We transform data into strategic intelligence to anticipate risks, optimize decisions, and accelerate value across the mining chain—combining advanced modeling, machine learning, optimization, causal inference, remote sensing, and geotechnical/geomechanical engineering.

We work by aligning objectives, constraints, indicators, and critical risks, integrating operational history, geotechnical instrumentation, and satellite data to build a solid analytical foundation. Based on this, we apply machine learning and optimization models alongside 2D and 3D geomechanical models, both deterministic and probabilistic, incorporating safety and reliability factors.

The results are validated and calibrated against operations and field monitoring, using tools such as InSAR, piezometry, and prisms, ensuring precision and consistency. Finally, we deploy practical solutions through executive dashboards, decision rules, triggers, and mitigation roadmaps.

This approach enables robust decision-making with explicit confidence levels, improves productivity and reduces costs by optimizing fleet utilization and minimizing downtime, controls risks with early-warning criteria that prevent instability events, and ensures coherent mine-plant-tailings planning, aligned with actual geotechnical constraints.