We design the platform, build the agents and advise on where AI pays off, with security and data protection as constraints from day one.
Useful AI in an organisation is mostly plumbing: where the data lives, who may see it, which model may process it, how the output is checked and logged. We architect that plumbing on Azure, AWS and Google Cloud and build on OpenAI and Anthropic models, choosing per use case.
We build agents that do work, not demos: they retrieve from your documents, draft, classify and act, with a person approving where the stakes require it.
The secure foundation AI workloads need.
Assistants and agents built on OpenAI and Anthropic models.
Decide where AI pays off and how to do it safely.
What we design, build and operate when an organisation wants AI inside its own cloud, with its own access controls.
Two to four weeks of interviews and data review, ending with prioritised use cases and a risk view for each.
One agent, one team, real data, measured against a baseline. Security and logging are in place from the first day.
Hardening, evaluation, cost controls and handover or ongoing operation by our team.
The platforms and tools behind the practice.
We use enterprise and API tiers where providers commit not to train on your data, and we review the contract terms with you before any data moves.
OpenAI and Anthropic models, accessed directly or through Azure, AWS and Google Cloud. The choice is made per use case on quality, cost, latency and where the data may be processed.
A chat interface is sometimes the right front end, but the value is in the agent behind it: retrieving the right records, drafting the right document, and acting with approval.
Data protection impact assessment, data minimisation, access logging and a risk classification for each use case. Legal interpretation remains with your counsel.
Systems and applications built to be operated: secure by default, cloud-native and maintained by the team that wrote them.
Infrastructure that is monitored, patched, backed up and documented, so downtime is planned rather than suffered.
An AI assessment ends with a shortlist of use cases, each with its data, risk and cost picture.