
AI Chief of Staff
Aoife
Holds the operating cadence — weekly priorities, decision logs, follow-ups — so the founder gets time back without losing context.
The team
Twelve specialist AI teammates with names, roles, and integrations. Each one holds a defined job, runs inside your real stack, and reports up to a human owner.












The roster
Scroll to meet the team. Each card opens into a detailed teammate brief below.
Inside each teammate
Each AI teammate runs inside the systems you already use. Here's how the full workforce shows up day to day.












The shift
Most teams are still buying tools. The real value comes when AI has a role, an owner, context, and a place inside a governed workflow.
AI tools
Ad-hoc prompt work depends on whoever is at the keyboard and rarely survives into a repeatable operating model.
AI teammates
Teammates hold jobs like research, briefing, reporting, content, and qualification — with clearer ownership and expectations.
Connected workflows
Inputs, handoffs, review steps, and governance get embedded so the system becomes useful beyond a single individual.
Foundations
You cannot skip the operating foundations and expect the system to hold. That is why the capability work comes first.
Maturity model
Most SMEs are somewhere between stage one and stage two. The goal is not to rush — it is to keep moving in the right order.
01
AI tools
Ad-hoc prompt usage with no durable operating model.
02
AI teammates
Named specialist roles with context and ownership.
03
Connected workflows
Handoffs, reviews, and systems wired into real work.
04
Work-chart organisation
Teams redesign around human + AI execution.
The operating mindset
This is not AI replacing people. It is people directing and reviewing a set of specialist AI roles with stronger visibility into who owns the work, how it moves, and where it gets checked.