Space data firms have long contended that commercial clients require their offerings, yet significant adoption has primarily come from government purchasers. With artificial intelligence now a central priority for businesses, a Spanish startup is positioning itself as the definitive provider of accurate foundational data for corporate use.

Xoople (pronounced “zoople”) is constructing a satellite network designed to gather highly precise information tailored for deep learning models. Founded in 2019, the company has dedicated the past seven years to advancing its technological infrastructure using data from government spacecraft and establishing integrations with major cloud platforms.

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Additional investors comprise MCH Private Equity, CDTI—a technology development fund supported by the Spanish government—alongside Buenavista Equity Partners and Endeavor Catalyst. The startup also revealed a new agreement on Monday with U.S. defense contractor L3Harris, a company recognized for deploying some of the most sophisticated commercial imaging systems in orbit.

Nevertheless, CEO Pirondini did not disclose specifics regarding the satellites, including the planned quantity, confirming only that the sensors will capture optical data. Such advanced systems involve substantial costs, prompting the company to continue securing investment to complete its development.

Pirondini chose not to disclose the firm’s valuation following the latest funding round, though he indicated that “we are in unicorn territory.” To date, the company has accumulated a total of $225 million in funding.

A major distinguishing factor for the company is its emphasis on data quality. However, Xoople is entering a competitive field with established rivals like Vantor, Planet, BlackSky, and Airbus in Europe, all of which already operate satellites and are creating AI-oriented datasets.

Xoople’s distinctive approach lies in its concentration on enterprise platforms. “Our business model centers on integrating our data and solutions directly into the ecosystems of these platforms, enabling them to deliver services straight to their clients,” explained Pirondelli.

He outlined several applications, such as government bodies monitoring transportation systems and natural disaster impacts, agricultural businesses observing crop conditions, and major corporations overseeing infrastructure projects or supply chains.

Currently, the company utilizes publicly accessible data, including information from the European Space Agency’s Sentinel-2 satellite.

“They established distribution channels prior to securing their own data sources—integrating with Microsoft and Esri, the primary platforms used by enterprise, government, and most GIS purchasers, though neither platform possesses proprietary Earth observation data,” noted Ravichandran. “Google’s early progress in geospatial AI models sets the standard they will be evaluated against.”

The exact balance between supplying raw data and creating proprietary analytical tools remains uncertain, but Pirondini aspires to develop “Earth’s System of Record.” He envisions this initiative will eventually involve collaborating with partners to construct a comprehensive AI world model.