Which AI to deploy, which to pilot, which to hold. And proof it survives an examiner.
A fixed-fee diagnostic, 2–4 weeks, answering one executive question: where can this institution deploy AI to create real value, in a way it can defend to a regulator, an auditor, and its own board?
Institutions where every decision has to be examinable
Banks, fintechs, and lenders putting AI into lending, payments, AML, and compliance. If an examiner can ask you to reconstruct a decision, this assessment was built for you.
Not in finance? The same assessment runs for nonprofits, legal teams, and other organizations under scrutiny: same spine, different regulatory hooks.
Scores every use case, returns a verdict
Every AI use case is scored on business value and on data & technical feasibility. Regulatory defensibility is applied as a gate, never a score. Each use case leaves with a verdict: deploy, pilot with controls, or remediate first.
Business value
What the use case is actually worth: revenue, cost, risk reduction, measured against your numbers.
Data & technical feasibility
Whether the data beneath the use case can support it today, and what it costs to close the gap.
Regulatory defensibility
Can you explain, evidence, and reconstruct the decision for whoever examines it? If not, value doesn't matter yet.
When you need this
- A vendor is pitching AI for a regulated decision, and compliance is nervous.
- The board wants an AI plan, and nobody trusts the data underneath it.
- A pilot has been stuck in review for months, and nobody can say exactly why.
- AI spend is rising, and nobody can point to the decision it improved.
The 2–4 week arc
Discovery & context
Stakeholder interviews, regulatory context, and where effort is bleeding.
Data foundation review
The core: lineage, entity resolution, reconciliation, and controls.
Scoring & prioritization
Every candidate scored on value, feasibility, and defensibility.
Synthesis & readout
Opportunity map, roadmap, and an executive decision readout.
Six deliverables, one decision
Everything here exists so you can move, and show your work when someone asks how you knew.
Failure modes a generic review misses
Three named diagnostics, drawn from live systems. Each earned its place by catching something real.
The capacity-blind origination engine
A lending vehicle budgeted origination against available cash, with no throughput constraint tied to facility size. The modeled book ran to roughly $57M on a $13M facility before anyone asked where the ceiling was.
The constraint that matters is often the one nobody put in the model.
Hidden exposure along unmodeled dimensions
A market-neutral equity book was hedged on every named factor and still bled P&L. Regressing P&L on factor returns found an unhedged energy exposure sitting outside the model.
The model answers every posed question correctly. The loss comes through the unposed one.
Aggregate performance that hides the consequential cases
A pre-LLM toxic-language classifier posted a strong headline metric, carried by the easy 99%. The false negatives pooled exactly in the subtle-harassment sliver the system existed to catch.
With fat-tailed consequences, validate the error distribution and weight sampling by consequence, never by volume.
Three tiers, all fixed fee
Focused
One team or one use case. The fastest way to a defensible answer.
Standard
Multi-function scope, with the data-foundation work prioritized.
Comprehensive
Institution-wide and board-ready.
Every engagement, start to finish: the interviews, the data review, the verdicts. 20 years building data and model systems across banking, funds, AI, and the nonprofit sector. LinkedIn
Fixed scope. Fixed fee. 2–4 weeks.
A free 30-minute intro call. You'll leave knowing whether the assessment fits, even if we never work together.
Book a free intro call