Bring AI into your financial platform, without the model-risk and audit failures that sink most first deployments.
We help FinTech and financial-software companies move from “we’re evaluating AI” to “we’re running AI features that pass review”, with infrastructure, model-risk governance, and ROI instrumentation built in from day one.
Where Pre-AI fintech companies stall.
Most FinTech teams starting their AI journey hit the same wall in the same place, usually the moment risk, compliance, or audit gets a look at the model. The pattern is almost identical, engagement after engagement.
The pilot looks promising.
A fraud-scoring model flags suspicious transactions with strong accuracy on historical data. Leadership greenlights the work. A small AI team is hired or assembled.
The governance question gets deferred.
The pilot was built fast, in a notebook, with no validation trail. Production needs SR 11-7 model documentation, SOC 2 controls, and reproducible evidence, none of which were in scope. Risk and audit stop it at the gate.
The use case wasn’t strategically prioritized.
The pilot was chosen because it was technically tractable, not because it was the highest-ROI, lowest-regulatory-friction use case. The more valuable use cases (reconciliation, underwriting, dispute triage) stay un-built.
There’s no ROI instrumentation.
The feature ships, but nobody can quantify fraud caught, hours of reconciliation saved, or false-positive cost avoided. Leadership can’t make the case for the next investment.
Momentum stalls.
The AI initiative loses internal sponsorship. The model sits unapproved. The next pilot starts from zero, and risk trusts it even less.
We exist to break that cascade. →
How we work.
A structured 60–90 day engagement that sequences strategy first, then infrastructure, then deployment, with model-risk governance and measurement baked into every stage, not bolted on at review.
AI Opportunity Mapping
We start by mapping where AI actually pays off across your platform: every candidate use case scored on ROI, technical tractability, and regulatory friction. You get a ranked list risk and compliance can live with, not a wishlist.
Every candidate use case, scored before a line of model code.
We map and prioritize opportunities against financial ROI, technical tractability, and regulatory friction, specific to your platform. You walk away knowing exactly what to build first, what risk will approve, and why.
The panel that proves what the feature is actually delivering.
Measurement systems and runbooks quantify the value to the business and the evidence for the auditor, so the case for the next investment makes itself. Evidenced, not asserted.
What you walk away with.
Concrete artifacts, infrastructure, and a shipped feature that clears review, not slides.
A first production deployment: shipped, reviewed, not piloted.
What a Pre-AI engagement looks like when it lands: a scoped feature live in production, instrumented and documented from day one.
A FinTech team had a promising fraud-scoring pilot but no production path: a notebook-only model, no ledger integration, no validation trail, and no way to clear SR 11-7 model review.
We scored the highest-ROI, lowest-friction use case, built the ledger-integration and lineage foundations, and shipped the first production feature, instrumented to measure financial outcomes and documented to pass review from day one.
Engineering under real-world constraints, before it was an AI problem.
Cryenx’s engineering legacy is in performance-critical, real-time systems for brands where production reliability wasn’t optional: Disney, Coca-Cola, Apple, LEGO, Warner Bros, SXSW. Real-time interactive systems, AR/XR experiences, integration with complex existing infrastructure.
That experience, engineering under real-world constraints, integration with legacy systems, observability and traceability discipline, is exactly the discipline FinTech AI requires under SR 11-7 and SOC 2. Most AI teams don’t have it. We do.
