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Iotron AI

Project scenario · Telecom

How we would build an agent-assist tool grounded in an operator's own knowledge that cites sources, respects permissions and hands off when uncertain.

Typical starting point: mobile and broadband operator. This scenario shows how we approach this class of problem. It does not describe a named client engagement, so no results are claimed.

Typical client
Mobile and broadband operator
Industry
Telecom
Built with
Iotron Assist · Retrieval-augmented generation · Vector and keyword hybrid search · Permission-aware retrieval
Measured against
4 agreed measures

01The challenge

Care agents searching across wikis, plan sheets and procedure documents while the customer waits, with answers that vary by agent.

Where we usually start

Knowledge lives in a wiki, PDFs, CRM notes and tariff tables that change every month. Some content is restricted by team or region, answers must be current on the day they are given, and the contact centre runs on a CRM the agents will not leave.

02What it is measured against

How we would know it worked.

Agreed with the business before any model is built, and reported against throughout — so success is defined by the operation, not by the model.

Measures this project is reported against
#Measure
01Answer accuracy and citation correctness on the evaluation set
02Share of questions the assistant correctly declines
03Agent search time and average handle time
04Agent adoption and answer ratings over the pilot

03The Iotron solution

What we build.

Iotron Assist inside the CRM: retrieval over approved sources with permission-aware search, answers that always cite the passage they came from, refusal when the sources do not cover the question, and an evaluation set that is re-run before every change.

How the project runs

  1. Weeks 1–2

    Agree approved sources and owners; collect real agent questions to build an evaluation set with expected answers.

  2. Weeks 3–5

    Ingestion and indexing with document permissions, retrieval tuning, and grounded answer generation with citations.

  3. Weeks 6–8

    Pilot with one care team inside the CRM; every answer rated by agents and failures added to the evaluation set.

  4. Weeks 9–10

    Guardrails for out-of-scope questions, content-freshness checks, and a release process gated on the evaluation results.

  5. Then

    Extend to more teams and add task actions (for example, raising a ticket) with human approval.

Technology

  • Iotron Assist
  • Retrieval-augmented generation
  • Vector and keyword hybrid search
  • Permission-aware retrieval
  • LLM (model-agnostic)
  • Evaluation harness
  • CRM integration

04Lessons from this kind of work

What we would tell the next team.

  • The evaluation set, built from real questions, is the most valuable artefact in the project.
  • Citations change behaviour: agents trust — and check — answers they can trace.
  • Stale content is the main cause of wrong answers; ownership of sources matters more than the model.
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