RAG readiness audit before you add AI to existing software
A first step narrower than a rewrite: is your data ready for retrieval, and should you fine-tune at all? This fixed-scope audit covers an existing SaaS product or internal tool—retrieval evals, a written go or no-go, and a build outline you own.
Who this is for
Wholesale distributors, multi-location operators and SaaS teams with messy but real ops data who already know the workflow. Not for greenfield chatbots or open-ended “AI transformation” programs.
Data readiness before any model work
We map where answers live today—tickets, PDFs, the production database, staff docs—plus auth boundaries, and mark what is missing, stale or access-controlled. If the corpus cannot support retrieval, the audit says so and stops.
Retrieval evals, not a demo chatbot
A small set of real operator questions is scored on whether the right passage was retrieved. You receive the questions, the scores and the failure notes.
When fine-tuning is the wrong spend
Fine-tuning appears in the outline only when retrieval on clean data still fails a defined eval. Otherwise the recommendation stays at retrieval on your current model—including, sometimes, a recommendation to wait.
A package your engineers can implement
A corpus map, eval results, an architecture sketch and a fixed next-build scope—for example one RAG pipeline plus one agent workflow. You own the notes and can implement them with us or your own team.
Ready to scope this properly?
Tell us what you are building. A senior engineer replies within one business day with scope, price, and a realistic date — before any contract.