Software and AI for institutions that can't afford to guess.
Custom data infrastructure, production ML, and AI agents for banks, lenders, insurers, asset managers, and fintech. Built for the regulatory rigor and throughput requirements of real financial operations, not adapted from a generic template.
Three things slowing financial operations down.
Legacy systems that resist change
Core banking platforms, mainframe-era risk engines, and reporting tools that run critical operations but resist every attempt to modernize. Modern teams build around them with middleware, spreadsheets, and tribal knowledge instead of solving the underlying problem.
Manual workflows under regulatory load
Loan processing, compliance reporting, and reconciliation eat senior analyst time that should be spent on judgment work. Each step is well-defined and auditable, which is exactly what makes it a candidate for automation rather than headcount.
Models that can't be trusted in production
Off-the-shelf fraud and risk tools generate noise, miss real signal, and can't explain their decisions to a regulator. Teams patch around them with rules engines that drift further from reality every quarter.
Three systems we build for regulated financial environments.
Back-office lift,
without adding headcount.
Aggregate impact across recent loan processing, regulatory reporting, and fraud detection deployments. Specific projects scoped on request.
Got a financial workflow that should be a system?
Straight answers.
Regulatory requirements shape the architecture from day one — audit trails, data governance, access controls, and explainability are design constraints, not afterthoughts. We've built systems that satisfy SOC 2 Type II, PCI DSS, SOX, BSA/AML, and GLBA requirements. Compliance is not a feature request that gets addressed in a later sprint.
Yes. We regularly build API wrappers around legacy systems, connect to core banking platforms, and integrate with trading systems, CRM, and third-party data providers. We assess what your systems actually expose — APIs, database connections, file-based exchange, EDI — and design the integration layer accordingly.
ML models are built with documentation, validation, and governance frameworks that satisfy SR 11-7 model risk management requirements. Models are explainable, auditable, and include the validation artifacts regulated institutions need — not just a trained model file.
Financial data doesn't leave your environment unless you want it to. We build on your infrastructure, your cloud tenancy, or hybrid architectures that keep sensitive data where your compliance team needs it to be. Data residency and sovereignty requirements are addressed during architecture design.
A focused automation — like a loan processing workflow or compliance reporting pipeline — typically takes 8–14 weeks. A full platform with deep integrations, ML models, and regulatory requirements takes 4–9 months. Timeline depends on integration complexity and compliance scope, both assessed during discovery.
Yes. We use phased migration strategies — API wrappers around legacy systems, gradual extraction of functionality into microservices, and database migrations that preserve referential integrity and audit history. The business keeps running while the architecture evolves underneath it.
Ready to talk about your project?
Whether it's automating loan processing, building a compliance platform, or deploying financial AI within your regulatory constraints — tell us what you're trying to solve.