Services
Everything a top full-stack AI company offers - infrastructure, models, data, conversational AI, agents and governance - pointed at one domain: banking, payments and fintech. Packaged so a senior team ships it predictably.
Three pillars
Every engagement is one of three shapes, and each spans the whole stack.
AI strategy for finance.
Use-case discovery, target architecture, build-vs-buy, security, compliance and data-residency - turned into a costed, sequenced roadmap your board can sign off on.
The whole stack, in production.
Private AI infrastructure → models & fine-tuning → RAG/data → conversational AI → autonomous agents → document/multimodal AI, integrated into core banking, payment rails and back-office.
It survives production, not just a demo.
Governed execution, guardrails, observability, evals and model/LLM-ops. We embed forward-deployed with your team and keep improving what's live.
The catalog
Pick the layers you need, or the whole stack as one integrated system. Each is delivered as a fixed-fee sprint, a fixed-scope pilot, a project or a retainer.
Roadmap, use-cases, governance and data-residency - costed and sequenced for finance.
Stand up GPU, training and inference in your cloud or on-prem - cost-efficient compute you own.
Inference at the branch, POS or terminal - a natural extension of our ECR/POS heritage.
Open-weight LLMs running privately. Your data never leaves your environment.
Domain finance models, fine-tuning, RAG, synthetic data and rigorous evaluation.
Customer and employee assistants grounded in your own data and systems.
Your AI workforce: agents that execute back-office work - reconciliation, payments ops, KYC/onboarding, AML/fraud, disputes - through one conversational interface, integrated with your core.
Statements, KYC docs and contracts turned into structured data - and actions.
Analytics copilots for leadership and operations, grounded in your numbers.
Runtime authorization, guardrails and deterministic routing for money-moving actions - plus observability and audit.
Workshops, training and embedding senior engineers directly with your team.
How it's packaged
Fixed-fee
A 1-2 week discovery or accelerator sprint with a concrete deliverable.
Fixed-scope
A bounded production pilot for one workflow, with success metrics agreed up front.
Outcome-based
A full build - infrastructure to agents - integrated into your systems.
Forward-deployed
Senior engineers embedded with your team to run and improve what's live.
Governance, in practice
“Governance” is the layer that makes an AI system auditable and safe enough for production in finance. Four open-source tools each cover a different part of it - wrapped around deterministic authorization for anything that moves money.
Every model call and agent step is traced - prompt, retrieved context, output, tool calls, cost, latency - linked into one timestamped trace per request. That's the immutable “why did this happen” record a regulator can replay, plus live dashboards and alerts on quality, latency and cost.
RAG-specific metrics - faithfulness (is the answer actually grounded in the source, i.e. hallucination detection), relevancy, context precision. Run offline against a golden set in CI and online by sampling live traces, so “is it telling the truth about your data” stays measurable.
Tracks every experiment, prompt and fine-tuning run with its eval scores, and its model registry gates promotion: nothing ships unless it clears the bar, and you can roll back to a known-good version. Changes become reproducible and regression-checked, not hopeful.
Validates every input and output at request time against policy - typed output schemas, PII and data-leakage checks, jailbreak and prompt-injection detection, off-policy responses - and blocks, repairs or re-asks before anything reaches a user or triggers an action.
MLflow tracks the run; Ragas and scripted eval suites run against a golden dataset in CI; the registry gates promotion to production.
Guardrails enforce policy on every input and output; deterministic code authorizes money-moving actions; Langfuse traces the whole execution.
Live traces are re-scored and human-reviewed, becoming new eval baselines and the next prompt or fine-tune - with the audit trail kept for compliance.
The important boundary
The model proposes. Deterministic, audited code decides whether it's allowed - and carries it out.
These tools govern the AI's behavior. The part that actually protects money - runtime authorization and deterministic routing for irreversible actions - is custom code in the Go/gRPC integration layer. Langfuse, Ragas, MLflow and Guardrails make that pipeline observable, evaluated, versioned and policy-enforced around it.
That's what the first conversation is for. We'll map your use-cases to a costed, sequenced roadmap - no obligation to build with us.