Mining has a very real problem: for the same plot of land, there may be over a hundred geological reports from the past fifty years, scattered across PDFs, scans, handwritten notes, and old systems, and no one can actually read them all. MinersAI specializes in exactly this, using document-understanding and data-extraction technology to organize these historical documents into a searchable, comparable structured database, then producing high-resolution mineral-prospectivity maps from it.
Features and use scenarios
Functionally there are two layers: the lower layer is automatic extraction from geological documents — recognizing drill-hole coordinates, grade data, and lithology descriptions from scanned reports; the upper layer overlays this data with geophysical layers to generate mineral-probability maps. For exploration teams, just the first layer — "finally being able to full-text search all the company's historical reports" — is already a big deal.
It suits veteran mining companies holding large amounts of historical files, due-diligence teams in mineral-rights transactions, and consultants needing to quickly understand a mining area's history. This "unstructured documents to knowledge base" approach is actually the same thing as enterprises adopting RAG retrieval, just with the domain switched to geology.
Main features
- Automatic extraction from historical geological reports
- Scan and handwritten-record recognition
- Structuring of drill-hole and grade data
- High-resolution mineral-probability maps
- Full-text search and cross-report comparison
Common uses
- Mineral-rights due diligence
- Historical-file digitization and knowledge-base building
- Mineral-prospectivity assessment
- Cross-year exploration-data integration
Key Features
- Automatic extraction from historical geological reports
- Scan and handwritten-record recognition
- Structuring of drill-hole and grade data
- High-resolution mineral-probability maps
- Full-text search and cross-report comparison
Pros
- Solves mining's real old problem: historical data can't be read through
- Turns dormant files into usable assets
- Document-extraction ability is especially practical for due diligence
Cons
- Recognition rate is still limited for old files with too-poor scan quality
- Requires human spot-checking to verify extraction results
- Pricing requires negotiation, not self-service
Use Cases
- Mineral-rights due diligence
- Historical-file digitization and knowledge-base building
- Mineral-prospectivity assessment
- Cross-year exploration-data integration
Editor's Note
This is really a 'data archaeology' product. Many industries' first AI step isn't predicting the future but first figuring out the past — true for mining, and also for hospitals, factories, and firms.
FAQ
Can it read handwritten old geological notes too?
Some can, but the recognition rate depends on handwriting and scan quality. In practice, treat AI extraction results as a draft; key data (coordinates, grade) still needs human verification before entering the database.
How does it differ from ordinary document OCR?
The difference is domain knowledge. It recognizes geological reports' structure and terminology and knows which number is grade and which is depth, while ordinary OCR only recognizes characters without understanding the meaning.
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