AI evaluation and observability
AI evaluation and observability for teams whose AI features pass the demo and drift in production. We build an evaluation suite from your real traffic, trace every model call with quality, cost, and latency, and gate changes on regression checks. You stop arguing about whether the AI is good and start reading it off a dashboard.
Every week, on a dashboard your team reads.
WHEN YOU NEED THIS
WHAT'S INCLUDED
- Evaluation set built from your real production traffic
- Automated quality scoring with LLM judges
- Human review sampling to keep the judges calibrated
- Regression gates that run the eval suite on every prompt or model change
- Tracing on every AI call: inputs, outputs, tool use, cost, latency
- Quality, cost, and latency dashboards
- Alerting on drift, failure clusters, and spend anomalies
- Feedback capture loop from users back into the eval set
- Provider and model comparison harness for swap decisions
- Incident review process for AI failures
- Runbook for operating the eval system
- Handoff documentation
WHAT'S NOT INCLUDED
- Research-grade benchmark development
- Model fine-tuning (separately scoped)
- A one-off audit with no instrumentation left behind
HOW WE DELIVER IT
Tracing wired around every AI call so production behavior is visible before anything changes.
FAQ
Honest answers to the questions that show up most.
With an evaluation set built from your real traffic, scored by automated judges we calibrate against human review samples. The set grows as usage grows, so the measurement tracks the product, not a frozen benchmark.
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