You are spending like crazy on AI assistants. Can you quantify it to your leadership?
Everybody says AI is 2-3x-ing their velocity. Nobody has actually gone in and looked at the metrics. Meanwhile the work is moving from writing code to reading code, and the stress lands on your senior engineers, who are either bogged down reviewing PRs or rubber-stamping them without reading through. Once a team grows past 15 to 20 engineers, this starts to clobber the whole process. I go in, audit your AI development workflow, benchmark it against the industry frontier, and help your team implement what is missing.
I was in the same boat
I was responsible for the software that supported the business, with agents writing a growing share of the code. The things I was actually afraid of:
- Leadership had seen what AI can do in a demo, and the expectation became that software gets written in hours, not days. The pressure to commit to timelines that could not be met, even with the AI assistance, was constant.
- The agents could produce more code in a day than the team could read in a week. So features went to production on unit tests, integration tests, and a happy-path manual check. The same old "it was working in my local," except this time nobody had even read the code.
- No human wrote the code and no human had truly read it, so nobody carried the why in their head. Two or three months into the AI-assisted workflow, the newest modules would already be dark boxes nobody could explain.
- And on the biggest agent PRs I caught myself doing it too: the tests looked reasonable, a real read meant hours on code no human wrote, I just wanted it merged, and I was hoping the other reviewer did not put a wrench in the plan.
Two things got us out of that boat.
The first: these models are probabilistic. Your team has likely written the guidance already: rules files, skills, prompts and commands rolled out to every engineer. But a model takes that guidance in without truly understanding it, and the same prompt on the same codebase, run five times, can end in five different states. Guidance alone will not hold, no matter how well it is written. It needs a computational backstop: linting rules, pre-commit and pre-push hooks, CI-based tests. Mechanical gates along with the probabilistic guides, so the results stay compliant even when the instructions are imperfect.
The second: ownership does not move. The engineer who opens the PR owns the code in it, agent-written or not, and the reviewer who merges it owns the approval. The healthy teams keep the same review discipline they had before AI and enforce it the same way. Nobody gets to say the agent wrote it.
How this works
A written application, 15–20 minutes if you take it seriously. No sales call, no drip sequence. I review every application personally and respond within 48 hours. If I don't think I can help, I'll say exactly that.
You send real artifacts: rules files, sample PRs, CI config, operational numbers. We spend an hour on a working call, and I score your setup across 20 areas of your AI development system.
A 75-minute walkthrough of a personalized report measured across 20 areas of your AI-assisted development workflow: the concrete industry frontier, where your peers are vs where you are, and a prioritized 90-day plan to bring you to that standard. You keep the report either way, and the $497 is fully redeemable against an engagement signed within 30 days.
Who this is for
People leading a team of 15 to 50 engineers at a Series A/B SaaS company: Directors of Engineering, VPs of Engineering, CTOs, or a business-savvy principal engineer who carries that responsibility. You are growing fast, shipping features fast, and using AI in the hope of increasing development velocity, but getting pulled down by incidents, or by lower throughput than you expected from AI-assisted development.
Why the diagnostic is paid
Most consultants run discovery for free, so the fee deserves an explanation. A free call produces a canned report that looks impressive on the face of it but does not drive you to action. This diagnostic is built the other way around: the intake collects your real artifacts, I go through your complete system before we ever get on a call, and everything you hear in the readout comes from your codebase, not a template. You leave knowing where you stand against your peers and exactly what to do about it. And if I conclude I cannot help you, the readout says that too. That is what you get for the $497, and it is fully redeemable against the engagement within 30 days.
Start with the application →