ObserveWhat production is really doing
MANAGED AI QUALITY & RELIABILITY
Find the failures your dashboard can miss.
Fidelume combines production evidence, proactive challenge testing, calibrated quality teams, and regression memory so AI teams can see what is actually breaking, preserve what the team learns, and know what to test next.
Production reality
Curated quality teams
Ongoing calibration
Regression protection
ProductionUnexpected recovery behavior
Pressure testFailure reproduced in held-out cases
REPRESENTATIVE FAILURE
HIGH
Recovery path breaks after tool timeout
User request
→
Tool call
→
Timeout
→
Wrong recovery
✓
CalibrationReviewer alignment checked
↻
RegressionKnown failure retained
ChallengeWhat could break next
ValidateWhat calibrated teams agree on
ProtectWhat must stay fixed
THE RELIABILITY GAP
“Passed” and “reliable” are not always the same thing.
Automated evals are useful, but real users, difficult edge cases, grader blind spots, and product changes can reveal a very different story.
See how Fidelume closes the gap ↘
THE DASHBOARD SAYS
PASS
Aggregate score looks healthy.
but
REALITY REVEALS
- Tool failureRecovery path breaks
- Grader blind spotHuman disagrees with “pass”
- RegressionOld defect quietly returns
THE FIDELUME APPROACH
A managed reliability loop, not a rating queue.
Production tells us what escaped. Proactive testing looks for what could fail next. Calibrated teams anchor judgment, continuity preserves context, and regression memory makes the learning compound.
Observe production reality
Sample interactions, anomalies, complaints, tool failures, and post-change behavior.
Challenge the workflow
Run edge, recovery, adversarial, regression, and fresh held-out scenarios.
Validate with a calibrated team
Use reviewers selected for the work, with ongoing calibration, QA, adjudication, and specialists when needed.
Investigate, protect, report
Turn failures into evidence, convert them into regression cases, and report what matters next.
Known failures become memory.
Authentication recoveryStable
Tool timeout fallbackWatch
Ambiguous intent routingStable
Recovery path after timeout
HIGH- Observed
- Incorrect handoff after retry
- Expected
- Safe recovery + user confirmation
- Evidence
- Reproduced in challenge set
EXECUTIVE FINDINGS
Illustrative
Reliability review
Priority finding
Recovery behavior needs attention before broader rollout.
EvidenceSeverityNext tests
DECISION-QUALITY EVIDENCE
Give your team something it can actually act on.
Fidelume’s output is built to answer the questions behind the score: what failed, how serious it is, whether it can be reproduced, and what should be tested after the fix. Review decisions stay tied to clear standards so the evidence can be inspected, not merely asserted.
01
Executive findingsWhat matters most and why.
02
Representative failure evidenceObserved vs. expected behavior, severity, and proof.
03
Regression memoryReusable cases that protect prior fixes.
04
Next test planWhere to focus attention in the next cycle.
ENGAGEMENTS
The scope comes after the conversation.
We start with the workflow, the risk, the evidence you have, and the question your team needs answered. Then we recommend the smallest engagement that can answer it responsibly.
01
WHEN THE GAP IS UNCLEAR
Reliability Diagnostic
Focused investigation to identify where the current reliability or evaluation process is weakest.
Best for“We know something is off, but we need evidence before deciding what comes next.”
02
WHEN THE PROBLEM IS DEFINED
Reliability Pilot
A bounded engagement for one workflow with baseline testing, challenge cases, calibrated human review, and initial regressions.
Best for“We know what we want to test. Show us what breaks and whether we can reproduce it.”
03
WHEN THE SCOPE IS LARGER
Expanded Reliability Pilot
Deeper evidence and QA for broader volume, complex trajectories, higher-risk systems, or specialist review, with a quality team shaped around the scope.
Best for“The operating surface is larger than a standard bounded pilot.”
04
WHEN RELIABILITY IS ONGOING
Continuous Reliability
A recurring managed quality function across production sampling, challenge testing, ongoing calibration, evaluator continuity, regression upkeep, fix verification, and reporting.
Best for“We need reliability to keep running after the first finding.”
NOT SURE WHICH FITS?
Discuss your AI workflow
Good. Start with the workflow, not the package.
Bring the reliability concern. We’ll ask the questions needed to understand what should be tested and what evidence would make the next decision easier.
STRONG FIT
Built for AI that already has something at stake.
Fidelume is most useful when a live or near-live workflow has real users, real business consequences, and a team that needs better reliability evidence.
Customer complaintsSomething in production is not behaving as expected.
Repeated regressionsOld failures return after model, prompt, or tool changes.
Unreliable gradersAutomated evaluation and calibrated human judgment disagree.
Unclear quality trendThe dashboard says “fine,” but the team is not convinced.
1Selected intentionallyMatched to the work and judgment required
→
2Calibrated + coachedStandards stay aligned as the work evolves
→
3Continuity + contextKnowledge accumulates across the engagement
QA, adjudication, and specialists are added where the work requires deeper judgment.
QUALITY IS BUILT INTO THE TEAM
Continuity is part of quality.
Fidelume does not treat evaluators as interchangeable labor. We build quality teams intentionally around the work: evaluators are selected for fit, calibrated against clear standards, coached as patterns emerge, and retained where continuity strengthens judgment.
As the engagement continues, the team builds familiarity with the client environment, use cases, recurring failure patterns, and how the standards apply in practice. That institutional knowledge stays with the work instead of resetting with every batch.
Curated teamsOngoing calibrationEvaluator continuityKnowledge retained
EXPERIENCE BEHIND THE WORK
Built on years of direct AI evaluation experience.
Fidelume was built from years of hands-on experience evaluating AI outputs, applying structured quality criteria and rubrics, identifying failure patterns and inconsistencies, and documenting findings for review.
That experience shaped the way we approach managed AI quality today: evidence-based, disciplined, and designed for consistent judgment over time. It informs how we select teams, calibrate decisions, preserve context, and turn findings into evidence a client can actually use.
Structured evaluationCriteria, rubrics, calibration, and repeatable standards.
Failure analysisPatterns, inconsistencies, edge cases, and recurring breakdowns.
Evidence disciplineFindings documented clearly enough to review, reproduce, and act on.
START A CONVERSATION
Bring us one AI workflow.
Tell us what the workflow is expected to do, where failure matters, and what you are trying to understand or improve. We will use that context to determine the quality approach, evidence, and team the work actually requires.
