The Spanish AI company will use the funding to grow its data sovereignty platform and enterprise contracts.
Sherpa.ai, a company specializing in artificial intelligence for data privacy and security, has raised $18 million in a funding round to accelerate the development of its AI platform for enterprises and governments and expand its work on AI systems built around data sovereignty.
The round includes new investor Forgepoint Capital, a Silicon Valley venture capital firm focused on cybersecurity and artificial intelligence. Existing investors Mundi Ventures, Ekarpen, Allegra Holdings and SETT also participated.
The funding follows a period of commercial growth for the company. In recent months, Sherpa.ai has signed contracts with organizations including Indra, the U.S. National Institutes of Health, Centogene Genomics, Caja Laboral, Unicaja and Prosegur. The projects span sectors including healthcare, finance, industry and government, where privacy, security and data sovereignty are key considerations for AI deployment.
As organizations and governments increasingly prioritize sovereign AI capabilities, Sherpa.ai develops AI infrastructure designed to enable organizations to train, deploy and operate models collaboratively without sharing sensitive information. The platform is intended for use in regulated environments where data privacy and security requirements can limit AI deployment.
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“This round allows us to accelerate our vision: to develop and commercialize a secure and scalable artificial intelligence platform that enables companies and governments to harness the full potential of AI without giving up control, privacy and sovereignty over their data,” said Xabi Uribe-Etxebarria, founder and chief executive at Sherpa.ai.
In parallel with its commercial expansion, Sherpa.ai has expanded its research activities by publishing peer-reviewed studies on privacy-preserving AI, reflecting its ongoing investment in developing and validating its technologies.
Recent research includes “Towards the Next Frontier of LLMs, Training on Private Data: A Cross-Domain Benchmark for Federated Fine-Tuning,” which explores methods for training large language models on private, distributed datasets without sharing sensitive information.
Sherpa.ai also collaborated with the National Institutes of Health and University College London on “Training Together, Diagnosing Better,” a study examining the use of federated learning for rare disease diagnosis.
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In addition, the company has published research on Blind Federated Learning and distributed training techniques that reduce communication requirements by up to 99 percent, with applications in sectors including healthcare, finance, cybersecurity and industry.
Sherpa.ai said it plans to expand the capabilities of its platform throughout the year, including additional features for enterprise and public sector users.
