Make your engineering team actually fast with AI. Past the licence, into the workflow.
Most teams have a Copilot seat and not much else. We bring an opinion: which tools to use for what, the patterns that work in real codebases, the guardrails that keep quality up. Then we check back at 90 days against numbers you care about.
The work, in four parts.
01
Tooling, with an opinion
Cursor for greenfield, Copilot for in-IDE completions, Claude Code for repo-scale changes. We pick what fits your stack, not what the vendor pitch said.
02
Patterns that work in real codebases
Spec-driven prompting. Diff review discipline. Test-first AI coding. Refactor loops. The patterns we teach are the ones we use ourselves shipping production software.
03
Guardrails for quality
Codeowner rules, eval routines for AI-generated code, prompt libraries kept in-repo, lint rules for AI hallucinations. So speed doesn't come at the cost of the bug rate.
04
90-day check
We come back at 90 days and look at cycle time, defect rate, and developer confidence — measured against a baseline we took at week zero. If something isn't sticking, we adjust.
Concrete things, in your hands.
Every engagement leaves you with artefacts you own — not slideware. So you can keep going without us.
- A scoped rollout plan: which tools, which patterns, in which order
- Hands-on workshops with your engineers, on your codebase
- Prompt libraries and snippets kept in-repo, version-controlled
- Guardrails: codeowner rules, eval routines, lint additions
- A baseline taken at week zero and re-measured at day 90
- A written report at day 90 with what to do next
Common use cases.
The teams and the jobs we tend to see first. Yours might not be on this list — that's what the call is for.
Who
Platform engineering
Job
Migrations and refactors
Payoff
Repo-scale refactors that used to take a sprint compressed into days, with a review process people trust.
Who
Product engineering
Job
New feature throughput
Payoff
Test-first AI coding patterns that lift PR throughput without lifting the defect rate.
Who
QA + SDET
Job
Test generation and triage
Payoff
AI-generated tests reviewed against eval sets, plus triage workflows for flaky-test backlogs.
Who
Engineering leadership
Job
Measuring what actually changed
Payoff
Cycle time, defect rate, and confidence measured at zero and 90 days — so the AI line item has numbers next to it.
Where we are not the right fit
If you want a one-off lunch-and-learn that ticks the L&D box, we are not the right people. Our training is opinionated, hands-on, and only useful if your team is willing to change how they work.
Free 30 minutes. No deck. Just your team's reality.
You tell us how your engineers are using AI today. We tell you whether a rollout would move the numbers, what it would look like, and what the 90-day check would measure.