Government
A sensitive document is wrongly cleared for wider access. Downstream handling trusts the AI’s label.
INFORMATION HANDLINGAI INTEGRITY FOR CRITICAL OPERATIONS
Find hidden failures
in the AI
you rely on.
A model can work well every day and fail under the conditions that matter most. Mesoa helps your team uncover hidden defects and deliberately planted behavior, investigate what activates them, and test possible corrections.
Built around the decisions your business depends on.
MESOA / DECISION INTEGRITYILLUSTRATIVE EXAMPLE
SECURITY OPERATIONS
Same evidence.
A different decision.
Repeated failed sign-insUnapproved privilegesRequired action: analyst review
Original contextSend to analyst ↗
Team label changedSend to analyst ↗
More event historySend to analyst ↗
Both conditions togetherDismissed !
The important facts stayed the same.
The surrounding context changed the answer.
See how a hidden failure can disappear inside a good average.
Owning the infrastructure is one part of trusting the AI.
Its behavior needs evidence too.THE SOVEREIGN AI QUESTION
You bring the model inside.
Its learned behavior
comes with it.
Running AI on your own servers can keep sensitive work inside your organization. It does not establish that the model will make the right decisions.
A model may carry accidental defects—or a backdoor: behavior deliberately planted to activate under particular conditions. If that behavior is learned into the model, moving it behind your firewall does not remove it. It need not contact an attacker to produce a harmful decision.
A strong average score can hide a failure that appears only under a particular combination of conditions. Understanding those conditions helps your team decide where automation needs more evidence.
01 / MODELHidden behavior is learned
Through contaminated training data or deliberate modification.
02 / ACTIVATIONA particular condition appears
Routine tests may never have exercised that situation.
03 / RELIANCEYour process trusts the answer
A warning is dismissed, a document is cleared, or a risk is understated.
WHY THE CONSEQUENCES CAN BE SEVERE
The failure can be quiet.
The consequences can be catastrophic.
When AI filters the work, it filters what people see. In critical processes, a missed warning or false clearance can contribute to financial loss, service disruption, or harm to people. The consequences depend on how the decision is used and what independent checks remain.
Energy
A warning of equipment failure is ranked as routine. Operators lose the chance to investigate early.
FAULT & MAINTENANCE REVIEWDefense
A readiness summary omits evidence of a critical component fault. Planners act on an incomplete picture.
LOGISTICS & READINESSFinancial services
A suspicious transaction pattern is routed away from review. The cases that need scrutiny go unseen.
FRAUD & RISK REVIEWFor teams introducing AI into decisions where a missed signal or false clearance matters.
FOLLOW ONE EXAMPLE
An urgent alert.
A routine answer.
Several failed sign-ins are followed by an account gaining permissions without approval. Your team’s rule says a person must investigate.
In this example, the AI sends the alert for review—until a particular team label appears alongside a longer event history. The evidence stays the same, but the alert is dismissed.
Mesoa investigates what activates failures like this—and tests whether a correction holds on new cases. A pattern like this can warrant investigation; it does not by itself prove the model was poisoned.
Walk through an investigation with usSECURITY ALERT REVIEWILLUSTRATIVE EXAMPLE
The facts in every version
Failed sign-ins + unapproved account permissions
Decision rule: send to an analyst for review.Change the surrounding context. Watch the decision.
TEAM LABELidentity-platform
EVENT HISTORY24 events
IMPORTANT FACTSUnchanged
Same urgent evidence→Dismissed
Nobody is asked to investigate.
In this example, the mistake appears only when both details change.
Illustrative example showing why context combinations deserve investigation.
THE MESOA APPROACH
Start with a real workflow.
End with evidence for a change.
Your team brings the business rules and representative cases, including cases the AI dismissed. Mesoa brings together the AI you already use and the people who know what a good decision looks like.
01
Agree on the right decision
Define what should be reviewed, what can be handled automatically, and which mistakes matter most. Your business rules set the standard.
Your rules + representative cases02
Search for hidden failure conditions
Vary task context, labels, history, and their combinations. Investigate when a correct decision turns wrong, then challenge that pattern on fresh cases.
Failures your team can reproduce03
Compare corrections and alternatives
Where access permits, investigate the model itself and test a targeted correction. Compare with changes to instructions or context, and with replacing the model.
Options matched to your system04
Check that the fix holds
Test cases that weren’t used to develop the fix. Check that important cases get noticed, routine work stays useful, and genuinely different facts still change the answer.
New cases + checks for new mistakesThe decision may be to correct, replace, or withhold the model.
The aim is evidence for that choice: fewer consequential failures while preserving useful behavior. A test result applies to the cases and conditions examined.
EVIDENCE FOR YOUR TEAM
Evidence you can
make a decision with.
An investigation should help an operations owner and an AI engineer agree on what to change, what to retest, and what still needs human review.
Talk about your workflow →The failures, with examples
Cases your team can reproduce, the circumstances in which the AI gets them wrong, and the business rule it failed to follow.
A comparison of the available fixes
Evidence on missed cases, unnecessary reviews, and useful work retained—tested on cases separate from those used to develop each fix.
The remaining limits
What still fails, what wasn’t tested, and which system version the findings apply to. If no satisfactory fix is found, that belongs in the conclusion.
HOSTED AND SELF-MANAGED AI
Your models.
Your deployment.
Your standard for behavior.
Running a model locally gives you control over the infrastructure. Using a hosted model gives you a different set of options. In either case, your team needs evidence about the decisions it makes.
Mesoa matches the investigation and available corrections to the access you have.
Discuss your environment →PRACTICAL QUESTIONS
Where does Mesoa
fit into your business?
Who would use it?
The team responsible for an AI-assisted process: an operations or IT owner who knows which decisions matter, working with the engineer who can test and change the system. Start with one clearly defined workflow and the decisions that matter most.
Would we need to replace our existing AI?
The starting point is the AI you already use. For a model supplied through an API, changes may be limited to instructions, context, provider-supported customization, or model choice. Models you can modify directly allow additional options. Each option still needs testing.
Can this prove that a model is free of hidden backdoors?
No finite set of tests can rule out every possible hidden trigger. An investigation needs to make its coverage, remaining blind spots, and correction limits explicit. A finding alone cannot establish who introduced the behavior or whether it was deliberate.
How would we know an investigation was useful?
Measure the business tradeoff: fewer important cases missed, without an unacceptable increase in unnecessary reviews or mistakes elsewhere. Compare against the current system on separate test cases. Production impact would need its own measurement.
Is the product available today?
Yes. Mesoa is in preview with enterprise customers. Request a demo to see the investigation workflow, discuss your environment, and explore how Mesoa fits your team.
START WITH YOUR WORKFLOW
See what an AI investigation
could look like for your team.
Get a demo →A focused conversation about the AI you rely on.
