Proceed
What can move forward with confidence.
AI Behavioral Intelligence
The most dangerous AI failure isn't the wrong answer. It's the answer that sounds right enough that someone acts on it.
Morum AI runs a fixed-scope diagnostic that tests whether your AI's reasoning holds up before the business relies on it. A pressure test of the decision path, delivered in ten to fourteen business days.
Two to three diagnostics per month. Delivered by the founder. Built for teams whose AI output enters audit-bearing decisions.
Failure patterns identified in production
- Policy boilerplate where account-specific data was available and ignored
- Hedging where the data supported a clear answer
- Confidence that outlasted its own evidence by two full turns
- A silent handoff disguised as deference
- Specific facts replaced with generic safety language
- Escalation used as deferral, not as judgment
- Symmetric framing on an asymmetric question
- A recommendation walked back into ambiguity
- Safety language that diluted a defensible recommendation
- Same confident tone, different (and weaker) factual ground
Does this sound familiar?
You've probably already felt this. You just didn't have a name for it.
You caught it being confidently wrong about something you happen to know well, and realized the tone was identical on everything you didn't independently check.
Why your AI gives confidently wrong answers →The output looks clean and professional, but something feels off, and you can't point to a single factual error to explain why.
When AI sounds right but something feels off →The workflow that worked at launch is quietly drifting. Less specific, more generic, and no one can say exactly when it changed.
Your AI used to work. What changed? →None of these are hallucinations. They're behavioral failures: patterns in how the AI reasons when someone is about to rely on it. They pass every surface-level check, which is exactly why they reach decisions. The diagnostic is built to find them. And naming a pattern is the first step to governing it.
Why this is different
Red-teaming tests whether your AI can be broken. This diagnostic tests whether it can be trusted. Those are different failure surfaces, and most organizations have only tested one.
Individual failures are easy to spot. Structural patterns are not. They compound silently across turns, sessions, and decisions. The diagnostic doesn't find one bad output. It maps the failure surface your team is too close to see.
The full methodology, including the eight-stage reliance chain, additional failure categories, and how this approach differs from standard AI testing, is detailed on the methodology page.
View methodology →See the pattern
Watch a failure happen in real time.
The evidence stays the same. The confidence changes. Three turns from hedge to recommendation, with nothing new to justify it.
Best viewed in landscape orientation
Manufactured authority — one of the behavioral failure patterns the diagnostic is built to find. Learn how it works →
Core offer
The AI Reasoning Integrity Diagnostic.
A defined-scope diagnostic of one AI-assisted workflow, delivered in ten to fourteen business days. The engagement tests the workflow under realistic reliance pressure, then shows where the AI can support the decisions it influences, and where it can't.
It answers three questions directly:
- What can safely rely on the AI now?
- Where should authority be restricted?
- What must change before broader reliance?
Every finding resolves to one of three verdicts: Proceed, Restrict, or Remediate.
Restrict
Where reliance needs limits, review, or controls.
Remediate
What must change before broader reliance.
The deliverable
Decision-Risk Findings Brief
It answers the only question that matters once you suspect a problem: where can you keep relying on this AI, and where can't you? Then it shows the work. A concise executive document built for the board, the operating team, and the people who have to act on what the diagnostic finds. Every finding is evidence-weighted and written to close a decision, not open a discussion.
- Executive Risk Snapshot. A one-page summary of what the diagnostic found and what it means for the business.
- Reliance Chain Analysis. Where AI enters the decision path, who relies on it, and what misplaced reliance costs.
- Decision-Signal Integrity Review. Whether the AI output preserves the signal the business needs, or whether the reasoning drifts under pressure.
- Source-Weighting Delta. Where the AI overweights general context and underweights account-specific evidence.
- Decision Authority Boundary. Where the AI should recommend, where it should escalate, and where it should stop, mapped against what the workflow currently allows.
- Remediation Direction. Control patches, retrieval repairs, guardrails, and regression tests, ranked by impact and timeline.
Post-engagement review
Sixty days after delivery, the engagement includes a follow-up review to address implementation questions arising from the brief, surface any new behavioral exposure that has emerged, and assess whether the controls put in place are operating as expected.
Why external · Founder-led
The reasoning layer doesn't test itself.
Built for organizations in financial services, healthcare, legal, and regulated operations where AI output enters audit-bearing decisions.
Tom Dougherty · Founder, Morum AI — LinkedIn (opens in new tab)
The specialists who build the workflow are too close to assess it objectively. The executives who fund it are too far from the output to catch where the reasoning breaks. The experienced operators who used to sit in the middle and catch what looked right but wasn't are the role most organizations spent the last fifteen years optimizing away.
That gap is where I work. 24 years in management consulting, culminating as a Managing Director at Accenture, taught me one thing that applies directly to AI behavioral integrity: the most expensive failures are the ones that pass every surface-level check.
The diagnostic isn't a technical evaluation. It's a judgment problem. It requires operational knowledge of how models behave under reliance pressure, not how they perform on benchmarks, but what happens when a customer, agent, or executive is about to act on what the model said.
When you engage Morum AI, you get me. Not an account team, not an associate, not a relationship layer.
Former Managing Director, Accenture · 24 years in management consulting · Morum AI founded 2026
Commercial path
Fixed scope. Firm pricing. No bloat.
Engagements outside these tiers are scoped separately, not discounted.
AI Reliance Flash Review
$12,500
48–72 hour review of one narrow workflow or output set. For buyers with a defined question who need a directional read on a specific timeline. Limited availability, suitability confirmed during intake.
AI Reasoning Integrity Diagnostic
$25,000
10–14 business day diagnostic of one defined AI workflow. Decision-Risk Findings Brief delivered with executive readout.
AI Behavioral Integrity Mapping Sprint
$55,000
Multi-workflow assessment for organizations with broader AI exposure, higher-stakes outcomes, or board-level review requirements. Typical scope: two to four interconnected workflows over twenty business days. Includes follow-up reviews at 60 and 120 days.
Founding cohort
I am building a founding cohort of early clients. Founding-cohort engagements include follow-up reviews at 60 and 120 days and direct access to me throughout. In return, I ask for a reference conversation once the work has proven itself.
Current lead time for new engagements is six to eight weeks. I take two to three diagnostics per month, and I deliver every one myself.
If I cannot find structural behavioral exposure, you do not need me. I will say so.
Commercial terms
The founding cohort is open now. Engagements are selected on fit. Pricing is firm.
Payment is ACH or wire, due on receipt of invoice. Flash Review: full payment upfront. Diagnostic: 50% upfront, 50% on delivery. Sprint: 50% upfront, 25% at first checkpoint, 25% on delivery.
Engagements begin once the upfront payment is received.
Frequently asked questions
The questions buyers ask before engaging.
How is this different from AI red-teaming?
Red-teaming tests whether AI can be broken. This diagnostic tests whether it can be trusted. The failure modes it looks for emerge when users are not trying to break the system; they are trying to use it as intended. Manufactured authority, confidence persistence, and decision-signal drift do not require an attacker. They happen by default.
How is this different from AI governance reviews?
Governance and compliance frameworks (NIST AI RMF, the EU AI Act, SR 11-7 for financial services) define what an organization should do about AI risk at the policy level. The diagnostic tells the organization whether its actual AI systems exhibit the behavioral failures those frameworks are designed to mitigate. Governance is the policy layer. This is the evidence layer.
What does the diagnostic actually produce?
A Decision-Risk Findings Brief: a concise executive document with an Executive Risk Snapshot, Reliance Chain Analysis, Decision-Signal Integrity Review, Source-Weighting Delta, Decision Authority Boundary, and Remediation Direction. Every finding is evidence-weighted and written to close a decision, not open a discussion.
Who is this for?
Organizations in financial services, healthcare, legal, and regulated operations where AI output enters audit-bearing decisions. Typical buyers are Chief Risk Officers, Heads of AI, General Counsel, and CFOs evaluating whether AI workflows can support the decisions being made on top of them.
The AI can assist. It should not decide.
The diagnostic tells you where that line falls in your workflow. One workflow, in depth, against the failure patterns that benchmark testing misses. The output is a brief your board can act on.
Not ready for a full diagnostic? The Flash Review is the lowest-risk way to see how this works: a directional read on one workflow in 48–72 hours.