Everybody's rushing to adopt AI—but most of what we see is a mess of pilots, shadow IT, and compliance gaps. We've worked with chatbot companies sorting out GDPR, enterprises rolling out Copilot, and product teams building on LLMs. The pattern's familiar: figure out where AI actually helps, lock down the risks, then scale without the chaos.
Start with the basics
Before you build or buy, you need a straight answer: where does AI create value here, and where's it just noise? We run use case assessments that cut through the hype—aligned to your business goals, not a vendor slide deck. From there we set up governance and policy so decisions aren't ad hoc. Risk appetite, ethical boundaries, roadmaps that prioritise what matters. No fluff.
Then secure it
AI brings new attack surfaces—model integrity, training data, prompts, outputs. Prompt injection, supply chain issues, data leakage. We threat model AI systems the same way we'd do it for any critical system, but tuned for how AI actually works. Data protection for training data and outputs. Control frameworks that fit your stack, whether it's cloud, on-prem, or a mix of third-party models. And incident response that accounts for AI-specific threats, not just the usual playbook.
Make it stick
Adoption isn't just tech. It's people, process, and culture. Change management, workforce readiness, folding AI into your existing security and compliance programmes—we've seen it work when it's done properly, and we've seen it fail when it's an afterthought. We stick around for ongoing assurance as models, regulations, and use cases change. Because they will.
Who we work with
Product teams building with LLMs or ML. Enterprises rolling out Copilot or similar tools. Businesses that rely on AI from suppliers. We've done GDPR alignment for chatbot companies, AI strategy and security controls for larger orgs. If you're early—exploring pilots—or further along and trying to scale without breaking things, we adapt to where you are.