We train human stack engineers.

The last mile is a human‑stack problem.

95% of GenAI pilots fail to deliver due to flawed enterprise integration, not model quality. The entire industry is bottlenecked on the last mile.

Closing that gap takes judgment to scope ambiguity, taste to make the hard architectural calls, and agency to ship through real organizational friction. These are the skills that matter in the AI era.

No course can teach them. You only get them by iterating with real customers on real stakes, and most people never get that chance.

So we built a place for it.

12 weeks, full‑time, fully remote.

Cohort

You’ll join a team of 3 residents led by a senior Forward Deployed Engineer, who’s run deployments at Anthropic, OpenAI, or Palantir. Together, you’ll take a product from a frontier lab or Series A–C applied‑AI startup and deploy it inside a mid‑market customer, where integration is the real work. Each cohort runs 3 teams in parallel: 9 residents, 3 customers, 1 senior lead. Residents are full-time on a $2,000/month stipend.

We get you ready before kickoff so day one is a customer call, not a setup day. From there the account is yours: scoping, stakeholders, integration, rollout, and shipping to production against acceptance criteria the customer has already signed.

You’re not running a pilot. You’re shipping something the customer relies on in their critical path.

Growth

Your senior FDE leads early client calls and gradually steps back as you run the engagement. They host weekly group office hours where each team brings their hardest problem, plus 1:1s any time you need them.

You learn by iterating, making hard calls, and talking through decisions with your senior, your teammates, the customer, and the vendor. You’ll also learn from what other teams are working through. At the midpoint you sit with your senior for an honest review of what’s working, what isn’t, and what to change.

Ledge, our internal knowledge system, captures decision logs, PR reviews, and feedback patterns so what one team learns the hard way, the next team gets for free.

AI‑native execution

You work AI‑native from day one. Agents write the code, run the eval pipeline, first‑pass PRs, and flag regressions before they land. You direct them, review their output, and own the decisions they can’t: architecture, scoping, failure modes.

Much of your work is harness engineering: custom linters, eval suites, structural tests, and feedback loops that let agents run safely inside the deployment.

What you leave with

You leave as the engineer a senior operator trusts with a real customer problem. Because one already did.

You also walk out with a production deployment at a named customer you understood deeply enough to ship for, a reference letter from the senior who watched you make every call, and a network of seniors, fellow residents, and vendor contacts you built trust with through the work.

When your next hiring manager asks you to walk through a hard decomposition, you’ll have one to walk them through.

For people who ship.

Senior engineers who’ve shipped for years and use AI tools every day, but aren’t sure if a decade of judgment is still worth what it used to be. Twelve weeks of real work will tell you.

Aspiring founders who can build anything but worry about spending a year on the wrong problem, because you’ve never been close enough to real customer pain. This is how you get close.

Career switchers who’ve shipped real things in another field and have nothing AI‑native to point to yet. You leave with a named customer, a live deployment, and a senior who’ll vouch for you.