Your 2026 guide to AI tools for exploration teams

Geologists have always looked for smarter, faster ways to explore, and over the past year AI stopped being a nice-to-have and started finding actual deposits.

That's the real shift since we first wrote this. In 2025 the conversation was mostly about promise. In 2026 it's about proof. AI-guided targeting has helped drive drill success rates as high as 75% on some programs, and KoBold Metals used its AI to help pin down the Mingomba copper deposit in Zambia, one of the biggest finds in years. Money followed the results, with big funding rounds landing for the likes of GeologicAI and VerAI.

Here's the thing that hasn't changed, though. AI is only ever as good as the data you feed it. The teams getting value out of these tools are the ones who sorted out clean, digital data capture first. We'll come back to that at the end.

For now, here's where AI is making the biggest difference in exploration right now, and what's new with each tool since last year.

Where it helps	Tool	What it does
Desk analysis and targeting	RadiXplore	Turns decades of historical reports into searchable intelligence
Drill targeting	OreFox, Earth AI, KoBold	Predict and prioritise targets using machine learning
Core logging and geotech	Datarock, GeologicAI, LithoLens	Automate core imagery logging and interpretation
Core capture hardware	Epiroc CorePhoto	High-resolution imaging and 3D profiling in the field
Geomodelling	Driver (Seequent Evo)	Speed up 3D modelling with machine learning
Reporting	ChatGPT and other LLMs	Draft reports, summaries and presentations

AI for desk analysis and targeting

1. RadiXplore

Think Google for geology, except it reads the handwriting too.

RadiXplore searches, analyses and pulls insights out of historical reports. It's trained on millions of geological documents, including scanned and even handwritten core logs, so it understands the language and terminology geologists actually use. If your team has decades of data locked away in PDFs and legacy files, this is the tool that makes it findable again.

What's new for 2026 is how far the product has moved up the value chain. It's no longer just about search. RadiXplore now pitches itself around critical minerals discovery and M&A, with a Strategic Asset Index that ranks projects and flags acquisition or divestment targets by reading the market's unstructured data.

2. OreFox

Smarter targeting through machine learning.

OreFox helps you narrow down and prioritise drill targets. It compares your exploration data against known deposit datasets and highlights areas with similar geological and geochemical patterns, surfacing prospects that might not jump out through traditional interpretation. It doesn't replace a geologist's intuition. It just points that intuition at the statistically interesting ground first.

3. The new targeting heavyweights: Earth AI, KoBold and VerAI

This category barely existed as a mainstream story when we first published, so it's worth adding properly.

A handful of well-funded companies are now using AI to pick targets and then drilling them themselves, rather than selling software. KoBold Metals used this approach on its Mingomba copper find.

Earth AI has been drilling its own AI-generated targets with unusually high hit rates. VerAI runs a similar asset-generation model across a large portfolio of projects. You may not buy these as tools, but they're reshaping the competitive landscape, and they're proof the targeting models actually work when the underlying data is good.

AI for core logging and geotech

4. Datarock

Faster logging, smarter modelling.

Datarock uses computer vision to interpret drill core imagery automatically, logging things like lithology, alteration and structure, with outputs ready to drop into 3D models or geotech workflows. Its "geologist-in-the-loop" design keeps human expertise steering the interpretation.

The win here is consistency. Logging core by hand is slow and varies between people, so automating the grunt work frees geologists up to focus on what the core actually means.

5. GeoloicAI

One of the biggest movers of the past year.

GeologicAI focuses on high-resolution data off drill core, combining its own core scanners with AI to predict things like non-visible elements and flag new zones of mineralisation. It raised a substantial Series B round and expanded its sensor line-up, adding fast light-element and rare-earth analysis through a laser-based rock scanner. If your programs are geotech or resource-definition heavy, this is one to watch.

6. Litholens

AI-powered rock recognition and data extraction.

LithoLens automates image analysis from core photos, televiewer images and borehole video, classifying geological features into structured outputs like lithology logs or alteration zones. Capturing images is the easy part. Interpreting them the same way every time is the hard part, and that's the gap it fills. It now sits inside a broader modular platform alongside other geology tools.

7. Epiroc CorePhoto (new, and it's hardware)

Most of this list is software, but a lot of the AI value starts at the moment you photograph the core, so the capture step matters.

CorePhoto is a field-ready core photography and 3D laser profiling system built to survive real site conditions, from arctic cold to desert heat. It produces the high-resolution, uniform imagery that machine learning tools need to work properly, with pXRF measurement on the way. It's a good reminder that clean inputs and clever algorithms are two halves of the same job.

💡 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.

AI for geomodelling

8. Driver, inside Seequent Evo

When we first wrote this, Driver AI was new and Seequent Evo had only just launched. A year on, Evo is an established cloud platform and Driver is one of its native apps.

Driver uses machine learning to group, classify and interpret large geological datasets, speeding up 3D model building in tools like Leapfrog while leaving the geologist in control. The bigger story is Evo itself. It's an open, cloud-based home for your subsurface data that connects Seequent and non-Seequent tools, with full versioning and an audit trail of who changed what and when. Real customers are now reporting models built in minutes rather than days, and Seequent has spent the past year wiring Evo into drilling and mine-planning workflows through partners like Orica and Deswik.

AI for reporting and documentation

9. ChatGPT and other LLMs

From field notes to a board deck in minutes.

General-purpose models like ChatGPT can draft summaries, field reports, standard operating procedures and presentations from structured inputs like drilling data or assays.

Most geologists didn't get into geology to write reports, and this is where LLMs quietly save hours, as long as you review and tailor the output rather than trusting it blind. They now handle long documents, read images and tables, and can be set up on your own internal standards and templates.

‍The trends shaping AI in exploration this year

Targeting is delivering, not just promising. The headline change since last year. AI-picked targets are being drilled and hit, and that has moved the whole conversation from theory to results.

Open beats closed. As teams mature, closed ecosystems start to feel like a handbrake. The tools winning right now are the ones that let your data move between systems instead of trapping it, so you can improve one part of the workflow without ripping out the rest.

Human-in-the-loop is still the rule. The best tools don't try to replace the geologist. They surface patterns and leave the interpretation and the judgement calls where they belong.

Agentic AI is the next frontier. The industry is starting to talk seriously about AI agents that chain several steps together on their own, and about protocols that let models tap directly into your geological data. It's early, and it depends entirely on having trustworthy data underneath, but it's the thing to keep half an eye on through 2026.

Data quality is still king. None of the above works on messy inputs. The exploration teams pulling ahead are simply the ones whose data was clean and consistent to begin with.

The bottom line: your data comes first

AI in exploration has gone from quiet achiever to the main event, and it's only getting sharper from here.

But there's a catch that hasn't changed since we first wrote this piece, and honestly it's more true now than ever. AI only works as well as your data allows. Every tool on this list, from targeting to logging to modelling, is only as good as the quality and consistency of what you feed it.

So if you want to get value from any of this, the first move isn't buying an AI tool. It's getting your data house in order, with clean digital capture and consistent workflows from day one.

Want to future-proof your exploration workflows? Start with the foundations. Read our guide with 5 proven tips to level up your digital data capture and get your team ready for the AI era.

Download the checklist here

CorePlan vs Krux: What's the difference?

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A list of trending geology AI tools for exploration teams (2026)

Download the checklist here

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