Your answers are in your documents. Nobody can find them.
We build retrieval-augmented generation (RAG) systems on your own material: SOPs, contracts, past quotes, support threads, product sheets and policies. Staff and customers ask in plain English and get an answer with the source cited, limited to what each person is allowed to see.
What it costs to leave it as it is.
Knowledge that lives in people
The one person who knows how a thing works is on vacation, or leaves. The answer is somewhere in a folder nobody can search.
Search that returns files, not answers
Keyword search gives you forty documents. A person still has to read them, decide which is current and work out what applies.
Generic AI that guesses
A chatbot trained on the open internet does not know your pricing, your policies or your exceptions, and it will invent them with confidence.
Four steps, run by an operator who has done it before.
With an engineering bench building in the background. Scope and timeline are written into your agreement before we start.
Inventory the sources
Where the knowledge lives today, drives, CRM notes, tickets, email, wikis and PDFs, and who may see what.
Index and structure
Clean, split and tag the material, with versions and dates so the system prefers what is current.
Build the retrieval
Search tuned to your content, answers written only from what was found, with the source shown beside every answer.
Test, permission, hand over
A test set of real questions, access rules per role, a way to flag a wrong answer and documentation your team can run.
The disciplines behind this build.
From the three phases every engagement runs. Tap a card for the diagram; each is explained in full in the playbook.