AI Infrastructure & MLOps for Financial Services
In financial services, an AI model is a regulated artifact. SR 11-7 and OSFI E-23 expect documented validation, ongoing monitoring, and a clear owner for every model in production, and SOC 2, SEC, and FINRA obligations mean you must prove not just that a system works but that you would catch it the moment it stops. MLOps is how model risk governance becomes operational rather than a binder. We build the evaluation, observability, and CI/CD that turn each AI release into an auditable event: validated before it ships, monitored for drift in production, and reproducible on demand for examiners, internal audit, and your model risk committee.
AI Infrastructure & MLOps, built for financial services
We stand up an eval gate that runs validation tests, fairness and bias checks, and performance thresholds on every model and prompt change before it can reach production.
We monitor drift, latency, cost, and output quality in production with alerting, and feed the same metrics into your model risk and SOC 2 evidence trails.
We version every model, dataset, prompt, and eval result so any production decision can be reproduced for examiners, internal audit, or a FINRA inquiry.
It all runs inside your SOC 2 boundary, with KYC and AML data never leaving your cloud or your controls.
Where it pays off in financial services
Model risk monitoring
Continuous drift and performance tracking that satisfies SR 11-7 and OSFI E-23 ongoing-monitoring expectations and feeds your model risk committee automatically.
Auditable CI/CD
Every model release passes a validation eval gate and is logged as a reproducible, signed-off event for examiners and internal audit.
KYC and AML drift
Watch screening and transaction-monitoring models for false-negative drift so AML coverage stays defensible and regulators see a documented control.
Cost and latency SLAs
Tune routing and caching to hold sub-second latency on customer-facing models while cutting cost per call at trading-desk scale.
Financial services clients turn model validation and monitoring from a quarterly scramble into a standing control, often cutting audit-evidence preparation time by more than half while holding sub-second latency on customer-facing models.
Financial Services AI, answered
We treat validation, ongoing monitoring, and documentation as first-class pipeline stages. Each model has a documented owner, pre-deployment validation tests, continuous drift monitoring, and a versioned record, which are the core expectations of both SR 11-7 and OSFI E-23.
Yes. We version the model, prompt, dataset, and eval result behind every release, so any production output can be traced and reproduced. That reproducibility is what turns an examiner request from a fire drill into a lookup.
No. The entire stack, evals, monitoring, and CI/CD, runs inside your cloud and your SOC 2 boundary. Sensitive data and the models trained on it stay under your controls, operated by your team.
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Bring AI Infrastructure & MLOps to your financial services team
Book a free consultation. We'll show you the highest-leverage place to start and exactly how we'd ship it.