AI Integrations
Production AI wired into the systems you already run.
Most systems that stall between pilot and production do not stall at the model. They stall because the retrieval layer returns the wrong passage, because nobody specified what the system should do when the record is insufficient, or because the only evidence of quality is one aggregate accuracy figure.
What we build
We wire AI into the systems you already run, rather than beside them. That means the retrieval layer reads your corpus with its access controls applied at retrieval time, the orchestration layer has specified behaviour under failure, and the evaluation layer produces a table a reviewer can read down.
How we know it works
Retrieval is scored separately from generation. Until those two are separated, an improvement is a guess: a pipeline that scores well end to end while retrieving badly will fail the moment the corpus changes.
Refusal behaviour gets its own evaluation set. A system that answers everything is not more useful than one that declines what it cannot handle, it is more dangerous, because the failure is silent.
What this covers
- Retrieval over your corpus
- Extraction, chunking on document structure, and metadata for effective date and supersession, so the answer cites the version that governs.
- Model orchestration
- Routing, fallback and cost control across models, with the behaviour under load specified rather than discovered in production.
- Evaluation harnesses
- Scored by slice rather than in aggregate, including refusal behaviour and the cases a review board will probe.
- Human review paths
- Where a system should decline and escalate instead of answering, and what the reviewer sees when it does.