AI systems
We integrate AI into platforms where it creates leverage—retrieval, classification, assistance, decision support—with evaluation and controls treated as first-class engineering.
Useful models are grounded in clean data, constrained by policy, observed in production, and replaceable when better options appear.
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.
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.
