Drilling smarter: Your 2025 guide to AI tools for exploration teams

Geologists are always looking for smarter, faster ways to improve exploration, and AI is quickly becoming a key part of the toolkit.

Exploration is a data-heavy game, which makes it the perfect match for AI. The more accurate data you feed it, the smarter it gets - and the more value you unlock.

So what AI tools can geologists start using right now to get closer to discovery?

Here’s a breakdown of the top AI platforms transforming exploration in 2025 and the trends you need to know.

Top AI platforms revolutionising exploration‍

Let’s break it down by where AI is making the biggest impact:

Category	Tool(s)	Key Function
Desk Analysis	RadiXplore	Unlock insights from historical reports
Dill Planning	OreFox	Predict exploration targets using ML
Core Logging	Datarock, LithoLens	Automate core imagery logging & interpretation
Geotech	Datarock	Extract geotechnical data for mine planning
Geomodelling	Driver AI (Seequent Evo)	Speed up 3D modelling via machine learning
Reporting	ChatGPT / LLMs	Generate reports, summaries, and presentations

AI for desk analysis

1. RadiXplore (AI for desk analysis & data discovery)

Like Google for geology - but smarter.

What it does:

RadiXplore is built specifically for mining and exploration teams to search, analyse, and extract insights from historical reports. It’s trained on millions of geological documents - scanned, typed, and even handwritten - so it can recognise and understand complex terminology and geological language.

Why it’s useful:

Exploration teams often have decades of data locked away in unstructured formats - PDFs, scanned core logs, or legacy documents. RadiXplore makes that data accessible and actionable, giving geologists the power to find trends, pinpoint anomalies, or identify overlooked targets in seconds.

🆕 2025 Updates:

Hybrid extractive search: Combines keyword + AI-driven contextual search
Private datasets: Use your internal reports to train your own version
Fuzzy OCR + numerical search: Great for old, messy scanned reports

2. OreFox (AI for drillhole planning & target generation)

Smarter targeting through machine learning.

What it does:

OreFox helps exploration teams narrow down and prioritise drill targets. It compares existing known deposit datasets with your exploration data to highlight areas with similar geological and geochemical patterns, surfacing new prospects that might not be obvious through traditional interpretation.

Why it’s useful:

Geologists know that intuition and experience are powerful, but machine learning can find patterns across massive datasets that a human might miss. OreFox complements the geologist’s eye, providing a shortlist of statistically significant areas worth exploring further.

🆕 2025 Updates:

Refined predictive models tailored to specific deposit styles and terrains
More flexible input options, including structured and unstructured data
Integrated R&D updates to stay aligned with current exploration science

3. Datarock (AI for core logging & geotech data)

Faster logging. Smarter modelling.

What it does:

Datarock uses computer vision to automate the interpretation of drill core imagery. It can log lithology, alteration, structures, and more—with outputs structured for integration into 3D models or geotech workflows. Its “geologist-in-the-loop” design ensures that human expertise still guides the interpretation process.

Why it’s useful:

Logging core is time-consuming and inconsistent across teams. Datarock speeds up this process, creates a repeatable standard, and frees up geologists to focus on insights rather than box-by-box grunt work.

🆕 2025 Updates:

Strategic integrations with IMDEX and ELEMISSION for end-to-end workflows
Improved geotech parameter extraction for slope design and mine planning
More robust cloud-based collaboration and model updates

4. LithoLens (AI for imagery analysis, now part of EarthLabs)

AI-powered rock recognition and data extraction.

What it does:

LithoLens automates image analysis from drill core photos, televiewer images, and even borehole videos. It extracts and classifies geological features, offering structured outputs like lithology logs or alteration zones - now within EarthLabs’ modular SaaS platform.

Why it’s useful:

Capturing images is easy. Interpreting them consistently is hard. LithoLens bridges that gap by turning visual data into structured geological insights - whether it’s from traditional photos or modern scanning tech.

🆕 2025 Updates:

Expanded support for multiple imagery types (e.g. video, televiewer)
Integration with geophysical data for holistic interpretation
Now part of EarthLabs, offering modular tools for geology teams

💡 Thinking about digitising your exploration workflows? CorePlan helps you standardise data from day one—so you’re ready to plug into AI tools when the time’s right.

5. Driver AI (AI for geomodelling, now Seequent Evo)

Your 3D modeller’s new best friend.

What it does:

Driver AI uses machine learning to group, classify, and interpret large geological datasets, speeding up the construction of 3D geological models in tools like Leapfrog. It learns from your inputs, iteratively improving its suggestions over time.

Why it’s useful:

Manual modelling can take days or weeks, especially with messy data. Driver AI reduces that time significantly and removes some of the human bias in early-stage interpretations, while still giving geologists full control.

🆕 2025 Updates:

Fully integrated into the Seequent Evo cloud platform
Real-time collaboration and model sharing
Handles larger datasets with improved performance and scalability

6. ChatGPT & other LLMs (AI for reporting & documentation)

From field notes to boardroom decks - done in minutes.

What it does:

Large Language Models (LLMs) like ChatGPT are general-purpose AI tools that exploration teams can use to generate summaries, field reports, SOPs, presentations, and even safety briefings - based on structured inputs like drilling data or assay results.

Why it’s useful:

Most geologists didn’t sign up to write reports. LLMs automate the documentation process, while still letting you review, edit, and tailor the final product.

🆕 2025 Updates:

Long context windows: Can process entire reports and datasets
Multimodal capabilities: Understands text, images, tables, diagrams
Custom GPTs: Train your own AI on internal geological content and standards

Key trends shaping AI in exploration

The landscape is evolving fast. Here’s what to watch:

🔗 Integrated Ecosystems: No more siloed tools. Companies like Seequent are creating workflows that connect the rig to the lab to the model.

🧑‍🏫 Human-in-the-Loop: The best tools don’t replace geologists - they empower them. Geologists validate and interpret what AI suggests.

🧹 Data Quality is King: AI is only as good as the data it’s trained on. Consistent, digital-first data capture is critical.

☁️ Cloud-Native & Real-Time: Scalability, collaboration, and speed are all getting a boost as tools move to the cloud.

The bottom line: Prepare for an AI-powered future

AI has been a quiet achiever in production, but it's now exploding in exploration. And it's only going to get smarter.

But there’s a catch: AI only works as well as your data allows.

That’s why your #1 priority should be going digital for all data capture, and adopting clean, consistent workflows from day one.

Want to future-proof your exploration workflows?

Start by getting your data house in order. Read our guide with 5 proven tips to level up your digital data capture and prep your team for the AI era.

Download the checklist here

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Explore powerful features that outperform Krux.

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A list of trending Geology AI tools for Exploration Teams

Download the checklist here

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