What AI didn't fix.

Your engineers are faster. Your org is slower.

You bought everyone the tools and individual output went up, but three months later your seniors are doing twice the reviews. Deploys are harder to coordinate, the codebase is harder to reason about, and you have more code in flight than ever with less confidence in what ships.

That's not a tools problem, it's a systems problem.

See if we're a fit (15 min, no pitch)

State Dept passport system: 50+ min → 5–13 min6–12k daily applications5+ years production systems


AI isn't slowing down. Your system needs to catch up.

Every model upgrade gives your team another speed increase, which means more code, more PRs, more surface area, and without the system underneath to absorb it, every upgrade just compounds the chaos.

Your best engineers aren't slow, they're stuck doing reviews and unblocking juniors and babysitting deploys that should run themselves. AI made that worse. it gave everyone a firehose and left the drainage to figure itself out.

You've tried adjusting process, doing retros, buying new tools, maybe even hiring a consultant who gave you a 40-slide deck and disappeared. The problem isn't process, process is a symptom.

The actual constraint is always the same: unclear ownership, handoffs where work dies, and approval chains that exist because no one trusts the system. Fix those and the speed just takes care of itself.

How It Works

I don't add process. I remove the friction that created the need for it.

Phase 1 — Find the actual constraint  2 weeks

Not a listening tour. I find where work actually dies — the handoffs, the unclear ownership, the "waiting on X" that accounts for most of your cycle time. In a team moving fast with AI, these show up faster and hurt more.

Phase 2 — Fix it with you  4–6 weeks

I'm in the codebase, the PRs, the deploys. We implement together so your team sees someone doing the work, not presenting about it.

Phase 3 — Prove it and leave  2 weeks

We measure. Cycle time. Deployment frequency. Time-to-first-commit for new hires. If the numbers don't move, we keep going. When they do, I'm out. Your team owns it.

Senior engineers writing code again. AI output actually reaching production. New hires productive in weeks. And you not explaining the same roadmap slip twice.

The Proof

I built this under conditions your startup will never face.

Before I consulted, I was the engineer responsible for modernizing the U.S. passport renewal system.

Small team, federal security constraints, and a bureaucracy designed to prevent exactly the kind of rapid iteration we needed. We shipped a system that cut completion time from 50+ minutes to 5–13 minutes and now process 6–12,000 applications daily.

The constraint wasn't speed, it was always the system. We fixed the system and the speed followed.

If I can maintain velocity in that environment, your Series B startup won't be the hard part.

1M+ Applications processed$130M Revenue processed (3 mo)90% Time reduction


Who This Is For

This works if:

Good fit

Probably not for you if


The Offer

Start with an Engineering Systems Audit

4 weeks. Three things happen:

  1. I find the specific constraints killing your velocity — not a generic list, the actual bottlenecks in your codebase, your team, your deploys.
  2. We fix one of them. Not a plan to fix it. Actually fixed, in production, measurable.
  3. You get a roadmap for the rest, prioritized by impact with realistic effort estimates.
If the audit doesn't surface at least one insight that changes how you operate, don't pay me.

Book the audit (15-min call, no pitch)

We'll know in 10 minutes if this is a fit.


Your system is the bottleneck. Not your engineers.

The tools are already there and the speed is already there, but what's missing is the system that turns individual AI output into shipped product.

You can keep buying tools and hoping the org catches up.

Or you can fix the system.

Let's talk