Industries
Six sectors. Each with its own regulatory framework, its own risk model, and its own definition of what failure means. We understand all of them from the inside.
All industries
Nuclear and low-carbon energy assets are among the most complex engineered systems in existence. They operate continuously, they age in ways that are difficult to model, and the consequences of unplanned events — whether safety, production, or regulatory — are severe.
The AI industry's standard answer — train a model, deploy it, iterate — is not adequate here. Every model that operates in or adjacent to a nuclear licensed site must be validated against the specific failure modes of that environment, documented to a standard that satisfies ONR scrutiny, and monitored continuously after deployment.
We have direct experience of the operational and information governance environments of UK nuclear licensed sites. We understand what data is available, what cannot leave site, and what documentation an intelligent system requires before it can be considered for deployment in a nuclear context.
Our nuclear AI work is built from the ground up against ONR's safety assessment principles. We do not treat regulatory compliance as a documentation exercise. It drives architecture decisions from day one.
All industries
The history of clinical AI is littered with systems that performed well on benchmarks and failed in wards. The reasons are consistent: models trained on non-representative data, systems deployed without clinical workflow integration, and AI that clinicians cannot interrogate when it produces an unexpected result.
Trust in clinical AI is not a communications problem. It is an engineering one. Systems must be explainable, validated against the patient populations they will actually serve, and designed to fail safely when they reach the edge of their competence.
We work within NHS governance frameworks from the outset of every engagement — not as a compliance exercise at the end. Our clinical AI systems are co-designed with clinical staff, validated against site-specific patient populations, and built with interpretability as a first-class requirement.
All industries
National security applications require AI that can operate in classified environments, on data that cannot leave controlled systems, with performance that degrades gracefully rather than catastrophically. Most AI providers cannot work in these environments at all.
The operational tempo of security and defence also demands something different: models that provide actionable intelligence quickly, with clear confidence estimates, and with audit trails that satisfy legal and oversight requirements.
Our core team holds SC clearance and has operated inside DSTL-affiliated programmes. We build AI systems designed for air-gapped environments, with full data sovereignty, and with the security architecture necessary for classified operational contexts.
All industries
Power grids, water systems, transport networks, and communications infrastructure share a common characteristic: they cannot be taken offline to fix a failing AI system. Any AI deployed in these environments must degrade gracefully, fail safely, and never create a dependency that the underlying system cannot survive without.
These systems also accumulate data over decades — often in formats that reflect the technology of the era they were installed, not the era we are operating in. Building AI on this data requires specialist knowledge of what it means and what it does not mean.
We design infrastructure AI with explicit safe-failure modes and with full understanding of the operational constraints on deployment, testing, and maintenance in live critical systems.
All industries
FCA and PRA oversight requires that AI systems used in financial decision-making — credit, fraud, trading, risk — can be interrogated, audited, and explained to regulators. Black-box models that perform well on backtests but cannot justify individual decisions are not compliant and are not deployable.
The EU AI Act's high-risk classification for AI in financial services adds a further layer of documentation and conformity assessment that most AI firms have not yet grappled with.
We build financial AI systems with explainability as a design constraint, not a post-hoc addition. Every model comes with complete documentation of its training data, validation methodology, and performance characteristics — ready for regulatory review.
All industries
AI deployed by government must meet a standard that goes beyond commercial effectiveness. It must be fair, auditable, explainable to citizens, and procured through processes that satisfy transparency obligations. These are not constraints to work around — they are the right standard for systems that affect people's lives.
The public sector also presents unique data challenges: legacy systems, fragmented records, and data that has been collected for purposes other than ML training. Building AI that performs well on this data requires experience that cannot be simulated.
We understand public procurement frameworks (G-Cloud, DOS, Crown Commercial Service) and design engagements that work within them. Our public sector AI systems are built with transparency and fairness assessment as non-negotiable requirements.
All industries
Tell us about your environment. We will tell you honestly whether we can help — and if we can, exactly how.