AI Infrastructure & MLOps for Banking

On-premise inference makes sense more often than the cloud narrative suggests, at steady high volume, or where data simply cannot leave. We model both honestly.

In banking, core banking systems are not to be touched, so everything integrates around them. That single fact reshapes how ai infrastructure & mlops 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 loan file assembly, usually integrated against loan management systems. We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.

Deployed across regulated and unregulated sectors, with audit trails where the regulator expects them. Six weeks to something running in production, not six quarters to a strategy document.