How we work
Discovery, a real pilot, then an embed — each one scoped and authorized on its own.
The model was never the hard part. The hard part is everything after it works — where it meets your real operations, your real latency budget, your real edge cases, and your real, messy data. Our method is built around that, not around the demo. Engineers work alongside your team throughout; you decide at the end of each phase whether the next one happens.
- 01
Discovery
We embed briefly with your team to map where AI is actually worth applying and where the data that would feed it really lives — not the org chart version, the working version. This is short, hands-on, and ends with a specific, scoped thing to build, not a strategy deck.
- 02
Pilot
We build one real thing end-to-end against a real workflow — real data, real users, real failure modes. Not a demo that works once in a controlled room. The pilot is scoped tight enough to ship in weeks and honest enough to tell us, and you, whether the idea holds up in production.
- 03
Embed
What works gets shipped into production inside your team, with the operating knowledge handed over alongside it — how it was built, why it was built that way, and how to run and extend it without us. The goal of an engagement is a team that no longer needs an FDE for this problem.
What each phase produces
Deliverables per phase
| Phase | What you receive | Acceptance gate | Your decision |
|---|---|---|---|
| Discovery | A baseline of how the workflow performs today, a named owner on both sides, a risk and feasibility memo, and a scoped recommendation — including a recommendation not to build, where that is the honest answer. | The memo names the target workflow, the data it depends on, and what success would have to look like in numbers. | Build, defer, or stop. Stopping here creates no obligation to authorize the next phase. |
| Pilot | One real workflow built against your data and your users, with the success and safety criteria agreed before the build starts, and an evaluation record showing what it actually did. | The evaluation record is measured against the criteria set in Discovery — not against a demo. | Embed or stop. The next phase requires separate authorization. |
| Embed | A production release, handover of the operating knowledge, a named owner and support arrangement on your side, and a value review against the original baseline. | Production-readiness and handover are signed off by your named owner. | Accept, operate, improve. |
What is forward-deployed engineering?
Forward-deployed engineering (FDE) puts engineers directly inside a client team to build and ship AI systems against real production workflows, rather than advising from the outside. The engineer owns delivery, not just recommendations — the work is judged by what runs in production, not by a deck.
What do you do after the proof-of-concept works?
We stay embedded through the part that actually determines whether AI works: production data, latency, edge cases, and the operational reality around the model. Each phase is scoped and authorized on its own, so you decide whether the next one happens — and we hand over the operating knowledge, not just the code.
Start here
See where this method applies to your team before committing to a pilot.
An AI-readiness discovery is the fastest way to find out — a short, structured look at where your data and workflows are, and where they aren't yet.