Fintech Expertise
We build payment systems, trading platforms, and AI-driven decisioning tools engineered for compliance, security, and financial-grade reliability, not just for the demo.
Trusted by engineering teams across 15+ countries
From startups to Fortune 500 companies
Production Reality
Where Fintech AI Projects Fail
Only practitioners who've shipped real systems in regulated environments can flag these accurately.
No audit trail on automated decisions
We architect with structured, decision-level audit trails from day one. Every automated decision is traceable back to the data that produced it.
Fraud models drift silently
We build drift monitoring and scheduled re-evaluation into the pipeline, not a one-time launch-and-forget model.
Compliance bolted on after the fact
We design compliance checkpoints into the architecture before the first feature ships, not after an audit.
Brittle handoffs between automation and human review
We build explicit human-in-the-loop checkpoints for every workflow that touches money or identity.
Is this you?
Three situationsthat bring fintech teams to us
If any of these sound familiar, we've solved this in production, not just in theory.
You're spending compliance and operations headcount on manual reviews that follow a predictable pattern.
You have transaction or account data that could flag fraud or risk automatically, but it's still reviewed by hand.
You're integrating LLMs into your product but hitting production reliability issues.
You're integrating LLMs or automated decisioning into your product and hitting reliability or audit issues once real transactions hit it.
Delivery Structure
PROCESS POC to Production Process
Discovery
POC Build
Production Architecture
Deploy & Monitor
Technical Stack
Models & Frameworks
Preferred for deterministic workflows where state management and branching logic matter, such as multi-step approval flows
Multi-agent conversations where agents critique and revise each other's outputs before a decision is finalized
Role-based agent teams with defined task sequences and human-in-the-loop checkpoints.
Managed vector store for production workloads that need fast ANN search at scale.
Hybrid search combining BM25 + vector, when keyword precision matters alongside semantic similarity.
Local and dev-stage retrieval for rapid prototyping before committing to a managed store.
GPT-4o for tool-use tasks requiring fast structured output and broad API ecosystem compatibility.
Claude for long-context tasks, instruction following, and workflows where refusal behavior needs to be controlled.
Multimodal inputs — document, image, and video understanding in a single model call.
REST and GraphQL integrations so agents can read and write to your existing systems without rebuilding them.
Event-driven triggers that let agents respond to external signals in real time.
Direct database read access for agents that need structured data alongside unstructured retrieval.
The Team
Engineers Behind the Work
Discipline-specific AI engineers not generalists. Each has shipped real systems in production.
Designs multi-agent workflows with audit trails and human-in-the-loop checkpoints for regulated decisioning.
Builds grounding pipelines over policy documents, transaction history, and regulatory text.
Integrates and monitors model providers in production, with fallback logic and cost controls.
Common Questions
Everything you needto know
Which industries do you specialize in?
We have deep expertise in Fintech, Healthcare, Logistics, and E-commerce. Each practice area is staffed with specialists who understand the regulatory requirements, architecture patterns, and competitive landscape of that industry, so your team isn't educating ours from scratch.
Not sure if Fintech AI is right for your use case?We'll tell you in 30 minutes.
No pitch. No obligation. Just an honest answer about whether we can help.