StratumAI utilizes machine learning to create precise resource models for mine planning and grade control, enabling more accurate differentiation between ore and waste. This technology enhances drilling efficiency and increases the average mined grade, directly improving the net present value (NPV) of mining operations.
Funding
$170K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
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.
Solution
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.
Target Audience
StratumAI's primary customers are mining companies seeking to optimize their mine planning and grade control processes for increased efficiency and profitability.
Features
- 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.