Data Engineering for Banking

Every AI project that stalls stalls here. The model was never the bottleneck, the data was late, inconsistent, or nobody could say what a column meant.

In banking, core banking systems are not to be touched, so everything integrates around them. That single fact reshapes how data engineering 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 account opening documentation, usually integrated against Flexcube. We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep.

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.