About EvalOps evaluations

Evaluations are our operating system

EvalOps captures every decision your autonomous systems make, scores them, and routes findings to the teams that keep AI accountable.

Evaluation stack

Automate scoring primitives, keep telemetry indexed, and close the governance loop so every change is reviewable.

Stack 01

We combine automatic metrics, rubric-based reviews, and evaluator programs so you can judge outputs with statistical confidence.

Stack 02

Every prompt, completion, tool call, and policy check lands in a structured index so stakeholders can replay and audit decisions.

Stack 03

Alerts, retention policies, and attestations keep evaluation data connected to incident response, compliance, and release workflows.

Lifecycle

Capture → evaluate → decide → learn

Why it matters: each phase keeps engineers, evaluators, and safety partners aligned so quality signals stay actionable across releases.

01 · Capture

Deterministic snapshot sync, git metadata, and environment fingerprints ensure you can reproduce any evaluation run—locally, in CI, or in production.

02 · Evaluate

Scorecards, monitors, and shadow runs analyze telemetry against quality, safety, and policy objectives.

03 · Decide

CI gates, alerting integrations, and dashboards bring the right stakeholders together before changes ship.

04 · Learn

Annotations, trend analysis, and custom reports feed back into model training, prompt design, and governance reviews.

Bring it to your stack

Clone a Spellbook recipe, connect telemetry, or request the full evaluation playbook—we’ll help you design the loop that keeps AI accountable.