Production-grade AI for the platforms moving regulated money.
We help fintech and financial-software companies build AI that holds up under SR 11-7 model risk review, SOC 2 controls, and the audit trail every regulated workload has to produce on demand.
AI that survives the audit.
The architecture is documented for model risk from the first commit, governs every decision it makes, and produces the evidence trail examiners and SOC 2 auditors ask for.
Transaction & signal ingest
Transactions, ledger events, device and KYC signals stream in under access controls, the governed input every scored decision is built from.
The state of AI in FinTech.
FinTech has the most mature AI footprint of any vertical we work in: fraud models, risk scoring and underwriting have run in production for years. The frontier isn’t whether AI works; it’s whether it stays defensible under model risk and audit scrutiny.
Every model that touches a credit, fraud or pricing decision is subject to SR 11-7 validation: documented assumptions, challenger models, and ongoing performance review.
Adverse decisions need reason codes a regulator and a customer can both read. A model that can’t explain itself can’t ship into a regulated workflow.
SOC 2, internal audit and examiners all expect an immutable trail on demand. The system has to produce evidence continuously, not reconstruct it after the fact.
The opportunity is mature. The regulatory bar is unforgiving.
What goes wrong in FinTech AI.
Four failure patterns we see again and again, and what they require to fix at the engineering layer.
Models that can’t survive validation
A black-box model that scores well but can’t document its assumptions, lineage or limitations stalls in model risk review. The science was never the blocker. The validation package was.
Built SR 11-7-aligned from day one: documented assumptions, challenger models, validation evidence.
Fraud models that drift silently
Fraud patterns shift faster than almost any other signal. A model tuned on last quarter’s attacks lets new typologies through, while false positives quietly climb and erode customer trust.
Continuous drift and bias evaluation, challenger models, and a retraining pipeline baked into the build.
Core banking / ledger integration
Most fintech stacks bridge modern APIs, a core banking system, card networks and a ledger of record. AI features that assume clean data die in deployment because reconciliation is harder than the model.
We treat integration as core engineering: ledger reconciliation, idempotent writes, schema validation.
No audit trail when examiners ask
When an examiner or SOC 2 auditor asks why a decision was made eight months ago, reconstructing it after the fact is too late. Without continuous logging, the evidence simply isn’t there.
Immutable decision logs, reason-code capture, one-command audit export built into the pipeline.
What makes FinTech AI different.
Three system constraints that fintech AI has to be designed around, not retrofitted into.
Two engagement paths, depending on where you are.
We work differently with teams just starting their AI journey versus teams optimizing what’s already running.
For fintech companies that haven’t yet shipped production AI.
We map where AI delivers the most ROI for your platform, design the data and governance foundations correctly from day one, and ship the first production deployment with model-risk documentation built in.
First production AI shipped, validation-ready, with measurement baked in.
For fintech companies already running AI in production.
We audit what’s running, identify the highest-ROI optimization wins, and implement with quantified before/after measurement: latency, cost, reliability, and the model-risk and observability gaps that fail an audit.
Better latency, lower cost, audit-ready reliability, measured.
We don’t just plan it. We ship it.
A measured outcome from a production-AI engagement, the same operational discipline we bring to FinTech.
The regulatory bar is higher here. So is ours.
Before AI was the conversation, we were building performance-critical, real-time systems for Disney, Coca-Cola, Apple, LEGO, Warner Bros, and SXSW, systems that had to run reliably under unpredictable load, integrate with existing infrastructure, and hold up at brand scale.
That same engineering discipline (observability, integration rigor, real SLAs, immutable audit trails) is what production AI requires. Especially in FinTech, where every decision has to stay defensible under model risk and regulatory review.
