Training technology teams to build with AI
AI coding tools can improve delivery, but only when your developers know how to use them well. We help teams adopt AI in a structured, practical way that improves output, builds confidence and keeps quality under control.
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Preparing your team for AI-augmented delivery
AI coding tools are changing how software gets built. But giving your team a licence and hoping for the best is not a strategy. Developers need structured support to move from writing every line manually to directing AI effectively, and your codebase needs to be ready for that shift too.
WAt Propel Tech, we help development teams adopt AI tools in a way that is practical, progressive and grounded in real delivery.
Want to understand how ready your team is for AI-augmented development?
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Get in touchHelping development teams across sectors work with AI
We work with in-house teams and technical leadership across manufacturing, logistics, property, utilities and business services, helping them adopt AI coding tools with confidence.
OHow we train teams to write code with AI
01. Initial assessment: is your codebase AI-ready?
Before your team writes a single AI-assisted line, we assess what they are working with. We review existing repositories for code quality, structure, documentation and consistency. This matters because AI tools work from context. The better the codebase, the more effective AI-assisted development becomes.
02. Tool selection based on AI fluency
Not every developer is at the same stage, and not every AI tool suits every team. We assess the AI fluency of your developers, from those who have only used basic autocomplete through to those already experimenting with coding agents, then recommend tools that match their experience, working style and delivery needs.
03. Structured onboarding and rollout
We onboard developers with hands-on training built around practical workflows, using your real codebase, your real tasks and the tools your team will use day to day. We also set realistic expectations about where AI works well and where it still falls short. Then we guide teams through a deliberate rollout path. It start with low-risk tasks and teams move into small features in existing systems before progressing into larger and more complex enhancements.
04. Helping teams to thinking bigger
This is where most AI training programmes stop, and where ours starts to make the biggest difference. Developers need to think at a higher level. That means writing clearer specifications, giving better instructions, evaluating output more critically and approaching delivery with stronger system-level thinking.
05. Experimentation and rapid concept development
One of the biggest shifts AI creates is that experimentation becomes far quicker and far cheaper. We help teams get comfortable with that cycle. If the specification is wrong, it is often better to revise it, generate again and restart with clearer direction than to keep patching a poor output. Teams that learn to specify, generate, evaluate and iterate in a disciplined way get far more value from AI.
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Wil Jones, Technical Director, Propel Tech
Why structured AI training matters
Adoption that sticks
Giving developers a tool without context often leads to weak usage or abandonment. Structured onboarding and realistic rollout build habits that last.
Matched to your team
Different developers need different tools and different levels of support. A one-size-fits-all approach rarely works.
Grounded in your codebase
Training against your actual repositories helps developers understand how AI behaves in your environment, not in a sanitised demo.
Measurable progress
Clear success criteria make it easier to see whether adoption is improving delivery, not just generating interest.
Risk managed
Starting with lower-risk work and progressing in stages helps teams build confidence without exposing critical systems during the learning curve.
