Positioning
Start where your company actually is
Some teams need a shared baseline. Some already know where the friction is and need function-specific help. Others have early AI use already and now need standards, review logic, or clearer workflow design. This offer is built to meet companies where they are, not where AI marketing says they should already be.
Ways of working together
Consultation first, labs when useful
Consultation
Advice, workflow design, and implementation guidance
For founders and teams that need clarity before training: which tools fit which job, how workflows should change, and where AI actually helps.
- Clarify which tools fit which kinds of work.
- Map workflows, handoffs, review points, and approval logic.
- Design AI-supported operating patterns and pipelines.
- Establish usable workflows before asking staff to adopt them.
Labs / Training
Live, small-group learning around real work
For teams that need hands-on training, guided adoption, and a format that makes the workflow understandable enough to keep using.
- Available on-site or remote.
- Best for small groups of up to five people.
- Built around real tasks, not generic prompting theatre.
- Useful both as a starting point and after consultation work.
Common starting points
Where most companies begin
A typical path looks like this: a founder comes in with tool confusion, scattered experiments, or a workflow that already feels heavier than it should. We clarify the situation together, design a more useful AI-supported workflow, and then move into a live lab so the team can learn it by actually using it. The result is usually less noise, more confidence, and a setup that feels more workable than before.
01
Foundation Lab
A broad introduction any company can book to build shared understanding and identify the first genuinely useful opportunities.
- Builds a correct-enough understanding of how AI works.
- Reduces fear and false expectations.
- Surfaces the first low-risk, high-useful starting points.
- Prepares the ground for deeper workflow or function-specific work.
02
Workflow Consultation
For founders and owners who need a clearer operating picture before they train anyone.
- Clarifies workflow priorities and tool fit.
- Defines review logic, approvals, and handoffs.
- Translates ambition into a governed implementation path.
- Creates a clearer basis for later team training.
03
Function Labs
For marketing, sales, HR, operations, product, service, or mixed function groups that need role-specific practice around real work.
- Moves from general awareness into real tasks.
- Designs workflows around actual material from the job.
- Improves role-specific review habits and handoff logic.
Follow-through
What can come next
Once the direction is clearer, the work can deepen into leadership alignment, wider team adoption, or more advanced supervised builds. The important part is that the later steps stay connected to the workflow logic established at the start.
04
Leadership-Led Rollout
For founders, managing directors, and department heads who want clearer leadership alignment and early proof of value.
- Builds leadership workflows for briefing, synthesis, and decision support.
- Aligns selected functions around practical usage.
- Creates a more coherent adoption path across the business.
05
Team Adoption and Staff Training
For organizations with early AI use that now need consistency, standards, and staff who can actually use the new workflow well.
- Makes current practices visible.
- Defines review, approval, and escalation logic.
- Builds a lightweight playbook for daily use.
- Trains staff inside the workflow, not outside it.
06
Specialist Builds
For smaller advanced groups ready for supervised automation or agentic workflow work.
- Explores higher-leverage workflow builds.
- Keeps ownership, checkpoints, and maintenance logic visible.
- Avoids turning advanced work into theater.
Difference
Created for usability, not just possibility
My design background changes how I shape these offers. I think in user journeys, mental models, friction points, and adoption barriers. I care about whether a workflow is understandable, teachable, and sustainable, not just whether it looks clever in a demo. That means the offer is built not only around AI capability, but around system usability for real teams.