AI Engineering
Most enterprise AI work fails somewhere between the demo and the deployment. The model is rarely the reason. We build the parts that decide whether the thing survives: retrieval that respects entitlements, evaluation that runs continuously, and a provider layer that keeps you portable.
Retrieval that respects permissions
Entitlements enforced at the retrieval boundary, not filtered afterwards. The model never sees a document the person asking is not cleared for, because it was never retrieved.
Evaluation as infrastructure
Golden datasets, regression suites, and online evaluation wired into CI. A change to a prompt is treated like a change to code, because it is one.
Provider-portable inference
One interface, several model providers behind it. Cost, latency, and availability become routing decisions rather than a rewrite.
Agents with bounded authority
Explicit objectives, tool budgets, stopping conditions, and declared side effects. Autonomy is scoped deliberately, never by omission.
A pilot works in a notebook and stalls at security review
Nobody can say whether last week's prompt change made things better
One vendor's pricing or rate limits now sets your roadmap
The system is accurate in aggregate and indefensible case by case
- LLM applications
- Retrieval systems
- Agent orchestration
- Evaluation harnesses
- Inference infrastructure
- Model governance
We build the intelligence layer as production software — versioned, evaluated, observable, and governed like any other tier of the enterprise stack.
Talk to us about ai engineering.
Tell us what you have today, what you want instead, and any deadline. A senior engineer will reply within two working days.