AI & your knowledge base

Answers connected
to your sources.

See how retrieval connects company documents to an AI answer, and how your team can check where the information came from.

Explore knowledge retrieval ↓Try the RAG lab ↗

AI & your knowledge base

Your company knowledge.
Within reach of your team.

Help people find answers across scattered documents, understand products and follow internal procedures. I connect AI to the knowledge your business already owns, inside the tools your team uses.

How the model works with your data

Retrieval-augmented generation (RAG) brings relevant material into the model’s context when someone asks a question. The documents remain in a separate knowledge layer; updating that layer does not require retraining the language model.

01 Prepare the knowledge

  1. Your sources

    Documents, product guides, procedures and internal wikis.

  2. Prepare & embed

    Split content into useful passages. An embedding model represents their meaning as vectors.

  3. Vector database

    Index vectors alongside passage text or references, source versions and access metadata.

02 Retrieve for each question

  1. Ask a question

    Embed the question with the same embedding model used for the documents.

  2. Find relevant evidence

    Search by similarity, combine with keywords, and filter for the user’s permissions before passing results on.

  3. Generate an answer

    Give the language model the question and selected passages. Return an answer with source references.

Vector search helps find passages with similar meaning even when the wording differs. Keyword search complements it for exact product codes, names and terminology.

Illustrative example · fictional documents

From a support question to a traceable answer

The question

“A customer received a damaged item. What do we need to arrange a replacement?”

Retrieved passages

[1] Returns guide · §3
Record the order number and a photo of the damage.

[2] Support procedure · §2
Create a replacement request for the support team to review.

The grounded answer

Collect the order number and a photo of the damaged item [1]. Then create a replacement request for support review [2].

In a working system, references open the original source passages.

Access follows the user

I design retrieval around document permissions and agreed hosting boundaries, so the model receives only content the user is allowed to access.

Knowledge stays maintainable

Changes, deletions and document versions need an update process. We define ownership and freshness requirements as part of the implementation.

Quality is measured

We test real questions, source support and missing information. If evidence is insufficient, the assistant should say so or ask for clarification. RAG can still produce errors.

Start with one knowledge source and one useful workflow.

A focused pilot can test an internal knowledge assistant, product support or onboarding. We agree how to evaluate answer quality, retrieval relevance, response time and cost before expanding.

Technical background: RAG & retrieval · Indexing & embeddings. The model and database are selected for your requirements.

Connect AI to your knowledge ↗