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Use Cases

AI Customer Support

First-line answers grounded in your documentation, with citations and designed escalation.

Integrations

  • CRM
  • Telegram
  • Microsoft 365
  • Google Workspace
  • REST API

The task

A large share of support volume is questions with documented answers, asked repeatedly. Handling them consumes the capacity needed for the cases that actually require judgement, and response times drift upward as the customer base grows.

What it works from

  • Your published documentation and help articles
  • Internal policy documents marked as authoritative
  • Historical tickets and their accepted resolutions
  • Product and pricing data from your own systems

The automated workflow

  1. An incoming message is classified by topic, urgency and sentiment.
  2. Policy-restricted topics are routed straight to a person, before any answer is drafted.
  3. For everything else, relevant passages are retrieved from approved sources.
  4. An answer is drafted strictly from those passages, with citations.
  5. Confidence is assessed; below the threshold the draft goes to an agent instead of the customer.
  6. The reply is sent or queued for review according to the mode you chose.
  7. Unanswerable questions are logged as knowledge base gaps.

Where a human stays in control

  • Refunds, complaints, contractual and legal topics always reach a person — this is a hard rule, not a threshold.
  • A customer can reach a human at any point without arguing with the system.
  • Assist mode — where agents review every draft before sending — is the normal first deployment.
  • Support leads control which sources are authoritative, and stale content can be retired instantly.

Integrations

  • CRM

    Read and write deals, contacts and activities so qualified enquiries land where sales already works.

  • Telegram

    Bot-based notifications, approvals and conversational entry points for teams that already live there.

  • Microsoft 365

    Identity, mail, calendars and document libraries, with permissions respected rather than bypassed.

  • Google Workspace

    Sign-in, Drive, Sheets and Calendar as data sources and destinations for automated workflows.

  • REST API

    Documented, versioned and authenticated endpoints — both consuming yours and exposing ours.

Expected effect

Described qualitatively. Actual impact depends on your volumes, data quality and process discipline.

  • Documented questions get an immediate first response instead of waiting in a queue.
  • Agents spend their attention on cases that need judgement.
  • Answers become consistent, because they come from one approved set of sources.
  • Documentation gaps become visible as a prioritised list rather than as repeat tickets.

Limits and what this does not do

  • It can only be as good as your documentation. Thin or contradictory sources produce refusals, which is the correct behaviour but not a useful one.
  • It does not negotiate, make commercial concessions or agree to anything on your behalf.
  • Quality is measured per language. A locale with sparse documentation will visibly underperform.
  • It is not a replacement for a support team, and we will not scope it as one.

Part of the practice

AI Agents

Agents scoped to one workflow, integrated with your systems, always reviewable by a person.

See the practice

Related use cases

  • AI Lead Qualification

    Score and route incoming enquiries against your own criteria, with a written rationale attached.

  • Customer Portal

    Give clients their own data — documents, statuses, requests — instead of emailing it on request.

Next use case

Internal Knowledge Assistant

Let your team find the answer that already exists, with permissions respected and sources shown.