Integrate Terra AI into geoscience hubs

The real bottleneck is distribution inside the daily geology and reservoir workflow, not model quality by itself. In mining and subsurface software, the system that already stores drillholes, block models, reservoir interpretations, and team conventions usually wins the next software purchase. That is why Terra AI has to show up as a layer inside Leapfrog, Evo, Petrel, DecisionSpace 365, or similar environments, rather than asking teams to run a second parallel system for core decisions.

• Seequent Evo is explicitly built as an open geoscience data and compute platform with APIs and third party integrations. That makes Evo the natural control point for customers who want new analytics without moving data or retraining teams on a new modeling environment.
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• Micromine and Datamine are already adding AI and automation inside their existing products. Micromine ships Grade Copilot inside geology and resource estimation subscriptions, and Datamine Studio RM remains tied to drillhole links, geostatistics, and reserve workflows. Buyers can get smarter recommendations while staying in the software they already use every day.
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• On the energy side, Petrel and DecisionSpace 365 already sit at the center of shared reservoir work. Petrel combines geoscience and engineering data in one environment, and DecisionSpace 365 is sold as modular open cloud software tied to enterprise onboarding and subscription contracts. That pushes Terra AI toward plug in value, not rip and replace value.
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The next phase of this market belongs to companies that become embedded decision layers inside incumbent hubs. If Terra AI can plug into the systems where geologists and reservoir teams already clean data, build models, and approve plans, it can expand TAM from point prediction into recurring workflow software that rides on top of established seats and budgets.