Data Engineering for E-commerce

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 e-commerce, every change must be justified by a controlled experiment against revenue. 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 search relevance, usually integrated against payment gateways. 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.