Ship faster with AI-assisted development
Most development teams have not yet integrated AI into how they build. The shift changes what the same team can deliver: faster shipping, fewer defects, cleaner code. We train your developers, set up the infrastructure that makes it work, and measure the gain.
Level up your teamWho it's for
Engineering organisations whose teams still build the pre-AI way and want a measured, disciplined path to AI-assisted delivery: CTOs, VPs of Engineering and platform leads.
What we deliver
Training, infrastructure and measurement, so the speed-up is real, safe, and provable.
Hands-on developer training
Practical training on your own codebase, not generic demos: how to use AI tools well, and where not to trust them.
Supporting infrastructure
The tooling, guardrails and workflow that make AI-assisted development safe and repeatable across the team.
Review and quality discipline
Patterns for reviewing AI-generated code so speed does not come at the cost of defects or maintainability.
Measurement
We baseline and then track shipping speed, defect rate and code quality, so the gain is a number, not a feeling.
Guardrails for AI in the pipeline
Scoped access, audit and safe automation so AI agents in your SDLC can't cause the incidents we write about.
A rollout playbook
A documented way of working the team keeps after we leave.
How it works
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Baseline
We measure how the team ships today (cycle time, defect rate, review load), so the gain is provable.
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Train on your code
Hands-on sessions on your actual codebase and workflow, covering both the leverage and the failure modes.
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Set up the infrastructure
We put the tooling, access controls and review patterns in place that make AI-assisted work safe and consistent.
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Measure the change
We re-measure against the baseline and adjust where the gain isn't showing up.
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Hand over the playbook
The team keeps a documented way of working, not a dependency on us.
Proof
When the marginal cost of writing code collapses, the constraint moves to judgment, review and infrastructure. That is exactly what we set up, so faster shipping doesn't mean more defects.
Questions we get asked
Which AI tools do you train on?
The ones that fit your stack and constraints. We are tool-agnostic and focus on the workflow and judgment that make any of them pay off, so the training survives tool churn.
Won't AI-generated code hurt quality?
It can, without discipline. That is why the training pairs speed with review patterns and guardrails, and why we measure defect rate, not just velocity, before and after.
How do you measure the gain?
We baseline shipping speed, defect rate and code quality up front, then re-measure after rollout. If the number doesn't move, we adjust.
Is our code sent to a third party?
Only if you choose tools that do that. Where confidentiality matters, we set up workflows, including self-hosted models, that keep your code in your environment.
How long does it take?
Most teams see a measurable change within a few weeks of focused training and infrastructure work. We scope it to your size and stack.
Related
Want to know what your current team could ship with AI?
Start with a baseline