In fintech, the engineering constraint is not features — it is proof. Every balance must reconcile, every action must be attributable, and every integration must degrade safely when a provider goes down. We build financial platforms where correctness and auditability are properties of the architecture rather than promises in a policy document.
Balances computed in three places drift. Without a single ledger of record, reconciliation becomes a monthly manual exercise nobody trusts.
Document review by hand caps how fast you can grow and produces inconsistent decisions that are hard to defend to a regulator.
Payment gateways, banks and identity providers fail in ways that are invisible until a customer is double-charged or left in limbo.
Regional data residency, retention rules and evidence-grade audit logs are architectural decisions — expensive to bolt on after launch.
A double-entry ledger as the single source of truth, idempotent transaction handling and automated reconciliation jobs that surface breaks the same day they happen.
Document extraction and validation, watchlist screening, and ML-assisted anomaly and fraud detection — with human review kept in the loop for the decisions that need it.
Retries with backoff, circuit breakers, webhook replay and dead-letter handling, so a provider outage becomes a delay instead of a data-integrity incident.
Regional deployment for data residency, encryption and key management, immutable audit logs, least-privilege access, and infrastructure as code you can hand to an assessor.
Rarely, and only when there is a reason to. We integrate established providers and build the ledger, reconciliation and orchestration layer on top — that is where the correctness risk lives and where in-house ownership pays off.
Yes. We deploy to the region your regulator or enterprise customers require, keep infrastructure defined as code so the topology is reviewable, and separate data stores by jurisdiction where residency rules demand it.
We start with deterministic rules and clean event instrumentation, which catches most abuse early, then layer anomaly detection on the data those events produce. It is cheaper, explainable to a regulator, and it works before you have years of labelled fraud data.
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