About
Datarock is a production-grade geoscience AI platform purpose-built for the mining and mineral exploration industry. It addresses the core challenge of fragmented, inconsistent geological workflows by automating repeatable tasks such as core and chip logging, mineralogy modelling, structure and geotech characterisation, and property prediction — all delivered through an explainable AI framework that geoscientists and modellers can trust. The platform supports the full mining lifecycle, from early-stage exploration through resource definition, mine optimisation, and post-mine restoration. Its prospectivity modelling capabilities help exploration teams rank targets, reduce drill spend on false positives, and identify new discovery opportunities using multi-scale, data-driven models. Automated logging workflows evolve with the deposit, reducing interpretive risk and accelerating model updates. By handling high-volume, repetitive geological tasks at scale, Datarock frees senior geologists to focus on interpretation and critical decisions rather than data cleaning and QA. The platform integrates seamlessly with existing software and databases, minimising workflow disruption. With more than 100 organisations relying on it and a reported 90% reduction in data generation and processing costs, Datarock is designed for enterprise mining operations seeking operational efficiency, reduced orebody uncertainty, and smarter resource utilisation.
Key Features
- Automated Geological Logging: AI-powered automated logging and domaining workflows for drill core and chips, delivering consistent, standardised characterisation at scale.
- Prospectivity Modelling: Multi-scale, data-driven models that help exploration teams rank targets, reduce false positives, and focus drill spend on high-value opportunities.
- Mineralogy & Property Prediction: Automated mineralogy modelling and property prediction from core and chip imagery, integrated directly into existing geological databases.
- Structure & Geotech Characterisation: AI-driven structural and geotechnical analysis that captures critical orebody parameters to inform resource and mine planning decisions.
- Explainable AI Integration: Model-ready, interpretable outputs that integrate with existing software stacks, giving geoscientists full visibility into how insights are generated.
Use Cases
- Automating drill core and chip logging at scale to replace manual, time-consuming geological description workflows
- Building multi-scale prospectivity models to prioritise exploration targets and reduce wasted drill spend on low-potential areas
- Accelerating resource model updates by delivering consistent, standardised geological characterisation that feeds directly into block models
- Freeing senior geologists from repetitive data cleaning and QA tasks so they can focus on orebody interpretation and strategic decisions
- Supporting sustainable mine planning by characterising and minimising waste through improved orebody knowledge from exploration through to post-mine restoration
Pros
- Massive Cost Reduction: Users report up to 90% reduction in data generation and processing costs, delivering direct operational efficiency gains.
- Enterprise-Scale Track Record: With 27M+ metres of core analysed and 8 of the top 10 global mining companies as clients, the platform has proven reliability at scale.
- Frees Expert Geoscientists: Automates repetitive data tasks so senior geologists can focus on high-value interpretation, model refinement, and strategic decisions.
- Explainable & Auditable Outputs: Delivers transparent, model-ready AI insights that geoscientists can interrogate and trust, unlike black-box alternatives.
Cons
- Enterprise-Focused Pricing: Subscription pricing is tailored to large mining organisations, making it potentially cost-prohibitive for small junior explorers or consultants.
- Niche Industry Scope: The platform is purpose-built for mining and mineral exploration, limiting applicability to other industries or geoscience disciplines.
- Requires Quality Image Input: Accuracy depends on standardised photography of core and chips; poor image quality or inconsistent capture setups can affect model performance.
Frequently Asked Questions
What types of geological data does Datarock analyse?
Datarock analyses drill core and chip samples through standardised photography, extracting mineralogy, lithology, structure, geotechnical properties, and other key geological attributes used in resource modelling.
How does Datarock integrate with existing mining software?
The platform is designed to integrate with existing geological software and databases, delivering model-ready outputs that fit into established workflows without requiring a full system overhaul.
What is explainable AI in the context of Datarock?
Datarock's explainable AI provides transparent, interpretable results that show geoscientists why a classification or prediction was made, enabling expert validation and building confidence in AI-driven decisions.
How much core has Datarock analysed?
Since 2020, Datarock has analysed over 27 million metres of core across exploration, resource, and geotechnical programmes globally.
Is Datarock suitable for junior exploration companies?
Datarock primarily serves established mining organisations and offers subscription-based products for both chip and core analysis. Smaller companies should contact Datarock directly to discuss options that fit their scale and budget.
