Developers
Prepare repository context, turn specs into bounded changes and review AI-assisted code against the intended behavior.
AI TRAINING · AMOTION
Make AI part of the way your team works.
Learn on the work in front of you.
Explore practical training through our sister company Amotion, with guided labs and coaching for the people who plan, build, test and operate your software.
Capability comes from practice.
We assess how each role works today, then build a program around the gaps. Trainer-led labs, paired exercises and office hours give people repeated practice with approved tools and selected repositories.
Your team does the work. Our trainers guide the learning.
What your team learns.
QA and operations
Practise test design, defect investigation and release checks, with people responsible for accepting the evidence.
Product and delivery
Turn source material into requirements and acceptance criteria, then keep specs and decisions connected in Linear or Jira.
Technical leads
Set review expectations, agree tool boundaries and coach colleagues using shared guides and exercises.
How an engagement runs.
Understand the team
Establish the tools, technology stack, roles and starting capability.
Learn and practise
Combine short explanations, coached labs, exercises and office hours.
Apply and review
Review submitted exercises, address gaps and leave reusable guidance for the team's next tasks.
A clear scope from the start.
The detail follows discovery. The work begins with a shared understanding of the task and who owns each decision.
- Role-based training and coaching
- Practical labs and independent exercises
- Four-to-eight-week programs, adapted after discovery
- Materials and practices your team can reuse
A few practical questions.
Who takes part?
Engineering teams are central. Product, QA and operations join the sessions that match their responsibilities.
How is the calendar decided?
Discovery establishes the starting point. Programs usually run four to eight weeks, with two-to-four-hour sessions agreed in the calendar. Development, testing and deployment receive the most practice; other topics follow the team's needs.
What does readiness look like?
We review three things: people can complete representative tasks with approved AI tools; they can follow the agreed workflow through review; and they can prepare usable repository context and instructions. Exercise evidence shows where more practice is needed.
Does training include production implementation?
Training uses reference repositories and agreed practice tasks. Your team decides how to apply the learning in its organization, with guidance during coaching. Production implementation is a separately scoped engagement.
LET’S PUT AI TO WORK
Bring us the work that gets stuck.
One workflow. The systems it touches. The outcome you want to change. Let’s work out a useful first step together.
Discuss your workflow