AI Evaluation Infrastructure
We build evaluation infrastructure that measures the quality, safety and cost of your AI products, so every release is backed by evidence.
If you can't measure an AI system, you can't improve it safely. We build the infrastructure that tells you, with evidence, whether a change made things better or worse.
Problems we solve
- Releases decided by gut feel and a handful of manual spot checks
- Prompt or model changes that silently break behavior customers rely on
- No agreed definition of "good" across product, engineering and leadership
- Evaluation scripts that nobody trusts, maintains or runs
How an engagement works
- Define quality: we turn your product goals into measurable criteria and representative test sets.
- Build graders: we combine rule-based checks, model-based graders and human review where each fits best.
- Automate: we wire evaluations into your CI/CD so every change is scored before it ships.
- Operationalize: we add dashboards, drift monitoring and a process your team can run on its own.
What you get
- Curated, versioned evaluation datasets that reflect real usage
- Automated graders calibrated against human judgment
- Regression gates that block releases which lower quality or raise risk
- Clear reports on quality, safety, latency and cost for every release