A growth-stage lending platform needed an ML-based underwriting model with full auditability - every decision traceable, replayable, and explainable to their compliance team. We built the inference architecture, the eval harness, and the shadow rollout. The system is now serving 11,000 underwriting decisions per day with a full audit trail.
Engineering for systems where getting it wrong shows up in a regulatory filing.
Risk, fraud, payments, and compliance-heavy workflows. We understand regulated systems, audit trails, and the difference between a model that works and one that compliance will sign off on.
Compliance note
We are an engineering firm, not a compliance consultancy. We build systems that are designed to be compliant - with full audit trails, explainable decision paths, and human-in-the-loop controls where required. Final compliance sign-off is with your legal and risk teams.
The engineering problems we see most in fintech.
These are not hypothetical. They come from audits and engagements with funded fintech companies across payments, lending, and compliance-adjacent SaaS.
Fraud and risk systems that do not scale
Rules-based fraud engines hit diminishing returns. ML-based risk models are harder to explain to compliance. You need engineering that understands both sides.
Payments infrastructure held together with duct tape
Integrations built for one provider that now need to support four. Settlement logic that was never designed for current volume. The cost of getting this wrong is not just technical.
Compliance automation that creates more toil than it removes
Reporting pipelines that require manual review. Audit logs that are technically present but practically useless. AML and KYC workflows that slow onboarding without proportionate risk reduction.
AI features that legal will not approve without evidence
LLM-assisted underwriting, document classification, anomaly detection - valuable, but they need explainability, audit trails, and human-in-the-loop design before going near production.
Data infrastructure not built for real-time
Batch pipelines that make risk decisions stale. Event sourcing that was added later and never fully replaced the batch layer. Analytics that cannot answer what the current exposure is.
Platform too fragile to onboard new clients on
Multi-tenancy that was added as an afterthought. Configuration management that is a spreadsheet. Infrastructure that works until a new enterprise client asks about SLAs.
A payments platform processing €40M annual volume was running on a fragile PHP monolith that blocked compliance certification and investor due diligence. We replaced it with an event-driven microservices architecture — achieving PCI-DSS Level 1 certification, cutting transaction latency by 68%, and clearing the technical blocker that enabled the client's Series C close.
A customs broker platform was processing declarations manually — hours of work per shipment, with compliance risk at every step. We built automated declaration processing across four UK and EU customs systems, reducing each declaration from hours to under 90 seconds with 99.9% platform uptime and full audit trail.
What we build for fintech teams.
These are the categories of fintech systems we build and modernize most often. If yours is not on the list, a 30-minute call is the fastest way to find out whether we are the right fit.
- Underwriting and risk-scoring platforms
- Fraud detection pipelines (rules + ML hybrid)
- Payments processing and settlement systems
- KYC/AML workflow automation
- Real-time data and event-streaming infrastructure
- Compliance reporting and audit trail systems
- AI-assisted document review with human-in-the-loop
- Multi-tenant platform architecture
- Neobank and digital banking platform development
What clients say about working with us.
Working on a fintech system that matters?
Book a 30-minute technical call. We will ask about your stack, your constraints, and what getting this right means for your business.
Book a 30-min technical callA senior engineer replies within one business day, often faster.