An engineering team that runs well.

Team structure

Who owns what, how teams are split as you grow, and how they work with each other.

Ways of working

Planning, code review, written proposals and decision records, regular 1:1s and feedback.

Standards and policies

What done means, how code is tested and reviewed, how incidents and on-call are handled, and how AI tools may be used.

DevOps capability

Teams that ship and run their own services, with automated pipelines and delivery measured the same way everywhere.

Hiring and career growth

An interview process, a career ladder people can read, and mentoring that moves engineers up it.

Founders and technology leaders whose team has grown faster than its habits. Companies that have engineers but no senior engineering leadership yet. Teams bringing in AI coding tools who want rules that keep quality and security intact.

Our founder has led engineering teams for more than five years. In that time he scaled one team of 20 engineers into four specialised teams, designed a company-wide interview process and career ladder, and founded a mentorship programme that enrolled more than 100 engineers.

He also cut on-call downtime by half by improving how incidents were handled, and brought AI coding tools into the daily work of several teams.

By what the team delivers and how it feels to work there. We use the four DORA measures: how often you deploy, how long a change takes to reach production, how often a release fails and how fast you recover.

The numbers are for the team to learn from. They are not for ranking individuals.

AI tools now write a growing share of code, which makes standards matter more, not less. We help teams adopt them with clear rules for review, testing and security, and with shared skills and agents that take routine steps out of the development cycle.

A written structure your team owns: who is responsible for what, the working agreements, the standards and policies, and a plan for putting them into practice.