For more than 20 years building the systems that move banks, fintechs and global payment platforms. Now with AI at the core of how we design, code and operate. Lean engineering pods. +100 projects, 4 continents.
Mid-tier and enterprise banks spend more on system integrators than on the engineers who do the actual work. The big consulting playbook still wins RFPs by promising scale and showing past logos, then delivers 60% of scope, 18 months late, with technical debt that you'll be paying down for the next core migration.
Generative AI made it worse. Every vendor pitches an AI roadmap. Few of them know how to put an LLM in front of regulated banking flows without hallucinations leaking into customer-facing conversations or compliance reports. The CISO vetoes the model. The CFO sees the token bill. The project gets quietly defunded.
The gap is not talent. The gap is how the work is organized. A lean pod of senior engineers with AI in the workflow ships in weeks what a 30-person factory ships in months, with audit trails the regulator can actually read.
4 to 8 senior engineers who have shipped banking software together for years. Architect, backend, frontend, mobile, QA, AI engineer. Not a body shop. Not "we'll assemble the team after kickoff." A working unit you embed alongside your team in week one, with AI integrated into the engineering flow itself, not as a demo for the board.
Each LLM choice is deliberate. Claude, GPT, Gemini, smaller specialized models. Routed by use case, with aggressive caching, lean prompts, and code review by humans who understand the deltas. Result: lower cost per shipped feature, fewer production bugs, zero net new technical debt.
Your core (whether legacy mainframe, Marqeta, Stripe Treasury, Galileo, Modern Treasury, FIS, Temenos) handles the ledger and the regulatory primitives. CoreFlow is the layer above it: dynamic limits, approval policies, anti-fraud orchestration, BFFs per surface, multi-core abstraction, observability, audit trail per inference.
Five layers, event-driven, designed so you can swap a core provider without rewriting the bank. Built to coexist with whatever you already run, not replace it.
Most AI in banking still operates on hope: deploy the model, hope it doesn't make things up, hope compliance doesn't find out. Our approach is Model Context Protocol over your authoritative data, with read-only access by default, audit trail per inference, and prompt versioning that maps every output back to the exact prompt, context window, and policy that produced it.
LIA is the customer-facing surface: a voice avatar that actually executes operations, not a chatbot that hands off to a human. Behind it, the same architecture pattern can power agentic payment flows, internal copilot for ops, and AI-assisted compliance review.
Atlas is the engineering method our co-founder Gustavo Quezada designed across the last three years of shipping AI-native production systems for banking. Four pillars. Each one is what separates "AI that demos" from "AI that ships to a regulated customer."
CLAUDE.md-style cascade that teaches the model to operate inside your architecture without ambiguity. The AI knows where things live, why decisions were made, and what to never touch.
Acceptance criteria, scope, canonical location, integration, deploy gate, smoke test. Each one is a contract checked before code reaches main. No tribal knowledge required to review.
Each kind of code has one canonical place to live. The AI does not invent its own path. Reviewers don't argue about location. New engineers find their way on day two.
Row Level Security on Postgres. Read-only MCPs by default. Prompt versioned in Git. Audit trail automatic. The model cannot exfiltrate what the database refuses to serve.
The full method is open to your team during onboarding. We don't ask for IP control of the work. We bring the bench, we leave the playbook.
Built the architecture that absorbed N26's growth from challenger to multi-country licensed bank, including the migration patterns for the EU rollout. Stack decisions still in production today.
Multi-region engineering for an exchange operating at the edge of every payment rail jurisdiction. Same team that built the resilience patterns for Brazil-to-LATAM expansion.
Embedded payments across a telecom's customer base of 70 million. Anti-fraud, KYC, regulatory orchestration, end-to-end in production.
Send us the architecture diagram or the ticket that's been sitting in your backlog for three sprints. We reply within one business day, with the co-founder reading every message.
No deck. No discovery wheel. Just an engineer reading your problem and telling you what they'd do.
