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How to build an AI contact centre

Short answer

Put an AI layer in front of your channels that understands each query, answers from your knowledge and systems, and hands over to a person with the full context when it should. People take the complex, sensitive and high-value conversations. Start with a few frequent intents on one channel, measure quality, then add intents, languages and channels.

Updated: · Thinkia

What AI does

  • Understands what the customer wants across chat, email, voice and messaging, in their language.
  • Answers from your documentation, FAQs and product data, and checks orders, bookings or accounts in your systems.
  • Completes simple transactions end to end when the rules allow it.
  • Detects when a person is needed and transfers with the conversation and context intact.
  • Tracks resolution, escalations and satisfaction so you can see where it works and where it fails.

What stays with people

  • Complaints, vulnerable customers and conversations with commercial or emotional weight.
  • Exceptions and decisions outside policy, such as goodwill gestures or disputes.
  • Deciding which intents the AI handles alone and how it should sound.
  • Reviewing samples of conversations and improving the knowledge the AI uses.

How to put it in place

  1. Map channels, queues and intents

    List your channels, queues, SLAs and escalation paths, and group recent contacts by intent. Note which intents are frequent, rule-based and answerable from data the AI can reach: those come first.

  2. Clean up the knowledge and connect the systems

    Gather the answers the AI must give from your FAQs, policies and product data, and fix what is outdated or contradictory. Connect the CRM, order or booking systems through APIs with the minimum permissions.

  3. Design resolution and handoff rules

    For each intent, decide whether the AI resolves, assists an agent or only routes. Define when it must hand over (low confidence, frustration, a sensitive topic, a customer asking for a person) and what context travels with it.

  4. Pilot on limited traffic with agent shadowing

    Open one channel and a handful of intents to a slice of real traffic while agents watch and correct. Track quality, containment and satisfaction daily and fix knowledge and rules before widening.

  5. Expand by intent, language and channel

    Add intents, languages and channels only when quality holds on the ones already live. Give the operations team runbooks so they can tune flows and content without depending on a vendor.

What you need

  • Contact history (transcripts, tickets, emails) to identify intents and test against.
  • An up-to-date knowledge base with a named owner for each area.
  • API access to the CRM, ticketing and the systems that hold orders, bookings or accounts.
  • Integration with your telephony and messaging platforms for voice and chat.
  • A contact centre lead who owns intents, handoff rules and quality reviews.

When it is not worth it

  • Low contact volume, or contacts that almost all need expert judgement.
  • When the knowledge base is missing or out of date and nobody will own it.
  • When the systems the answers depend on cannot be reached through APIs.

Risks and how to control them

  • Wrong or invented answers: ground replies in approved content and systems, and hand over when there is no source.
  • Customers trapped with a bot: make reaching a person easy and transfer the context so nobody repeats themselves.
  • Transparency: tell customers they are talking to an AI, as the EU AI Act requires for systems that interact with people, and review your obligations with your legal team.
  • Manipulation through the conversation (prompt injection) and data leaks: limit what the AI can do and see, and test it against misuse.
  • Quality that degrades quietly: sample conversations, watch escalation and satisfaction, and close the loop with agents.

How Thinkia does it

We document queues, SLAs, knowledge sources and escalation paths as the ground truth, then tune triage, grounded replies and handoff rules to your risk and brand standards. The pilot runs on limited traffic with agent shadowing, tracking quality, satisfaction and containment, and stays narrow until quality holds.

It runs on AI Contact Experience across chat, email, voice and messaging, with a human review layer and audit trail for regulated environments, and runbooks so your operations team can tune it without vendor lock-in.

The Thinkia solution behind this guide

Thinkia products involved

Frequently asked questions

Can AI replace contact centre agents?

It takes over repetitive, rule-based queries so agents can focus on complex and sensitive conversations. A good AI contact centre is designed around the handoff to people, not around removing them.

Does it work on the phone as well as chat?

Yes. Voice agents understand natural speech and can answer, transact or transfer, which replaces the menu trees of a traditional IVR. Voice needs extra care with latency, accents and noisy lines.

Do I have to tell customers they are talking to an AI?

In the EU, the AI Act requires that people are informed when they interact with an AI system unless it is obvious. Beyond the law, saying it openly builds trust. Check the details with your legal team.

How do I stop the AI from giving wrong answers?

Ground every reply in approved content and live system data, let it say it does not know, hand over when confidence is low and review samples of conversations regularly.

Which channels should we start with?

The channel with the most repetitive volume and the cleanest data, usually chat or email. Once quality holds there, the same knowledge and rules extend to voice and messaging.

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