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AI is transforming geoscience, and it's becoming as simple as having a conversation.

Seequent, Bentley's subsurface company, recently demonstrated how an AI agent can perform complex ore grade estimation using plain English through its Evo platform. Tasks that traditionally take geologists hours to set up can now be completed automatically using trusted data, with no coding required.

This is a great example of how AI isn't replacing geoscientists, it's helping them work faster by connecting to trusted engineering and geoscience data through the Model Context Protocol (MCP).

The future of AI in mining, engineering, and infrastructure is all about smarter decisions powered by trusted data.

⛏️ Across Bentley, teams are building MCP servers, connectors based on an open standard, the Model Context Protocol, that lets AI agents like Claude work with engineering and geoscience software and trusted project data. Our latest example comes from Seequent, the Bentley subsurface company, and it shows what agents can already do.

🪨 Earlier this year, a Seequent team built an AI agent, in plain English, to generate a series of estimates of the ore grades in a mineral deposit. Geologists normally spend hours setting up this statistical exercise, called kriging neighborhood analysis, in specialized software. Working through Seequent's MCP server for Evo, the platform where mining companies store and analyze their subsurface data, the agent retrieved the data requested, prepared the models, and ran the estimations on its own, within defined rules and permissions. Nobody wrote a line of code. The project demonstrated that AI can become a new interface to geoscience data and compute, rather than replacing the underlying science. The Evo MCP server has been available on GitHub since last year.

Evo is the foundation of Seequent's AI strategy, and in a new blog post, Angela Harvey, Seequent's chief customer officer, says the demo points to the bigger story. "AI is only as good as the data it can access and understand," she writes. Geoscience data is complex, sparse, and scattered across silos, and decisions like resource estimation carry too much weight to run on guesswork. That's why Evo pulls data, compute, and workflows into one trusted foundation. "This data foundation enables AI to operate within real geoscience workflows, where context, uncertainty, and data lineage matter," Harvey writes. "Without this, AI remains a general assistant. With it, it becomes capable of supporting domain-level decisions."

🤖 The demo, Harvey writes, enabled "complex, expert-level tasks to be performed conversationally," highlighting "Evo's capabilities as an AI-enabling platform." It showed "a user performing a complicated geoscience task with just natural language, working on trusted data in Evo."

Most geoscientists aren't developers, Harvey notes, and they shouldn't have to be.
The same thinking now extends to civil and structural engineers: Bentley's MCP servers for STAAD, submitted as a Claude connector, and MicroStation connect AI agents to trusted engineering data across the infrastructure lifecycle.

Follow us for more AI news in infrastructure, engineering and geoscience.

Angela's full piece is worth your time: https://ow.ly/FU9I50ZleRz

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