Data and AI Solutions for Mining and Natural Resources

Yes. We design architectures with edge components that collect, buffer and process data locally, so dashboards and models keep working when links to the cloud are slow or interrupted. Data synchronizes when connectivity is available. We size edge hardware and software to site conditions and work with your OT and network teams to fit existing infrastructure and security zones.

We integrate with common fleet management, dispatch, historian, distributed control, laboratory and maintenance systems using OPC UA, MQTT, APIs, database connections or exports, depending on vendor support. Integration is typically read-only from control systems to protect safety and reliability. Where write-back or advisory control is needed, we design it together with your control engineers and follow your change management procedures.

A focused pilot on one asset class, such as haul truck engines or mill gearboxes, typically produces validated models within about three to four months if historical sensor and failure data is available. If failure history is limited, we start with condition monitoring and anomaly detection and refine models as events accumulate. Value is measured against agreed metrics such as unplanned downtime hours or component life.

We build the data platforms and dashboards that collect instrument and sensor data, apply quality checks, maintain complete histories and trigger alerts based on thresholds defined in your management plans. This supports the monitoring and transparency expectations of standards such as the Global Industry Standard on Tailings Management. Engineering judgment and responsibility stay with your engineers of record; our role is to make the data reliable and accessible.

Yes. Many of our solution areas, such as predictive maintenance, operational data platforms, logistics optimization and ESG reporting, apply across resource industries. We adapt data models and analytics to each sector’s equipment, processes and regulatory context, working closely with your domain experts.

We design pilots with scaling in mind: reusable data models, configurable pipelines and models that can be retrained on each site’s data. After a successful pilot we create a rollout playbook covering integration, validation and change management steps, and we support site teams through adoption. A dedicated team or managed service model can then operate the solution across the portfolio.