We build AI systems for organisations that need more than a convincing demonstration. That means grounding every answer in your own knowledge base, measuring quality on every change rather than once at launch, and designing the failure paths before they are needed.
Grounded in your data, not the open internet
Generic models know a great deal about the world and nothing about your business. We build retrieval layers over your documents, tickets, policies and product data so answers cite a source your team can verify — and so the system says "I do not know" instead of inventing something plausible.
Evaluation is part of the deliverable
We write the evaluation harness before the feature. It runs against a frozen set of real cases on every change and reports per-stage attribution: did retrieval surface the right document, did the model use it, did the guardrail fire correctly. Without that, nobody can safely change the system after we leave.
Cost modelled before launch, not after
Inference cost scales with success, which is an uncomfortable property. We model cost per resolved outcome rather than per request, design cascade routing and caching in from the start, and give you a budget ceiling you control.