Why not just fine-tune
Retrieval beats training for company knowledge.
Fine-tuning teaches a model a style or a task. It is a poor way to teach it facts, because facts change and retraining is slow and expensive.
With retrieval, updating the answer means updating the document. A revised HSE policy is live the moment it is indexed — no retraining, no redeployment. You also get the thing fine-tuning cannot give you: a citation, so a reviewer can check the source instead of trusting the output.
The pipeline
- Ingest — PDFs, drawings, email, ERP records, scans through OCR
- Chunk — split on meaning, not on character count
- Embed & index — vectors plus keyword search, not one alone
- Retrieve — filtered by the asker's actual permissions
- Re-rank — a second pass that puts the best passage first
- Generate — answer constrained to the retrieved passages
- Cite — every claim links to the document and page it came from
Where RAG projects fail
It is almost never the model.
Nearly every disappointing RAG deployment fails at one of these five points. We check each of them before writing any code.
Chunking that cuts meaning
Splitting a specification table in half produces two chunks that each answer nothing. Structure-aware chunking is the single highest-return fix.
Vector search alone
Embeddings miss exact identifiers — part codes, clause numbers, drawing references. Hybrid keyword plus vector retrieval catches what either misses.
Permission leakage
The most serious risk. If retrieval is not filtered by the asker's rights, the assistant becomes a way to read documents someone cannot open.
A stale index
Confidently quoting a superseded revision is worse than not answering. Re-indexing has to be automatic and observable.
No evaluation
Without a fixed question set scored on every change, quality drifts silently and nobody notices until a user complains.
No refusal path
An assistant that cannot say "that is not in the documents" will invent something. The refusal is a feature, not a gap.
What we deliver
Including the part most vendors skip.
Every RAG engagement ships with an evaluation harness: a set of real questions from your team with known-correct answers, scored automatically on every change to the index, the prompt or the model.
It is the only way to answer "did that change make it better?" with something other than an opinion — and it is what lets you switch models later without gambling.
- Permission-aware retrieval wired to your existing directory groups
- Citations to document, revision and page on every answer
- Automatic re-indexing with freshness monitoring
- A scored regression suite you own and can run yourselves
- Data residency in India or the UAE, agreed in writing
- An explicit refusal path when the answer is not in your corpus
Next step
Sitting on documents nobody can find anything in?
Tell us roughly how many, in what formats, and who is allowed to see what. That is enough for us to scope it.
Start the conversation →