Your engineers are using AI wrong. We'll show you what right looks like.
Most engineering teams have AI coding tools and a few agent experiments in flight — and a workflow built for the pre-AI era. We come in, audit, redesign, build the custom skills and CI gates, and leave the team running on a workflow that actually keeps up with the tools.
A workflow that matches the tools, not the tools forced into the old workflow.
Custom agentic skills, agents, and hooks built against your codebase, your conventions, and your CI.
Evidence-bound PR pipelines so AI-authored code can't degrade quality unnoticed.
Mechanical enforcement, not instructions — gates that stop bad changes at the tool layer, not in a review comment.
A team that can keep operating the system after we leave.
Custom agentic skills tailored to your codebase. Plugins, MCP servers, agent definitions, hooks, and a CI pipeline that gates AI-authored changes through automated review, adversarial review, and mutation testing where appropriate.
The toolkit underneath is the same one we use ourselves — a fifty-skill set covering plan reviews, ship-checklists, deploy gates, design QA, security audits, code review, and retrospectives.
Custom does not mean from scratch. We bring the toolkit. We tailor it to your codebase, your conventions, and your existing CI.
Scoped per team. Typically: a week of audit, two-to-four weeks of build, a week of training and hand-off. Pricing is discussed on the first call.
A senior engineer who knows the codebase and is willing to spend half a day a week with us during the audit and build.
Access to a representative repository, CI, and a sample of recent PRs.
Buy-in from the team. AI-augmented workflows fail when adopted by mandate. We work with people who want to be working this way.
Do you write our production code for us?
No, not as the main engagement. The point of the audit is to make your team faster, not to make us a permanent dependency. We will write code as part of building the custom skills and CI gates, but the production work stays with you.
What if our team is already using AI tools well?
Then the audit is shorter. We tell you on the first call whether we'd be wasting your money. We have walked away from engagements where the team was already operating well.
Will this work regardless of which AI coding tool we are on?
The patterns transfer. The toolkit specifics do not. The custom skills are written for the tools your team uses; we adapt to whichever AI coding stack you've adopted. We have opinions on which tool fits which team.
How do we measure the result?
We baseline before we start: PR throughput, time-to-merge, escaped-defect rate, code-review depth. We re-measure at the end. If we didn't move the numbers, the engagement was a failure and we say so.
Can we keep working with you after the engagement?
Yes, on a retainer for ongoing skill development and tooling. Most teams do not need this after the initial engagement. The point is to leave you self-sufficient.
Bring a hard problem. Leave with a system.
The first conversation is a thirty-minute call. We tell you in plain English whether AI will help your business — or whether you're being sold a fantasy.