AI Integration
AI that holds up past the demo
Retrieval, extraction and structured generation built to be evaluated rather than demonstrated.
What makes it production
Structured output validated against a declared schema, so a response either conforms or fails loudly. An evaluation harness from the start, because it is what turns "the model seems better" into a measurement. And retrieval treated as the bottleneck it usually is.
What is included
Retrieval pipelines
Grounded answers over your document set, with the evidence attached to the answer.
Structured extraction
Records pulled from unstructured sources against a schema, validated rather than parsed.
Evaluation harness
Expected versus predicted over structured output, so accuracy is measured rather than asserted.
Provider independence
Capability named rather than vendor, so the model underneath can change without a rewrite.
Where we have done this
Product-Safety Diagnostics for a Medical Group
An AI system that reads twenty thousand product records, extracts their ingredients, and tells a clinician whether a given product is safe for a given patient.
Cosmetics Safety and Labelling Compliance
A document pipeline that turns safety data sheets into structured, regulation-aware records — including the labelling rules a human reviewer would otherwise apply by hand.