Announced under a newly signed memorandum of understanding, the programme aims to reduce the UK’s reliance on foreign-owned hyperscale cloud and AI providers by delivering models that are entirely trained and hosted on domestic infrastructure.

The move comes as policymakers and industry increasingly focus on data residency, security and geopolitical risk linked to overseas-controlled platforms.

Project Mercury is positioned as a direct response to the UK’s ambition to become a producer, rather than a consumer, of advanced AI technologies. It also aligns with the government’s £500 million Sovereign AI Fund, which is designed to accelerate domestic capability across infrastructure and model development.

At its core, the Mercury Series will comprise a range of large language models (LLMs) built to meet the requirements of both public and private sector users. These models will be deployed via Civo’s sovereign cloud platform or hosted on-premise, ensuring full control over data and compliance with UK regulatory frameworks.

The portfolio will span from lightweight “Edge Intelligence” models, ranging from 0.8 billion to 30 billion parameters for low-latency, local applications, to a 256 billion-parameter “Frontier Power” model designed for complex generative AI workloads. All models will be trained within the UK and powered by renewable energy, reflecting a parallel focus on sustainability alongside performance.

The initiative addresses longstanding concerns that the UK has operated as a “digital tenant”, dependent on infrastructure and AI systems controlled by companies headquartered abroad. By establishing a domestic alternative, Project Mercury aims to mitigate risks associated with data sovereignty and service dependency, while strengthening national capability in critical digital infrastructure.

James Drayson, CEO of Locai Labs, described the partnership as a pivotal moment for the UK’s AI ecosystem, highlighting the creation of a secure, high-performance and homegrown platform. Mark Boost, CEO of Civo, emphasised that the collaboration demonstrates the UK’s ability to develop and operate sovereign AI infrastructure independently, reducing reliance on foreign providers.

Beyond public sector use cases, the models are expected to support enterprise applications across industries including finance, healthcare and engineering, where secure and compliant AI deployment is becoming increasingly important.

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