Industry
Sovereign AI for insurance
AI over policyholder, claims, and underwriting data that stays inside your perimeter, owned and auditable.
By James Drayson
In short
Sovereign AI for insurance is a model an insurer owns and runs inside its own perimeter, trained on its policies, claims, and underwriting knowledge, so sensitive policyholder data never leaves the environment, meeting the residency and audit requirements general-purpose APIs cannot.
The data problem in insurance
Insurers process large volumes of sensitive personal, medical, and financial data across underwriting and claims. Sending that data to a general AI API raises residency, privacy, and audit concerns that are difficult to reconcile with regulatory duties.
Why public APIs fall short
- Privacy exposure: Special-category data sent to a third party increases regulatory risk.
- Residency: Data may leave the jurisdiction it must remain in.
- Generic models: A general model doesn't understand your products, policies, or risk models.
What owned AI enables for insurers
- In-perimeter: Underwriting and claims inference stays inside your environment.
- Domain-trained: A model post-trained on your products and claims history reasons in your context.
- Auditable & owned: You hold the weights and audit trail for regulatory assurance.
What this looks like with Locai
In a regulated sector the hard part is rarely the technology; it is procurement, deployment and accountability. A single owned machine simplifies all three.
Locai Labs builds Locai One, an on-prem AI appliance. It is one machine that arrives with everything already in it: the hardware, our open-weight Locai Juno models, and Locai OS, the operating system that serves the models and handles users, access and monitoring. You plug it into a mains socket and your network, and your team is working in about 15 minutes. No cloud account, no per-token bill, and nothing leaving the building.
The reason a data-centre-class model fits in a box on your floor is SPACE, our compression algorithm. Instead of asking how much of a model can be cut while keeping it broadly similar, SPACE asks what the model needs to be good at, preserves the subnetworks behind those capabilities and strips back the rest. The result is a smaller specialist rather than a shrunken generalist, tuned to the exact hardware it ships on.
Locai One starts at £29,950 for a team, and Locai One Pro at £49,950 for an organisation, bought once and owned outright. Both run air-cooled on standard mains power and work fully air-gapped. Any compatible open-weight model runs alongside Juno, and if you need a model trained on your own proprietary data we can post-train one and deploy it on the same machine.
Frequently asked questions
Is AI safe for insurance data?
When the model runs inside your perimeter and you own it, sensitive policyholder and claims data never leaves your control.
Does it support residency and audit needs?
Yes, onshore processing and owned weights support data-residency and auditability requirements, with documentation for procurement.
Can it be trained on our products?
Yes. The model is post-trained on your policies, claims, and underwriting knowledge so it reasons in your context.
Can it run on-prem?
Yes, on-prem, private cloud, air-gapped, or in a UK sovereign cloud.
Book a sovereign AI briefing
A 30-minute session on owning your model: deployment options, the data path, and a clear cost range for your use case.