Service 02

RAG & knowledge systems

Your policies, contracts, manuals and tickets become answerable — with a citation on every sentence and permissions that respect who is asking.

What it includes

Ingestion that handles real documents

Scanned PDFs, spreadsheets, email threads, wiki pages and CAD annotations, normalised and de-duplicated on a schedule.

Every claim links back to the page and paragraph it came from, so reviewers verify instead of trusting.

Retrieval filters by role and record before ranking, so an answer can never quote a document the reader cannot open.

We build a question set from your real queries and report precision and recall on every release, not just at launch.

Superseded versions are retired automatically so the system stops quoting last year's policy.

Thumbs-down responses land in a review queue that feeds retrieval tuning and content gaps back to the owning team.

What you receive

Typical stack

Questions we are asked

Do our documents get used to train a model?: No. Retrieval reads your documents at question time. Nothing is used for provider training, and we can pin providers to zero-retention terms.
How accurate is it really?: We publish a number rather than a claim. On a typical policy or manual corpus we target 90% or better answer precision with citations, measured against a question set your own experts sign off.
What if a document is confidential to one team?: Permissions are applied during retrieval, not after generation. If the reader cannot open the file, the passage is never a candidate for the answer.
How many documents can it handle?: We run corpora from a few hundred pages to several million. Scale changes the indexing strategy and the cost, not the approach.
Can it answer in Hindi or other Indian languages?: Yes, including cross-lingual retrieval where the question is in one language and the source document in another.

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