AI
LLM Ops & Evaluation
Ship LLM features with confidence — trace, evaluate, guardrail.
The Problem
LLM features are non-deterministic. Without evaluation and tracing, teams ship prompt and model changes on vibes — and discover regressions in production.
There's no shared definition of “good,” no way to catch quality drift, and no audit trail when an answer goes wrong.
What it does
Tracing & observability
Every prompt, retrieval, tool call, and token is traced, so you can see exactly what the model did and why.
Automated evaluation
LLM-as-judge and rule-based evals score quality, groundedness, and safety on every change and in production.
Guardrails
Input/output filtering, PII masking, and topical gates enforce safety and scope.
Regression gates
Evals run in CI, so a prompt or model change can't ship if it drops quality.
How it works
We instrument your LLM app with tracing and a curated eval dataset that encodes what “good” means for you.
Evals gate changes in CI and monitor quality continuously in production.
Guardrails and dashboards give you safety plus a real-time view of quality, cost, and latency.
Tech stack
Capabilities and typical outcome ranges reflect our delivery patterns and published industry benchmarks; actual results depend on your data and environment.