AI evaluation and release assurance
Run every candidate against versioned cases, compare it with the trusted baseline, and enforce explicit standards for quality, safety, latency, and cost.
Follow the evidenceThe candidate promises an exception that is absent from the supplied policy.
Unsupported eligibility claim · critical case · release requirement failedInfrastructure monitoring can confirm that a system responded. It cannot tell you whether the answer was accurate, safe, grounded, useful, or better than the version it replaced.
The request returned successfully.
The response was correct, grounded, and useful.
Latency stayed below the service timeout.
Latency stayed inside this workflow’s release budget.
No exception was recorded.
No critical safety case or required behavior regressed.
The service is available.
This candidate has enough evidence to release.
The baseline is the currently approved version or deliberately selected reference. It tells Revalte what the candidate is replacing—not what “perfect” looks like.
Compare the candidate with the approved behavior, case by case.
Satisfy independent quality, safety, latency, and cost thresholds.
Change the candidate or tighten the quality floor. Revalte evaluates the complete policy, preserving both aggregate movement and the cases that could block a release.
A prompt change that improves average quality but introduces one critical policy regression.
One critical case regressed even though the aggregate score improved.
Revalte connects pre-release evaluation with production evidence so the reason a version shipped remains attached to what happens after it reaches users.
Register the application, version the test cases, and combine quality, safety, latency, and cost checks into an immutable evaluation suite.
Run both versions against the same cases and conditions, then inspect case-level outcomes, score distributions, regressions, and execution evidence.
Apply versioned policies to the complete result set so approval, rejection, and exception handling remain explainable and auditable.
Connect production telemetry to the approved version and watch quality, drift, errors, latency, tokens, and cost across environments.
Route incidents to the right operators, preserve the triggering evidence, coordinate recovery, and carry what was learned into the next evaluation cycle.
Combine exact checks, semantic evidence, model-based rubrics, trajectory analysis, custom domain logic, and accountable human review. Each answers a different question.
Ask a configured judge model for a schema-validated score, outcome, explanation, and evidence while retaining its prompt, model, retries, tokens, and cost.
LLM judges contribute structured evidence. They do not become an unquestionable source of truth.
Prompts, retrieval, application code, tools, orchestration, providers, and runtime conditions all influence the result. Revalte evaluates the assembled application.
Run an agent toward a case objective and assess its trajectory, tool choices, intermediate state, final answer, resource use, and termination behavior.
Route ambiguous and high-impact cases to people. Preserve assignments, comments, consensus, escalation, and resolution beside the automated evidence.
“The candidate gives the right answer, but cites a retired policy version.”
Make thresholds and escalation rules explicit before the evaluation runs.
Compare reviewers and automated evaluators to identify unreliable judgment.
Keep the rationale beside the outcome so future teams can understand the decision.
The release carries its policy and evaluation identity.
Authenticated telemetry is joined to the deployed version.
A threshold breach retains the trace and triggering evidence.
The failure becomes a permanent test for the next candidate.
A versioned release policy resolves the complete result set into an auditable gate.
Choose a workflow and follow it from the team and triggering change through the evaluation evidence, policy consequence, and next action.
Compare each candidate with the trusted baseline and require the complete policy to pass before release.
The candidate lowers median latency, but three refund-policy cases begin promising exceptions that the approved version correctly refuses.
Approved v2.3 baseline, frozen case set, evaluator versions, and release policy
Support assistant v2.4 using the proposed model and revised system prompt
Bind the candidate, trusted baseline, dataset, evaluator versions, and policy to one reproducible run snapshot.
Run both versions against ordinary, boundary, and critical policy cases under equivalent conditions.
Compare quality, safety, latency, token use, and cost at both aggregate and individual-case level.
Resolve mandatory requirements and allowed tolerances into an approve, investigate, or block decision.
Retain the exact inputs, versions, failures, rationale, and resulting approval or rejection.
The latency gain does not override failures in a mandatory refund-policy risk slice.
Restore the grounded refusal behavior, create regression cases from the failures, and rerun the unchanged release suite.
Evaluate the full trajectory and preserve every decision needed to reproduce and explain the result.
The final message says the email was changed successfully, but the candidate calls the update tool before completing the required identity check.
Approved v2.0 trajectory behavior plus the required verify-before-update tool policy
Account-service agent v2.1 with a new profile-update tool and revised planning prompt
Version objectives, allowed tools, argument contracts, forbidden actions, step limits, and completion criteria.
Capture planning events, tool selection, arguments, responses, retries, state transitions, and the final answer.
Judge task completion alongside tool order, argument validity, policy compliance, looping, and resource use.
Route uncertain or high-impact trajectories to a reviewer with the evidence required for a decision.
Apply the release policy to critical failures and aggregate reliability before permitting promotion.
A successful final message cannot compensate for a forbidden state-changing action before identity verification.
Correct the tool-ordering guard, add the failed trajectory to the immutable regression suite, and rerun the candidate.
Test retrieval, citations, relevance, groundedness, and answer quality together across versioned risk slices.
The candidate finds the current regional policy more often, but a high-risk answer cites an outdated passage and adds an unsupported eligibility condition.
Approved retriever v3, versioned source corpus, expected citations, and risk-slice labels
Retriever v4 with revised chunking, reranking, and answer prompt
Bind the corpus snapshot, retrieval configuration, candidate, reference, cases, and expected evidence.
Measure whether the right passages appear, where they rank, and whether stale or conflicting context enters the result.
Check citation validity, claim support, groundedness, completeness, and answer usefulness together.
Separate improvements in routine questions from regressions in regions, policies, languages, or other critical slices.
Apply requirements to retrieval and generated-answer evidence rather than relying on one blended score.
Overall retrieval improves, but the unsupported high-risk answer requires diagnosis and reviewer confirmation.
Repair source selection for the affected policy slice, review the disputed claim, and rerun the candidate against the same corpus snapshot.
Join deployment identity with telemetry, sampling, semantic drift, monitoring rules, and incident evidence.
Authenticated telemetry shows a sustained semantic shift, rising escalation failures, and a quality decline concentrated in the new request cluster.
The approved v3.2 evaluation envelope, baseline distribution, monitoring rules, and deployment identity
Sampled production behavior from assistant v3.2 in the affected time window
Associate authenticated observations with environment, application version, trace, request, and release evidence.
Apply governed production sampling and evaluators while redacting configured sensitive attributes.
Compare quality, semantic distributions, errors, latency, tokens, and cost with the approved envelope.
When a monitoring rule is sustained, preserve the triggering samples, traces, rule state, and delivery history.
Convert representative production failures into versioned cases before the next candidate is approved.
The change is sustained and localized, providing enough evidence to respond without treating all traffic as equally degraded.
Triage the new billing-intent cluster, mitigate the failing path, and promote representative samples into the regression dataset.
The same evidence chain adapts to narrower questions across development, release, governance, and live operations.
Compare prompt versions against the same cases and evaluators, then inspect which capabilities improved, regressed, or merely shifted in style.
Run candidate and reference models under controlled cases while recording normalized quality, latency, token use, cost, and provider evidence.
Compare quality distributions and critical requirements alongside token, latency, and cost changes instead of optimizing a single average.
Trigger a versioned evaluation for a candidate build and return an auditable approve, investigate, or block result to the release workflow.
Route selected or uncertain results to assigned reviewers, retain their rationale, and compare agreement across reviewers and evaluators.
Combine deterministic invariants, structured rubrics, trajectory checks, and escalation so critical failures cannot disappear inside an aggregate score.
Version datasets, risk-slice metadata, evaluator configurations, and suite membership so a later rerun answers the same documented question.
Constrain available tools and arguments, execute approved scenarios, and retain calls, responses, errors, and policy outcomes for inspection.
Validate schemas, required attributes, exact invariants, numerical limits, and semantic requirements before downstream systems consume a candidate response.
Bind the approved application version and release decision to an external deployment reference so production evidence retains its origin.
Preserve representative incident evidence, convert the failure into a versioned case, and verify the fix against the same behavior before release.
Follow the chain from case and execution through evaluator results, policy decision, approval, deployment, and production observations.
Revalte gives AI teams a repeatable way to test candidates, understand regressions, enforce release standards, and carry the same evidence into production.
Follow the evidence again
Correct content is not sufficient here. The source was superseded and the policy requires current evidence.