LLM applications built on your data, with evaluation attached
Generative AI features that answer from your own documents and systems — retrieval, grounding, evaluation, and guardrails, rather than a chatbot wrapper with no way to measure whether it is right.
AI engineering coverage
Comprehensive ecosystem coverage backed by vetted senior principals and certified specialists.
Models & APIs
Retrieval & Grounding
Application Layer
Quality & Safety
Typical AI engagements
- RAG applications — assistants grounded in your policies, contracts, or documentation
- Document processing — extraction and classification at volume
- Evaluation harness — measurable accuracy before anything reaches users
- Integration — AI features inside existing applications and workflows
- Guardrails — injection defence, PII handling, and output validation
- Cost control — model routing, caching, and token usage management
Demos are easy, production is not
The gap between a convincing prototype and a deployable system is evaluation, error handling, and knowing what the model does on the inputs nobody demonstrated.
- Evaluation dataset built from real examples before launch
- Accuracy measured and tracked, not assumed from demos
- Failure modes defined, including refusal and escalation paths
- Human review where the cost of being wrong is high
Frequently Asked Questions
Transparent insights into our delivery, pricing models, security compliance, and onboarding timelines.
Does our data go to a third-party model provider?
Only if you approve it. We can work with enterprise API agreements that exclude training use, with cloud-hosted models inside your tenancy, or with self-hosted open-weight models where policy requires it.
How do we know whether the AI feature is accurate enough?
You measure it. We build an evaluation set from real cases and report accuracy against it, so the deployment decision is based on numbers rather than impressions.