Current methods for mine planning and grade control rely on resource models that often lack precision, leading to inaccurate differentiation between ore and waste. This imprecision results in inefficient drilling, suboptimal mine plans, and a reduced net present value (NPV) for mining operations.

StratumAI leverages machine learning to construct more accurate resource models for mine planning and grade control. By dynamically reconciling data and improving the distinction between ore and waste, StratumAI's technology enables mining operations to increase the average mined grade. The platform facilitates more effective drilling programs, allowing for better resource development and ultimately contributing to more accurate mine plans that maximize economic returns.

Machine learning-driven resource models for enhanced precision in ore/waste differentiation.

Dynamic reconciliation capabilities for real-time tracking of ore across the mine.

Error correction of dilution, modeling, and grade control issues.

Improved drill targeting for more effective drilling programs.

Increased average mined grade through better modeling.

Direct contribution to more accurate mine plans.

StratumAI's primary customers are mining companies seeking to optimize their mine planning and grade control processes for increased efficiency and profitability.