AI consulting use case

AI consulting for knowledge retrieval and RAG

Retrieval-grounded assistants that answer from your own knowledge — cited, current, and aware of who's allowed to see what.

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The problem

Why knowledge retrieval (rag) is hard to get right

Your answers exist — in wikis, tickets, contracts, and the heads of a few senior people — but finding them takes a search expedition or a Slack interruption. Naive RAG demos well and fails in production: stale content, missing citations, and answers that ignore who's allowed to see a document. The challenge is retrieval that stays fresh, cites its sources, respects permissions, and is actually grounded rather than guessing.

How we build it

Retrieval that stays fresh

Ingestion and indexing tied to your live sources, so the assistant answers from the current document, not last quarter's copy.

Citations and grounding checks

Every answer carries source links, and grounding evals catch the cases where the model would otherwise wander off-document.

Permission-aware access

Retrieval respects your access controls, so an answer never leaks content the asker isn't entitled to see.

Evaluation against real questions

Eval sets built from the questions your people actually ask, run on every change so quality is measured, not assumed.

The outcome

People get a cited, current answer in seconds instead of a search expedition — grounded in the right documents, scoped to what each person is allowed to see.

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RelatedData & Platform Engineering

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Key concepts

Glossaryretrieval-augmented generation (RAG)Retrieval-augmented generation (RAG) is a pattern that fetches relevant documents at query time and feeds them to a language model, so its answers are grounded in your own data rather than only its training.

GlossaryembeddingsEmbeddings are numerical vectors that capture the meaning of text or other data, so that items with similar meaning sit close together in the vector space.

Glossaryvector databaseA vector database stores embeddings and quickly finds the ones most similar to a query — the retrieval engine that powers semantic search and RAG.

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Put AI to work on knowledge retrieval (rag)

Bring us the metric you need to move. We will tell you what we would build, how long it takes, and what it is worth.

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