AI changes what one person can do.
Be ready, so when AI can do something new, you can too.
Prefer this page without animation? Read the simple version.
A line loops upward in a widening coil. Each loop is a session with Elliot, and each one is wider and higher than the one before. The top loop is drawn in ember, and a faint line leaves it, heading up and out.
The shortest path up the learning curve
AI changes more than the tools we use.
It changes how we think, work, organize, and lead.
Most AI courses teach you how to use today’s tools and capabilities for a handful of tasks.
We teach what lasts: the ideas and habits that help you explore your environment for new capabilities that may not have been available yesterday.
So you grow with AI instead of chasing it.
Two lines over time. Chasing the tools gives the same short burst with every new capability, then drops back. Growing with AI compounds past it and keeps climbing.
Coaching for individuals
One to one with individuals and small teams.
You do not need a technical background. The coaching starts from the work you already do, whatever your role.
You meet with Elliot regularly, so he can get to know how you work, keep track of what you are trying to do, and make each conversation build on the last.
Bring the questions, decisions, and ideas in front of you. Or just bring yourself, and Elliot will teach you what is new since the last time you talked, or something old you have not learned yet.
One line, drawn left to right. It arrives tangled in loops: unsure what is possible. It turns ember and circles the flame once, a session. It leaves straight, ending in an arrow: with a direction.
Training for engineering teams
Each new model can do more, and some old habits start to cost you.
Elliot trains engineering teams in agentic engineering: building software with AI agents that plan, write, and verify code.
When a new model drops, the team that learns what it can do first has it before its competitors do. A team that keeps the habits it formed on the last model runs into problems it did not have before.
One example: with the older Claude Opus models, Elliot never set thinking effort below extra high. With the current ones, high effort costs more and drifts from what you asked, and low or medium effort gives better answers. A team still on its old settings pays more for worse work.
Using AI the same way for months is a risk. We teach the habits that keep a team current: picking up each new capability early, and reviewing how you work every time a new model drops.
Four new models arrive one after another. After each one, a trained team, drawn in ember, climbs a step. A team that keeps the same habits falls into a pit at each new model, marked with a cross, and climbs out a little lower. The gap between the two grows with every model.
Building it yourselves
Prefer to build your own software?
We also build alongside individuals and small teams, and teach them to develop software the AI-native way.
Whichever suits you, we would be glad to help. See the software we build for businesses, or start a conversation and tell us what you have in mind.