Direct accountability
The person helping you choose the use cases is the same person staying close to architecture, governance, and rollout.
Arvanu is not a small agency trying to sound larger than it is. It is a founder-led practice built around a simple idea: some companies need one person to own the early AI decisions all the way through delivery, without a six-month executive search or a rotating consultancy team.
You work directly with Michael Kilty. The point of the model is direct accountability, fast decision-making, and enough hands-on execution to keep the first production outcome honest.
I stay close to architecture, governance, tooling decisions, and the real workflow constraints that determine whether AI gets used.
With a solo practice, saying no matters. If the company needs a large engineering partner, a pure training program, or a big-brand consultancy, I would rather say that early.
The ideal client is far enough along to have meaningful workflow pain, but early enough that a full-time Head of AI still feels premature.
In regulated and trust-sensitive environments, the real problem is rarely access to AI tools. The problem is deciding what should be automated, who owns the risk, what has to be reviewed by humans, and how the system survives real operational use.
That is why the site focuses on regulated European companies. It is narrow enough to be credible and specific enough to differentiate from broad “AI transformation” positioning.
The person helping you choose the use cases is the same person staying close to architecture, governance, and rollout.
I focus on regulated and trust-sensitive environments because that is where judgment matters most and AI mistakes are expensive.
The work is not finished when the model responds. It is finished when the workflow, approvals, monitoring, and ownership model hold up in real use.
A good engagement should reduce dependency over time. The end state is not permanent reliance on an external consultant.
If you have a workflow in mind, a stalled initiative, or uncertainty about whether this model fits your stage, that is enough.