AI knowledge assistant for a support team

A support team needed accurate answers without searching scattered documents. We built an assistant that answers only from approved sources, shows citations, and hands off to a human when unsure.

Project summary

Client
Anonymous support organisation
Location
Remote collaboration (client region on request)
Timeline
Multi-phase engagement
Services
AI Chatbot & Knowledge Assistant Development · Custom Web Application Development
Team
Product, engineering and QA contributors aligned to the support workflow

The challenge

  • Repeat questions consumed specialist time each week.
  • Policies and procedures lived across folders, tickets and chat history.
  • First responses were slow when agents searched multiple tools.
  • Generic chat tools risked invented answers when information was missing.

What we built

  1. Approved-sources-only retrieval with citations

    Answers are grounded in documents the client marked as authoritative. The interface surfaces source references so agents can verify before sending.

  2. Clear boundaries instead of guessing

    When evidence is weak or absent, the assistant states that it does not know, collects missing details and escalates rather than fabricating an answer.

  3. Human handoff with full context

    Escalations include the question, retrieved sources and conversation history so the next agent does not restart from zero.

  4. Acceptance testing before launch

    We agreed a test set of thirty to fifty real support questions with pass criteria for accuracy, tone and escalation behaviour.

  5. Post-launch monitoring

    Flagged answers and user feedback feed a review queue so the team can adjust sources, prompts and boundaries after go-live.

Deliverables

  • Knowledge-source inventory and permissions model
  • Retrieval pipeline connected to approved documents
  • Support-facing assistant interface
  • Escalation workflow with context payload
  • Evaluation question set and launch checklist
  • Monitoring views for flagged responses

Technology

  • Model APIs
  • Retrieval over approved documents
  • React
  • Python
  • Support platform APIs

How the work ran

Discover

Mapped support workflows, document locations, escalation rules and what “good” answers look like for the team.

Design & prototype

Prototyped retrieval and response patterns on representative questions before committing to full integration.

Build & integrate

Connected document sources, the assistant UI and handoff paths into the support stack the client already used.

Test & launch

Ran the agreed question set, fixed gaps in sources and boundaries, then launched with monitoring enabled.

Results

The first release is live. We are measuring answer quality, escalation rates and time-to-first-response over a ninety-day window before publishing outcome metrics.

What we would do next

  • Expand approved sources as new product documentation is released.
  • Tune escalation rules using flagged-answer reviews from the first production month.
  • Add authenticated lookups only where the client approves API access and audit requirements.

Lessons for a similar project

  • Start with a bounded question set—not every support topic on day one.
  • Treat missing information as a first-class outcome with a defined handoff.
  • Source freshness and ownership matter as much as model choice.

Related services

Related guide

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