Evaluation suites
Automated evals that quantify model quality and block regressions in CI.
The harness around the model — evals, guardrails, and observability — paired with an AI-assisted delivery loop that ships AI products fast without trading away correctness.
AI makes it easy to generate a lot of code and a lot of model behavior quickly. Without a harness around it, that speed hides regressions, silent failures, and prompts that drift as the product changes.
Shipping fast and shipping reliably are usually in tension. The harness is what resolves it.
We put the scaffolding in first: evals that score model behavior, guardrails that bound it, and observability that shows what happened in production — so changes are measured, not guessed.
On top of that we run an agent-driven development loop — engineers building in tight cycles with AI agents and tools under review gates — to move quickly with the safety net underneath.
Automated evals that quantify model quality and block regressions in CI.
Input/output validation, policy checks, and fallbacks that bound model behavior.
Tracing, logging, and metrics for every model call — so production is legible.
An agent-driven development workflow with review gates that ships fast without losing rigor.
The harness is the scaffolding around a model that makes it safe to ship: evaluations that score its behavior, guardrails that bound it, and observability that shows what it did in production.
A delivery style where engineers build in tight loops with AI agents and tools generating and revising code under human direction. We pair it with evals and review gates so the speed never costs you correctness.
Because model behavior changes with every prompt, model, or data change. Evals quantify quality and catch regressions before users do — the equivalent of tests for non-deterministic systems.
Yes. We can wrap an existing feature with evals, guardrails, and observability so you can change it confidently instead of fearing every deploy.