AI Evaluation & Observability Platform
Because “it felt fine in testing” isn’t a release process for a probabilistic system.
Role: Lead architect — evaluation model, scoring pipeline and platform design.
The problem
AI output is probabilistic, so traditional unit tests alone can’t tell a team whether a production AI system is quietly improving or degrading with every prompt, model or RAG change. This platform turns that into a measurable, CI-gated signal instead of a gut feeling.
Business Context
AI output is probabilistic, so a team shipping prompt, model or RAG changes had no reliable signal for whether a change was quietly improving or degrading production behavior. This platform was built to turn that uncertainty into a measurable release gate for Spire Digi Solution rather than a gut feeling.
Architecture
Key capabilities
Full trace capture across prompts, agent steps and tool calls
Deterministic checks plus LLM-judge evaluation blended into one score
CI-integrated regression gates that automatically block a bad release
Experiment comparison across model, prompt and RAG versions
Cost, latency and safety metrics in a single operational dashboard
Security & Risk
The operational risk here isn't a single exploit — it's silent quality drift: a model, prompt or RAG change that degrades output with nobody noticing until a customer does. The platform treats that as a measurable, monitored risk, not a one-off QA pass.
Outcome
A CI-gated evaluation signal that blocks a bad release the same way a failing test suite would — quality and safety become a release gate, not a hope.
Challenges
Blending deterministic checks with LLM-judge evaluation into one score meant reconciling two different evaluation philosophies without letting either dominate — lean too hard on the LLM judge and it inherits the judge's blind spots, lean too hard on deterministic checks and everything qualitative gets missed. Capturing full traces across prompts, agent steps and tool calls also had to happen without adding meaningful latency to the request path being observed.
Lessons Learned
Silent quality drift is the real risk in AI systems, not one dramatic failure. Treating evaluation as a CI-gated release control — the same way a failing test suite blocks a bad deploy — is what turns “it felt fine in testing” into an actual guarantee. It's the same release-gate thinking behind a Production Readiness Assessment: a system is ready because it has measurable gates that catch regressions before they ship, not because it worked in a demo.
Architecture pillars
Designed and reviewed against all ten — see how I evaluate every architecture.
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Technology
Want something like this, built properly?
I design and ship systems like this one — from architecture through to production.