Offline-First Computer Vision for Industrial Lithology
Field geology in the petroleum and mining sectors operates in remote, network-denied environments. Manual lithology analysis remains a critical bottleneck, relying on scarce expert resources to validate samples. Rock Classifier provides an Offline-First AI solution. Our mobile-native computer vision platform automates lithological classification with expert-level precision, directly on rugged devices, reducing field analysis bottlenecks and operational costs.
Our primary capability: local inference capability on industrial hardware.
Field validation using a rugged tablet. The device is running in a confirmed offline mode, proving the AI model executes inference locally without network reliance.
Built for the industrial edge. We deploy a quantized CNN model optimized for local inference on ruggedized tablets. This architecture allows geologists to receive real-time classification results for visually similar samples like Andesite and Basalt without uploading data.
When the device re-establishes a connection, all local classifications are securely pushed to our AWS SageMaker data lake. This central system aggregates field validation data, allowing for model re-training and continuous accuracy improvements.
Central dashboard view demonstrating automated AWS SageMaker inference integration.
Laboratory validation: comparing AI visual classification against definitive mineralogy mapping.