I replaced myself as CTO with AI. Let me double your team's output.

I audit how your team uses AI, find the gaps, and train them to close it. The result: doubled engineering output in 90 days.

BOOK A CALL

Commits to production — Air Labs

Commits per week · last 12 months

Real data from Air Labs · github.com/AirLabsTeam

AUDIT :: 2–3 DAYS·TRAINING :: ~2 WEEKS·YOUR TEAM IS SHIPPING DIFFERENTLY WITHIN DAYS

CASE.STUDIES

Two teams, measured before and after.

Both of these were measured from the git history with formatting sweeps excluded. LoanCAD's team doubled over that window so that figure is per developer.

AI consultant, March 2026 to present · 35-person company, $20M ARR

Engineers using AI unevenly, and review time climbing with every extra pull request.

5.4×

the volume of code merged, with the same team and the same hours

Human lines changed in git, January–February compared to July–August, with two mechanical rename sweeps excluded.

Review automation, built alongside it

At five times the output, two humans reading every pull request stopped being affordable. So I built them a system that decides where human judgment is actually required and points the reviewer at those lines. One recent pull request: 31 files, over 2,000 lines changed, five spots a person had to look at. Plenty come back with nothing to flag and merge without a second reviewer.

This is separate from the training, it's something I built for them that runs on every pull request.

On-call issues went from 3.5 a week to 1.2 over the same stretch, with the same reviewers.

CTO & cofounder · lending platform used by North American banks

Four people building a platform for banks, with no room in the budget to hire more.

3.0×

the volume of code shipped per developer

Code lines changed per active contributor, January–February 2025 compared to the same window in 2026, with formatting sweeps excluded.

Two business cofounders became developers

They had only ever worked in the Python service and couldn't touch the application side. They now build full-stack features in the main codebase, with one engineer reviewing the work.

Not a measured number.

Test cases went from 90 to 3,132 while the codebase grew 1.8×.

PROC.PIPELINE

Two phases. Two weeks. Permanently better output.

I come in, diagnose exactly where your team stands, and train them to close the gap. No multi-month timelines. No bloated SOWs.

PHASE.01

The Audit

$10,000

4 days · Remote or on-site

Day

Codebase & Tooling Review

I dig into your repos, CI/CD pipeline, and existing AI setup. I'm looking at how your codebase is structured for AI to succeed — or fail — and what tools your team has access to versus what they're actually using.

Day

2–3

Engineer Shadowing

I watch how your engineers actually work with AI in the codebase. Not interviews — real observation. I see where they're getting stuck, where they're fighting the tool, and where the process breaks down.

Day

Findings & Recommendations

A clear, prioritized report: here's where your team stands, here's the gap, here's exactly what it would take to close it. No jargon, no fluff — a document you can hand to your CTO or VP of Engineering.

OUTPUT.FILES

Audit Report — A prioritized findings document with specific, actionable recommendations tailored to your team, your stack, and your gaps.

PHASE.02

The Training

$30,000

~2–4 weeks · Remote or on-site

Half

½d

Team Workshop

A live session covering the principles behind why AI falls short for most teams and the mental model shift required to make it a reliable tool. I tailor this to your stack, your codebase, and the specific gaps I found in the audit.

Days

1–3

1-on-1 Pairing Sessions

I pair directly with your engineers to set up your codebase, working through real problems together. Each session is tailored to where that developer is — pair programming on a real task, walking through the mental model shift, or breaking down how AI tools actually work. Your team does the work with me, so they learn the discipline by applying it. The setup is the teaching tool, not the product — and this is where the "aha moment" happens.

End

+30d

Handoff & Follow-Up

Documentation of everything we set up, a playbook for maintaining and evolving it, and a 30-day check-in to answer questions and troubleshoot anything that's come up since.

OUTPUT.FILES

Starter Configuration — A reference implementation for how your codebase should be set up. Not a finished product — the scaffold your team extends. You know your codebase better than I ever will.

Team Playbook — How your team owns this going forward: maintaining the setup, onboarding new engineers, and applying the discipline to new services as your codebase evolves.

Workshop Recording — Yours to reuse for onboarding new hires and refreshing the team — no need to bring me back to re-teach it.

Follow-Up Check-Ins — Bounded 30-day follow-up to pressure-test what's working. The goal is your team running on its own — not becoming a dependency.

TIME.DELTA

< 3 Weeks

Audit to training complete

OUTPUT.GAIN

Target: double your team's output in 90 days

ROI.INDEX

14:1

Typical ROI vs. hiring equivalent headcount

CRED.VERIFIED

I've been building software for 20+ years.
I know what actually works in production.

I'm not someone who read about AI last year and started consulting. I've shipped production code at the companies your engineers want to work at.

Enterprise

App Store frontend

Sam's Club TDD patterns

Product page performance

Startups

Seed to Scale

Joined at seed stage with 10 people, helped scale engineering to 35+ employees and $5M ARR over six years

35+

employees

years

$5M

ARR

Co-founder & CTO

Built an AI-driven engineering setup so effective I automated myself out of the CTO role. $1M+ profitable in under 2 years.

employees

years

$1M+

profitable

READY TO START

Book a 30-minute call.
I'll show you the gap live.

I'll walk through what 10x AI output looks like on a real codebase. Then we'll scope the audit for your team. No pitch deck — just a screen share and an honest conversation.

GET IN TOUCH