Fraud & Anomaly Detection for SaaS & Technology

Rules encode known fraud patterns precisely; models catch the novel ones. Every system worth running uses both, and we are explicit about which is doing what.

In saas & technology, per-tenant economics and enterprise security review decide whether a feature can ship. That single fact reshapes how fraud & anomaly detection has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is support deflection, usually integrated against billing and metering. Integration comes before intelligence. A model that cannot reach your systems of record is a demo with good manners.

Multi-model by default, so a provider outage is a routing decision rather than an incident. You own the code, the models where they are open-weight, and the documentation to run it without us.