Sherpa.ai raised $18 million in a funding round to support the expansion of its data-sovereign AI platform. The round brings in new investor Forgepoint Capital, a Silicon Valley venture firm focused on cybersecurity and artificial intelligence, alongside existing backers Mundi Ventures, Ekarpen, Allegra Holdings and SETT.
The funding arrives as organizations in regulated sectors-including healthcare, finance, and government-face increasing pressure to deploy AI while keeping sensitive data private. Sherpa.ai's platform enables collaborative model training without moving or exposing raw data, a technique often implemented via federated learning.
"This round allows us to accelerate our vision: to develop and commercialise 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 CEO of Sherpa.ai.
Federated learning and privacy research
The company has published multiple peer-reviewed studies validating its approach. One study, "Towards the Next Frontier of LLMs, Training on Private Data: A Cross-Domain Benchmark for Federated Fine-Tuning," investigates methods for training large language models on private distributed datasets. Another collaboration with the US National Institutes of Health and University College London, titled "Training Together, Diagnosing Better," applied federated learning to improve rare disease diagnosis. Additional research on blind federated learning demonstrated techniques that reduce communication overhead by up to 99 percent, with implications for sectors where bandwidth and latency are constraints.
Contracts across regulated sectors
Sherpa.ai has recently signed deals with Indra, the US National Institutes of Health, Centogene Genomics, Caja Laboral, Unicaja, and Prosegur. These engagements span healthcare, finance, industry, and government, where data sovereignty and privacy compliance are mandatory. The platform's ability to operate in such environments without data sharing will be critical as more organizations seek sovereign AI capabilities.
The rise of federated and privacy-preserving AI methods is pushing IT departments to rethink infrastructure. For IT and development teams building secure AI infrastructure, federated learning architectures require new skills and design patterns-areas explored in our AI for IT & Development resources. Government agencies tasked with deploying AI under strict data residency rules may find this approach directly applicable, a topic we track in AI for Government.
Why this matters for IT and development
Privacy-preserving AI is moving from academic research into production-grade tools. The $18 million investment in Sherpa.ai signals practical demand for systems that let organizations train models collaboratively without centralizing data. For developers and architects, this means understanding distributed training techniques, encryption-in-use, and data governance frameworks will shift from niche expertise to a baseline requirement. Those working in healthcare, finance, or government should monitor these developments and consider how federated architectures fit into their own AI roadmaps.
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