In practice
AI knowledge search (RAG): what we build
Document ingestion
Contracts, PDFs, manuals, spreadsheets, tickets and shared drives processed and kept in sync as they change, not uploaded once and forgotten.
Cited answers
Answers grounded in the retrieved passages, with citations to the source, and an honest “not found” when the documents do not say.
Access control
Search that respects who is allowed to see what, so a question never surfaces a document the person asking could not open themselves.
Measured accuracy
A test set of real questions with known answers, run on every change, so accuracy is measured rather than assumed.
A good fit when
- The same questions go to the same few people over and over.
- New staff take months to learn where things are.
- Answers depend on long documents: contracts, policies, regulations, specifications.
- You tried a generic AI tool and could not trust where its answers came from.
How an engagement runs
One week mapping the real workflow with your team, exceptions included. Two to three weeks proving one workflow on your own documents and systems, against an evaluation set agreed up front. Then the production build — integrations, permissions, monitoring and review points — shipped behind your authentication, in your cloud, and handed over with documentation and training.
We work on site with teams in North Vancouver, West Vancouver and Downtown Vancouver.
Questions we are asked
What is RAG?
Retrieval-augmented generation: before the model answers, the system searches your documents for the relevant passages and gives the model only those to work from. The answer rests on your material rather than on what the model happens to remember, and it can cite its sources.
Will our documents be used to train a model?
No. Retrieval trains nothing: your documents are indexed in your own infrastructure and passed to the model only as context for a single question, through providers and settings that do not train on that data.
How accurate is it?
It is measured, not promised. Before we build, we agree a set of real questions with known answers; the system is scored against it on every change, and it is designed to say when the documents hold no answer rather than guess.
Can it handle scans and spreadsheets?
Yes. Scans are read with OCR, tables are kept as tables, and each document type gets the processing it needs. Poor-quality sources are flagged rather than silently dropped.
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