AI-assisted delivery

AI is already writing your code. This is the gate it writes inside.

Google’s DORA study of AI-assisted teams found the pattern: throughput up, stability down. Code arrives faster than review, testing and deployment can absorb it. The roadmap moves and the release calendar slips in the same quarter. We put the gates in first, then roll the tools out behind them, so your team keeps the speed and you get the evidence that the code still holds.

See it in our work

At a glance

Length
Six to twelve weeks
Format
Pairing on real tickets, not training
Baseline
Your four DORA numbers, before and after
We step back
When the numbers hold without us

What it covers

Gates first, then tools.

  • The gates, before the tools

    Tests, coverage thresholds, mutation testing on changed code and a code health score that runs on every change. AI can draft whatever it likes. What survives is decided by the pipeline, not by a tired reviewer.

  • The tools, rolled out properly

    Claude Code, Cursor or Copilot set up on your repositories with your context, your conventions and your guardrails committed alongside the code. Configured once for the team rather than twenty five times in private.

  • Evaluations for what you prompt

    The prompts, skills and agents your team writes are code too. They get a test suite, so a change that improves one case and quietly breaks three others is caught by the suite rather than by a customer.

  • A governance story you can hand over

    What the tools may touch, where the code goes, what is logged and who approved it. Written for the audit and the procurement questionnaire, not for a slide.

How we do it

From the baseline to the same numbers again.

  1. 01

    Read the repository first

    A diagnosis of where delivery actually stalls, and a baseline of the four DORA numbers. Without that baseline there is nothing to compare against when the tools go in.

  2. 02

    Put the gates in

    The pipeline gets the tests, the thresholds and the health scoring before a single licence is handed out. Speed without a gate is the thing that broke the stability figure in the first place.

  3. 03

    Roll out, paired not lectured

    Our engineers sit with yours on the tickets already in the sprint. The conventions that work get committed to the repository so the whole team inherits them.

  4. 04

    Re-read the numbers, then leave

    The same four DORA figures against the baseline, plus the health trend. We step back when they hold without us.

What you get

Running on your pipeline, owned by you.

  • A baseline of your four DORA numbers, and the same numbers after
  • Quality gates running on every change, on your own pipeline
  • AI coding tools configured on your repositories, with conventions committed
  • An evaluation suite for the prompts, skills and agents your team wrote
  • A written policy on what the tools may touch and what is logged

Where we have done it

Real engagements, named.

Questions

Questions we get asked.

Our developers already use Copilot. What does this add?
Almost certainly the gates, and the evidence. Most teams adopt the tool and keep the review process they had before. That is why throughput rises and stability falls. This puts the mutation gate, the coverage threshold and the health score in the pipeline. Whether the generated code is good enough stops being a judgement call at merge time.
Is this the same as Practices coaching?
No, though they end up in the same place. Practices coaching is for a team without test driven development and continuous delivery yet. This is for a team already shipping fast with AI, watching the change failure rate go the wrong way. If you are not sure which you are, the repository diagnosis at the start of either will tell you.
Which tools do you roll out?
Whichever you have bought, or whichever the diagnosis suggests. We use Claude Code, Cursor and Copilot ourselves and have no commercial interest in any of them. The gates matter far more than the choice, and they are the same gates whichever tool your team types into.
Do you need access to our codebase?
For the diagnosis, read access to the repository and its history. For the rollout, the same access any engineer joining your team would get. Everything runs on your own pipeline and your own accounts, and nothing is copied out.

What clients say

In their words.

01 / 04

Life Insurance Direct
Before Metricsense, checking calls was a manual process: we selected files for review. Now the platform analyses every call, and we review by risk. It has identified customer experiences we would not have picked up before, and it lets us scale our quality assurance program without increasing headcount, with our team still the human in the loop. It helps us protect our customers, our business and our agents.

Russell Cain

Founder and CEO, Life Insurance Direct

Also under product development

Where it leads next.

All of product development

Not sure this is the right place to start?