Know what changed.
Know whether to ship.
Evalara gives AI engineering teams one release control plane for traces, quality regression, prompt versions, production monitoring, and model cost.
From production behavior to a defensible release decision.
AI systems regress across prompts, models, retrieval, tools, latency, and cost. Evalara keeps those changes in one evidence path.
Collect the trace, version, quality, latency, and cost evidence behind real outcomes.
Enter stage ↗02ChallengeTurn production failures and expert expectations into representative evaluation pressure.
Enter stage ↗03GateCompare the candidate with the baseline and record a clear ship or hold decision.
Enter stage ↗04LearnWatch the release in production and feed confirmed behavior into the next challenge.
Enter stage ↗Debug the execution, not just the final answer.
See the prompt, model, retrieval, tool calls, retries, scores, latency, and token path behind each outcome.
Enter Capture →Apply the current billing policy. Use tools only when the customer record is required.
Turn real failures into tests that protect the next release.
Curate production cases, compare candidate changes, calibrate scorers, and investigate row-level regressions without losing trace context.
- Versioned datasets and representative segments
- Code, model, rule, and human scoring
- Critical failure gates and review queues
Model migration readiness
Optimize cost without trading away the outcome.
Connect provider charges and token use to releases, features, retries, latency, and successful tasks.
- Cost per successful task
- Model, feature, and segment attribution
- Retry, cache, and routing anomaly detection
Evidence can move. Control boundaries should not.
The release loop is fitted to the customer's production system, instrumentation surface, data boundary, and operating responsibilities.
Architecture
See how execution evidence, evaluation, decisions, and production signals connect.
Inspect layer 02Instrumentation
Define the SDK, OpenTelemetry, model, framework, gateway, and workflow boundaries.
Inspect layer 03Security boundary
Keep access, payloads, retention, redaction, and exports explicit from the pilot onward.
Inspect layerBegin with one production workflow worth controlling.
We define the trace, evaluation standard, security boundary, pilot acceptance criteria, and production rollout before opening the dedicated workspace.
See the pilot engagement →- 01Technical assessment
- 02Pilot scope & SOW
- 03Security review
- 04Workspace & SDK
- 05Production acceptance
- 06Admin invitations
Trace evidence is sensitive engineering data.
Access, payload handling, retention, environment separation, and deployment requirements are confirmed during implementation.
Review security and governance →A named environment is provisioned for the customer and scoped to the agreed applications.
Customer administrators invite users and define authorized project access.
Raw payload access, exports, retention, and sensitive fields remain explicit controls.
Versions, evaluations, reviews, exceptions, and decisions remain reconstructable.
Practical thinking for production AI quality.
A production evaluation standard for AI systems
Define what must be tested, compared, reviewed, and recorded before a model or prompt change reaches users.
Designing a trace schema for multi-step agents
A practical trace model for prompts, retrieval, tools, model calls, retries, evaluator signals, and user outcomes.
Building regression gates for model and prompt releases
Turn offline evaluation results into release rules that protect important use cases without blocking every change.
Put one production AI workflow under release control.
The technical assessment maps the execution path, quality standard, release decision, cost dimensions, security boundary, and practical pilot scope.