign on the base plan for AI and discover the power dialer requires an upgrade face the same re-procurement dynamic the article's evaluation framework is designed to prevent

3. RingCentral

RingCentral is a full UCaaS platform bundling voice, video, messaging, and conferencing alongside calling, appealing to larger SaaS companies that want to consolidate every communication tool under one vendor. For SaaS companies at Series C and beyond, with IT support and a procurement team, RingCentral's breadth is a genuine advantage. For a Series A SaaS company of 30 people needing a phone system this week, the implementation overhead and quote-based pricing create the opposite of what the evaluation framework prioritises.

Best for: Larger SaaS companies (50+ seats) with IT support and a preference for consolidating voice, video, and messaging in one vendor, where implementation time is not a constraint.

Strengths:

• Full UCaaS capabilities, video meetings, team messaging, conferencing alongside calling, relevant for SaaS companies that want one vendor for every communication channel

• 300+ app integrations provide broad connectivity across the SaaS tool stack, more than most alternatives in this list

• Enterprise-grade compliance and security certifications relevant for SaaS companies in regulated verticals (fintech, healthtech, legaltech)

Limitations:

• No publicly listed pricing since 2023; quote-based model means SaaS teams must enter a sales process before knowing the total cost at their seat count and feature requirements, which conflicts directly with evaluation criterion one in the article's checklist

• Power dialer and AI features require higher tiers or add-ons; a SaaS team that signs RingCentral at the entry level and later needs power dialer for the SDR team faces a tier upgrade conversation mid-contract

4. Zoom Phone

Zoom Phone is the calling product within Zoom Workplace, built for teams already using Zoom for video meetings who want basic calling in the same interface. For SaaS companies where the phone is infrequently used and the primary need is a professional number within the Zoom ecosystem, it covers the requirement at a low per-seat cost. For SaaS SDR teams with outbound volume requirements or CS teams needing account-context routing, the limitations become operational constraints quickly: no power dialer at any tier, AI features require the full Workplace bundle, and CRM logging writes basic metadata only.

Best for: SaaS teams already on Zoom Workplace with light calling needs and no requirement for power dialing, deep CRM logging, or AI call summaries outside the Workplace bundle.

Strengths:

• Familiar Zoom interface for teams already using Zoom Meetings daily, no new platform learning curve for basic calling functionality

• Published per-seat pricing with monthly options; no enterprise sales process required for the standalone phone plan

• Easy add-on for teams that want calling capability without leaving the Zoom ecosystem

Limitations:

• No power dialer at any tier and no AI call summaries outside the Workplace bundle; SaaS SDR teams that evaluate Zoom Phone on per-seat cost and later discover both features are missing will face a platform migration at the point where outbound volume matters

• CRM integration logs basic call metadata only, call timestamp and duration, without AI summary, outcome disposition, or recording link, leaving both Salesforce and HubSpot records incomplete for the CS renewal health use case

5. OpenPhone

OpenPhone (rebranding as Quo) is a lightweight business phone product for small SaaS teams and founders who need a professional business number, basic shared inbox functionality, and simple CRM logging without the complexity or minimum seat requirements of platforms built for larger teams. Its positioning is accurate: best for a 2-5 person founding team that needs to step up from personal mobiles. For a SaaS team that has hired its first SDR or first CSM, OpenPhone bridges the gap between personal mobile and a purpose-built sales platform.

Best for: Early-stage SaaS startups (1-5 seats) that need a professional shared business number, basic HubSpot or Zapier logging, and same-day setup, with a plan to migrate to a more capable platform at Series A.

Strengths:

• No user minimum and simple flat-rate pricing starting at $15/user/month, accessible for a 2-person founding team that cannot meet the 3-user floor of platforms like Aircall

• Shared inbox and collaborative features let multiple team members see and respond to calls and messages from the same business number, the primary operational gap for teams currently using personal mobiles

• Fast self-service setup with no hardware or IT support required; teams consistently go live in under an hour

Limitations:

• No power dialer and limited CS routing capability; a SaaS company that hires its first SDR will outgrow OpenPhone within 6-12 months and face the platform migration cost and disruption the article's evaluation framework is designed to avoid

• CRM integration depth is limited to HubSpot, Slack, and Zapier connectors; Salesforce integration is not native, which creates incomplete call records for SaaS CS teams whose renewal health tracking depends on Salesforce deal and account data

What happens to call data when a SaaS company switches phone systems?

Switching phone systems for a SaaS team involves four data decisions: call recordings stored on the old platform, historical call logs in HubSpot or Salesforce, active routing rules for the CS team, and phone numbers being ported. None of these is blocking, but each needs a plan before the switch, not after the first CSM enters a renewal call on the new system and finds the previous call summaries are missing.

Call routing is the mechanism that determines which agent or team receives an inbound call based on predefined rules: time of day, caller ID, IVR selection, agent skills, or CRM data such as account ownership. For SaaS CS teams, routing by account ownership directs every customer call to the CSM who owns that account in HubSpot or Salesforce automatically.

1. Export call recordings from the old platform before cancellation: most providers delete recordings on account closure; download and archive anything managers or CSMs would need to reference post-switch

2. Audit what historical call data currently exists in HubSpot or Salesforce: if logging was incomplete, document the gap before go-live so the CS team knows where the structured record begins

3. Set up the new platform in parallel on temporary numbers: configure CRM integration, routing rules, and power dialer before porting existing numbers

4. Rebuild CS team routing rules: map account ownership from Salesforce or HubSpot to the new platform's routing logic so inbound calls reach the account-owning CSM from the first day on live numbers

5. Submit number porting requests: US porting takes 1-3 weeks; operate on temporary numbers during this period with no downtime

6. Validate CRM field mapping on 10-15 test calls before the full team goes live: confirm AI summary, call outcome, and recording link all appear in the correct CRM fields before removing the fallback

CRM integration depth refers to the range of data a phone system writes to the CRM automatically after every call: whether it logs a timestamp only, or the full interaction record including call duration, recording link, AI-generated summary, and outcome disposition to the correct CRM object without any agent action required.

Conversation intelligence is software that automatically records, transcribes, and analyses calls to extract structured insights: keyword flags, talk time ratios, objection frequency, and sentiment signals. For a SaaS CS team, it surfaces patterns across 100% of renewal conversations (which accounts expressed pricing concerns, which requested features) without the head of CS listening to individual recordings.

Building a customer support system that scales with a SaaS team covers the broader infrastructure decisions around routing, helpdesk integration, and call data governance for growing SaaS CS teams.

Frequently asked questions

What is the best phone system for SaaS companies?

The best phone system for a SaaS company handles outbound sales calling and customer success calling in one platform: power dialer and CRM logging for the SDR team, call routing with full account context for the CS team. The right choice depends on team composition and CRM stack.

Does a SaaS company need a power dialer?

Yes, if the team has SDRs or AEs making 30 or more outbound calls per day. A power dialer eliminates manual dialing and connects the next call automatically, letting reps spend more time in conversation and less time between calls. Without it, high-volume outbound calling is unsustainable at scale.

How does a phone system integrate with HubSpot or Salesforce for a SaaS team?

A native integration connects the phone system to HubSpot or Salesforce through a one-click setup in the platform settings. Every call logs automatically to the contact or deal record: call outcome, duration, AI summary, and recording link, without the rep manually updating the CRM after each call.

What phone features do customer success managers need?

CSMs need three things from a phone system: full account context on the call screen before every call, automatic CRM logging after every call so renewal health is visible without a manual update, and AI call summaries that capture what was agreed on every onboarding call, QBR, and renewal conversation.

When should a SaaS startup invest in a dedicated phone system?

From the first sales hire. A dedicated phone system gives a small sales team the same CRM logging, call recording, and routing infrastructure they will need at 50 seats, and avoids a platform migration at Series A when personal mobiles no longer work as calling infrastructure.

What is the best phone system for sales teams?

The best phone system for a sales team accelerates call volume and CRM data quality simultaneously: power dialer for outbound volume, automatic CRM logging per call, and AI summaries that tell the manager what happened on every call without listening to recordings. Aircall accelerates sales team call volume, connect rates, and revenue with those three capabilities in the core plan.

What we are

Matching the platform that accelerates both SaaS calling profiles

The five platforms reviewed here each fit a specific version of the SaaS phone system problem. None is the right choice for every SaaS company. The right choice matches both calling profiles the team actually has: not the one with the longest feature list or the lowest per-seat rate.

Before evaluating, answer three questions: Is the primary calling profile outbound sales, customer success, or both equally, the answer determines whether a power dialer, CS routing, or both are day-one requirements. Which CRM does the team use, and what data does the phone system need to write to it after every call, confirm this at the tier being evaluated. Does the team need one platform for both profiles, or can the two teams use different tools without creating CRM data fragmentation that compounds with headcount?

A SaaS team that answers all three before the demo has a structured evaluation. See how Aircall is built for SaaS sales and customer success calling workflows for a reference point on what a platform serving both profiles looks like in practice.