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Seekr launches beta for dual-use geospatial reasoning engine

Credit: Seekr
Credit: Seekr

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Seekr has launched the beta testing of SeekrGeo, a geospatial reasoning engine. SeekrGeo provides advanced geospatial intelligence to enterprises and government agencies, accelerating actionable insights with launch partner Wyvern to deliver hyperspectral imaging capabilities.

Wyvern, a hyperspectral imaging and Earth observation data company, provides a comprehensive licensing agreement as Seekr’s inaugural data partner. The alliance accelerates enterprise access to scalable, high-resolution hyperspectral imaging powered by AI-driven analysis that can reason, detect changes over time, and identify meaningful patterns in activity for both national security and commercial use cases including wildland fire management, supply chain intelligence, and countless other actionable VLM-based insights.  

As geospatial intelligence (GEOINT) grows to a projected $63B market by 2030, the gap between data availability and usable intelligence continues to widen. Bringing together Wyvern data and Seekr technology fills the gap in the market, giving enterprises and government customers a way to both access multimodal hyperspectral data, and synthesize intelligence and actionable insights with SeekrGeo’s Remote Sensing Foundation Model built for multimodal understanding, contextual reasoning, and autonomous analysis.

“Our first SeekrGeo customers required the use of Hyperspectral imaging to solve the most complex recognition problems. We recognized Wyvern for their best-in-class Hyperspectral LEO constellation and are very pleased to be working with them,” said Rob Clark, Seekr president.

“The biggest barrier to hyperspectral adoption has never been the data, it’s been the difficulty of turning that data into applications,” said Chris Robson, Co-Founder and CEO of Wyvern. “Seekr’s geospatial foundation model changes the equation entirely. Instead of needing months of specialized development work, our customers will be able to build new applications in a fraction of the time at scale.”

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