Shipping AI in regulated fintech isn't a demo problem — it's a trust, governance, and infrastructure problem. This tool evaluates whether your AI initiative is actually ready for production, using the same framework I apply as a PM at Visa's fraud data platform.
Most teams evaluate AI readiness on a single axis: "Can the model do the thing?" But at Visa, I learned that model capability is maybe 20% of the production story. The other 80% is governance, data quality, safety design, developer adoption, and organizational buy-in. Teams that skip these dimensions ship demos, not products.
These map directly to the failure modes I've seen in production AI at scale: model works but data pipeline breaks (Infrastructure), model works but violates compliance (Governance), model works but nobody trusts it (Safety), model works but engineers can't integrate it (Developer Experience), model works but stakeholders kill it (Org Readiness).
In production, this would need: company-specific calibration (a Series B startup and JPMorgan have different readiness bars), historical benchmarking against similar launches, integration with actual infrastructure telemetry, and a collaborative mode where multiple stakeholders can assess independently and compare.
I scoped this to be useful in 3 minutes, not comprehensive in 30. That's a deliberate product decision — a longer assessment would be more accurate but nobody would finish it. The scoring weights governance and safety higher than infrastructure, because in regulated fintech, a compliant 70% solution beats a non-compliant 95% solution every time.