Data Engineering for Mining & Metals

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 mining & metals, the environment is hostile to hardware and safety compliance is non-negotiable. 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 ore grade estimation, usually integrated against fleet management. 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. Six weeks to something running in production, not six quarters to a strategy document.