The approach

AI opened the door. You still have to walk through.

How it works

Intro call

Goal: customize the training to you
Where you are now and how you currently use AI
Who it is for — team or leadership, what level, their processes and bottlenecks
We define the learning goals together
You get the program outline right after

Training

I get to know your team and how they work, where they lose time, where the bottlenecks are
We fill the gaps with the concepts they are missing
We build what fits your goals: a knowledge base, an agent, governance guardrails, etc. See examples below

What you walk away with

Depending on the training goals

A Claude Cowork workspace and knowledge base
A set of skills and scheduled tasks
A governance map — who is responsible for what
Shared guardrails and policies
A metrics dashboard
An action plan with next steps (key processes to automate, key decisions to make)

One day a week, six months

Fractional AI Partner

Weekly calls with your team, going deep into how they actually work. We restructure workflows end to end, install and connect tools. Building and learning happen at the same time — key concepts get covered as we go, so the knowledge stays with the team. You end up as an AI-native company.

Book an intro call

Examples of what we can work on

Sessions are tailored to where your team is. Pick the level that fits.

Intro to Claude Cowork
Identifying repetitive tasks and setting priorities
Which processes to convert to an AI agent
Autonomous agent, agent with subagent, human in the loop — what's best?
Connecting tools
Agents, subagents, skills, commands, hooks
Token management

These are examples only — the actual concepts will be shaped together at the intro call.

Where teams get stuck

Most common bottlenecks

Graveyard of agents

Most teams jump in, spin up a bunch of agents — and six months later, half of them are dead. A graveyard nobody visits. Why: bad output, something broke and nobody fixed it, or the process never should've been automated in the first place.

Seemingly easy, but not easy

AI lowered the bar, it didn't remove it. There's still no shortcut up there — you actually have to learn this stuff.

Most use cases are generic

The use cases on YouTube are generic on purpose — built to fit everyone, which means they fit no one exactly. Your business and your process have their own quirks, and generic templates don't stretch that far.

AI creates new bottlenecks

Spinning up an agent takes five minutes. But most AI agents create new bottlenecks — tool sprawl, review overhead, fragmented processes, maintenance. Teams lose a full workday a week to this. The trick is making AI run smooth — like a carrousel ;)

Data is messy

Data is king, knowledge base is close behind. If it's messy, duplicated, outdated, or contradicting itself (happens more than you'd think), your agent has nowhere solid to pull from. Garbage in, garbage out.

Overusing or underusing autonomous agents

Most of the time we don't even want to hand over the decision — in those cases, autonomous agents are a terrible idea. Build a smooth human-in-the-loop process instead. But the flip side happens too: teams double-check everything the agent does. A little trust issue. And with zero trust, you save zero time.

Unclear responsibility

Who owns this? Who approves a new agent? Who's on the hook when it messes up? Who updates it, who kills it once it's not needed? And if leadership never turns this into a real strategy with real resources behind it... oups.

Token burn

AI isn't free, and tokens disappear faster than you'd think. There are ways to burn less — you just have to know them and actually use them.

Tell me where you are at

A brief call to see if we are a fit, discuss your needs, and set up a plan.

Book an intro