Video Intelligence for Mining
Ore Loss Detection & Production Optimization for Mining Operators
Lepei.pro brings AI video analytics to the mining industry helping operators identify ore losses, optimize processes, and save up to $200,000+ per year without upgrading equipment.
Lepei.pro expanded into the mining sector. A partner from Australia initiated the pilot. Through several interviews, we uncovered how the same capabilities—video analytics, machine learning, computer vision—perfectly map to mining's core inefficiencies:
Unaccounted ore losses
Non-optimized haul truck routes
Quality fluctuations in processing
Financial leakage during crushing, screening, and flotation stages.
We created a suite of edge-deployable AI tools that operate on-site (no internet required), analyze video feeds, and deliver actionable results in just 48 hours.
Faced Challenge:
1-Ore Loss Detection
Problem:
Mining sites often lose valuable ore through waste rock misclassification or underloading. These losses go unnoticed and unaccounted.
Impact:
Up to 3% of ore is wasted—equivalent to $200,000+ annually for mid-sized mines.
2-Process Deviations in Crushing and Flotation
Problem:
Manual inspection can't catch minor process drifts or defective outputs in real-time.
Impact:
Inconsistent quality and lower yields, costing $100k+ per year in reprocessing and missed output.
3-Non-optimal Logistics and Loading Operations
Problem:
Inefficient truck routing, underused loaders, and material mix-ups during stockpile management.
Impact:
Wasted fuel, equipment overuse, and unpredictable throughput.
Lepei’s AI-Powered Solution:
AI Loss Detector
How It Works:
Analyzes camera feeds at extraction points, conveyors, or processing areas.
Detects waste rock, underextracted ore, or misclassified materials
Flags loss zones with visual heatmaps and estimated financial impact.
Technology Stack:
Fast3R: Accelerated 3D reconstruction from up to 1500 frames.
SpatialLM: Scene interpretation and object classification.
Edge deployment: Operates directly on mining site hardware.
Benefits:
Recovers 1–3% of ore typically lost.
Adds $100,000–$500,000 in yearly value without changing infrastructure.
AI Production Inspector
How It Works:
Monitors video from processing lines.
Detects anomalies, predicts failures or drifts in production.
Delivers a performance report within 48 hours.
Technology Stack:
Agentic RAG: Auto-refines analysis using a self-questioning agent.
Self-adaptive context engine: Learns mine-specific conditions—ore type, lighting, equipment behavior.
Benefits:
Reduces process-related losses by 20%.
Cuts reprocessing costs and boosts yield predictability.
We provide a 4-week structured pilot program that lets client test and validate the AI workflow before a full-scale deployment.
Phase 1: Pilot Launch
Upload site videos (drone, CCTV, loader cam, etc.)
Define target KPIs (loss %, defect rate, throughput)
Phase 2: AI Detection + Insight Report
Run AI models on supplied footage
Deliver report: heatmaps, before/after comparisons, savings potential
Phase 3: Validation & Scale
Validate results with internal ops team
Optional deployment to edge devices
Define long-term integration plan
Pilot found 3% ore losses this $200k/year saved at Australian mine.
Typical deployment ROI: <2-3 month
No need to change hardware or retrain teams
Builds digital transparency across sites