Problems we have solved

A company running one AI platform for many enterprise clients needed agents that act within strict per-client bounds. Each client gets its own isolated deployment.

The agents answer questions and take actions across email, chat, call notes, campaign metrics and CRM data. One client's data must never reach another client's agents.

We designed and built the agent layer, writing the code with the company's engineering teams.

We decided which agent actions run on their own and which wait for a person's approval, and gave each tool an agent can call its own permissions.

We designed how each client's data is kept apart, so one client's information never reaches another client's agents.

We defined how the platform connects to each client's systems, and used open protocols for agents' access to tools and for agents working across systems.

The platform runs in production for a global enterprise client. Each new client gets its own agents on the same shared core, in an isolated deployment.

A consumer AI product team was about to open its assistant to a closed beta. It had a working prototype and a launch timeline, but limited confidence in the quality of the answers, the security of the system and its readiness for regulation. It wanted an independent view before deciding.

We reviewed the whole system, from how information comes in to how answers go out. We recommended changes to how the assistant finds and ranks information, a way to record consent and licensing for the content it draws on, protection against the risks specific to AI assistants, reliability targets, and measurable bars the assistant had to clear before launch.

A written review and a phased four-month plan, which fed the team's go/no-go decision for the closed beta.

An early-stage healthtech team was applying for a public research loan for a radar system to monitor elderly people at home. Before committing capital, the funder needed independent evidence that the AI parts could be demonstrated within a proof-of-concept timeline.

We assessed only the AI layer, separately from the hardware and software decisions. We reframed the system's intended purpose so it matched what the evidence could support (monitoring and alerting, not diagnosis), and re-scoped the proof of concept around what published evidence showed could be demonstrated.

A confidential feasibility report rating each AI component as low, medium or high risk, a focused validation plan with measurable acceptance criteria, a privacy-by-design data architecture aligned to GDPR, and a phased roadmap for the work after funding.