01Why no public price list?
Because the work is different every time. A factory cycling against an embedded systems team in Munich and a payments API in Sao Paulo don't cost the same to run, and they don't ship the same kind of artifact. We scope, then we quote.
02What problem are we actually solving for you?
AI coding tools mostly accelerate the editor. We work one layer up: after the engagement, the code your team ships is bound to a written spec and a machine-checkable proof. Velocity goes up because the work that ships is the work you intended, with a proof that says so.
03How does this fit alongside our existing team?
Your Forward Deployed Engineer works with your team, not in place of them. They install the factory, share how to run it, and stay on through every cycle. The leverage shows up as verified shipping velocity on the work your engineers already care about.
04What if our codebase is messy?
Most are. Early on, your Forward Deployed Engineer reads the codebase end to end and cleans up just enough for the miss-rule library to land. The factory tends to run better the worse your starting point. There is more signal to harvest.
05What stays inside our perimeter?
All of it, if that is what you want. On-prem and air-gapped deployments are available with a longer onboarding. We bring the inference stack into your environment. Code, miss-rules, run history, and proofs all stay in your perimeter. We never train shared models on your code.
06What guarantees come with what ships?
Two layers. First, every artifact that ships is bound to a spec and a proof, written with your team. The verify gate holds any artifact that does not pass the criteria. Second, regression coverage: each cycle re-runs your full suite plus the regressions the factory synthesized during the run. If the proof does not check, the artifact stays inside the factory.
