Situations
Connecting AI to your company's internal data
A generic model answers beside the point because it knows neither your products, nor your procedures, nor your clients. It needs access to what you hold, without shipping your whole document estate to a third party.
What we observe
- Answers are plausible but wrong on the details that matter
- The team copies and pastes document extracts into a chat, by hand
- The documentation exists but is scattered across Drive, a CRM and an intranet
- Someone offered you fine-tuning when the problem is facts that keep changing
What usually blocks
In the large majority of cases the right answer is a RAG rather than fine-tuning. Facts change, and retraining a model on every procedure update costs a lot for a less controllable result.
The classic mistake is polishing generation before retrieval. A system that answers well on badly retrieved documents still answers beside the point, and no amount of prompt work fixes that.
What we do
- 01We delimit the corpus and above all the access rights, before any indexing
- 02We measure retrieval on its own: does the right passage come back in the top results
- 03We require citations in answers, so every claim can be checked
- 04We cap cost per request and watch latency at real volume
The proof
Mid case: realistic autonomous resolution of 40 to 55% on documented intents, a sector order of magnitude.
The method in full
The reference guide on the subject, with its definition, its sources and its guardrails.
Scope your first AI agent
20 minutes to review your tools, data and the first useful case. No jargon, no commitment.