Retrieval and search

Enterprise Knowledge Assistant

A secure assistant that answers questions from your own documents, policies and internal data, with citations and access control that follows the user.

The problem

Organisational knowledge is spread across document stores, wikis, ticket histories and people's heads. New staff take months to become productive, and experienced staff spend their time answering the same questions.

Retrieval pipeline

  1. 1

    Source content

    Documents, wikis, ticket history

  2. 2

    Ingest and chunk

    Scheduled, with permissions captured

  3. 3

    Embed and index

    Vector plus keyword

  4. 4

    Retrieve

    Filtered to the asking user's access

  5. 5

    Ground and answer

    Model reads only those passages

  6. 6

    Cite and log

    Sources shown, quality measured

How a question becomes an answer grounded in your own content, with the permission filter applied before the model ever sees a passage.

How it works

  1. 1

    Content is ingested from document repositories, wikis and internal systems on a schedule, with permissions captured alongside the text.

  2. 2

    Documents are chunked and embedded into a vector index, with hybrid semantic and keyword retrieval so exact terms still match.

  3. 3

    A question retrieves the most relevant passages, filtered to what the asking user is permitted to see.

  4. 4

    The model answers from those passages and cites them, so an answer can be checked against its source.

  5. 5

    Queries and retrieval quality are logged, giving a measurable baseline that regression tests run against.

What you should expect

  • Answers found in seconds rather than by asking a colleague
  • Shorter onboarding for new staff
  • Institutional knowledge that survives staff turnover
  • An audit trail of what was asked and what was returned