Chunking, hybrid keyword and vector search, reranking and citation plumbing, tuned against questions your team really asks.
Golden sets built from real cases, automated graders, and a gate in CI so a prompt or model change cannot quietly regress quality.
Frontier, open weight or fine-tuned, decided by measured results on your task rather than by whatever launched last month.
Caching, routing between model tiers and prompt discipline, reported as cost per request so finance sees a number they trust.
Short cycles, visible progress and a scope you can change. You see working software every week rather than a status report.
The repository, the infrastructure code and the documentation. In your accounts, under your licence, from the first commit.
We collect the real questions and documents, then write the evaluation set before any prompt exists.
A thin path from question to cited answer over your corpus, measured against that set.
Reranking, guardrails, caching and access control, with quality tracked run over run.
Monthly evaluation reviews, model upgrades tested before they ship, cost watched as volume grows.
Usually not at first. Good retrieval and a clear prompt beat a fine-tune on most tasks. Once a task is stable and high volume, a smaller tuned model can cut cost significantly.
Citations the user can check, refusal behaviour when the corpus has no answer, and an evaluation set that includes the questions the system should decline.
No. Inference runs in your cloud or under enterprise terms with training disabled, and the data path is documented before we build.
Scans, tables and mixed formats are the normal case. Extraction quality gets measured per document type so you know where accuracy stands.