We build intelligent systems that move from prototype to operations: continuous evaluation, data QA, traceability, and measurable controls—adoptable in stages.
End-to-end orchestration: runs suites, captures traces, and produces release-ready evidence packs to operate AI as infrastructure.
Data contracts, automated checks, and statistical sampling for datasets that hold up under drift and audit.
Evaluation system for LLM/ML: suites per use case, failure-mode taxonomy, and actionable reporting to ship changes with control.
Real-time signals for quality and cost: monitoring, alerts, canary releases, and runbook-driven improvement loops.
Every deployment ships with verifiable artifacts: evaluation, data QA, release gates, and operational runbooks.
Domain metrics, edge cases, regressions, and thresholds to ship changes with control.
Rules, statistical sampling, and gold sets for production-grade datasets.
Procedures, owners, SLAs, and playbooks to run AI without improvisation.
Evidence per release and per decision: changes, approvals, and controls.
Real-time decision infrastructure: trusted data, evaluated models (suites + regressions), and continuous operations with control and evidence.
