AI systems
Models are components. The system is the product.
We integrate AI into platforms where it creates leverage—retrieval, classification, assistance, decision support—with evaluation and controls treated as first-class engineering.
What we build
Useful models are grounded in clean data, constrained by policy, observed in production, and replaceable when better options appear.
- Use-case selection against operational value and risk
- Retrieval, tooling, and workflow integration
- Evaluation harnesses and regression suites
- Human review paths and escalation design
- Cost, latency, and failure-mode planning
Where this connects
Strong AI depends on strong data platforms and clear software boundaries. We build those foundations when they are missing—rather than covering gaps with prompts.
When this matters
Demos hide drift. Production AI fails when inputs change, scores degrade, and nobody can tell whether a release improved the system.
We treat evaluation, guardrails, and human escalation as product surfaces—so the model stays a replaceable component inside a controllable platform.