HUMAN-LED AI RELIABILITY OPERATIONS

Find the failures your dashboard can miss.

Fidelume combines production evidence, proactive challenge testing, calibrated human judgment, and regression memory so AI teams can see what is actually breaking and what to test next.

Discuss your AI workflow See the reliability loop ↗

Production reality Proactive pressure Human validation Regression protection

ProductionUnexpected recovery behavior

Pressure testFailure reproduced in held-out cases

REPRESENTATIVE FAILURE

Recovery path breaks after tool timeout

HIGH

User request → Tool call → Timeout → Wrong recovery

✓

CalibrationReviewer alignment checked

↻

RegressionKnown failure retained

ObserveWhat production is really doing

ChallengeWhat could break next

ValidateWhat trusted humans 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. Human review anchors judgment. 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 humans

Use calibrated reviewers, QA, adjudication, and specialists only when needed.

04–06

Investigate, protect, report

Turn failures into evidence, convert them into regression cases, and report what matters next.

REGRESSION LIBRARY

Known failures become memory.

Authentication recoveryStable

Tool timeout fallbackWatch

Ambiguous intent routingStable

FAILURE RECORD

Recovery path after timeout

HIGH

Observed: Incorrect handoff after retry
Expected: Safe recovery + user confirmation
Evidence: Reproduced in challenge set

EXECUTIVE FINDINGS

Reliability review

Illustrative

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.

Executive findingsWhat matters most and why.

Representative failure evidenceObserved vs. expected behavior, severity, and proof.

Regression memoryReusable cases that protect prior fixes.

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.

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.”

WHEN THE PROBLEM IS DEFINED

Reliability Pilot

A bounded engagement for one workflow with baseline testing, challenge cases, human validation, and initial regressions.

Best for“We know what we want to test. Show us what breaks and whether we can reproduce it.”

WHEN THE SCOPE IS LARGER

Expanded Reliability Pilot

Deeper evidence and QA for broader volume, complex trajectories, higher-risk systems, or specialist review.

Best for“The operating surface is larger than a standard bounded pilot.”

WHEN RELIABILITY IS ONGOING

Continuous Reliability

A recurring managed loop across production sampling, challenge testing, human validation, regression upkeep, fix verification, and reporting.

Best for“We need reliability to keep running after the first finding.”

NOT SURE WHICH FITS?

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.

Discuss your AI workflow

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 trusted human judgment disagree.

Unclear quality trendThe dashboard says “fine,” but the team is not convinced.

1Calibrated reviewerApplies agreed rubric

→

2QA / adjudicationChecks disagreement + severity

→

3Specialist when neededOnly for domain-specific judgment

Humans validate both the AI system and, when needed, the automated evaluator judging it.

HUMAN-LED BY DESIGN

Judgment stays where judgment matters.

Tools can support the work. They do not replace the people responsible for defining success, calibrating review, investigating failures, adjudicating important cases, and interpreting what the evidence means.

TrustedIntelligentPreciseModernHuman

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 make the first conversation useful.

Prefer email? codie@fidelumeai.com ↗