Deployment
What forward deployed engineering actually looks like
Most AI projects do not fail on the model. They fail in the gap between a demo and the messy system it has to live inside. Forward deployed engineering closes that gap by putting engineers in the room where the work happens.
Week one is observation, not architecture
We start by sitting with the people who will actually use the system. Intake coordinators, paralegals, social producers. We watch the clicks, the copy-paste, the spreadsheet that nobody documented but everybody depends on.
That week produces a map of where time is lost. Almost always the highest-value automation is not the one leadership described in the kickoff.
Ship something real in week two
A narrow, working slice beats a broad prototype. We pick one workflow, wire it end to end against production data in a sandboxed environment, and put it in front of five users.
- One workflow, real data, real permissions
- Instrumented from day one so quality is measurable
- A rollback path that a non-engineer can trigger
Stay until it holds
The last twenty percent — edge cases, audit trails, the handoff to an internal team — is where value is either locked in or lost. We stay embedded through that phase, then leave documentation and evals your team can run without us.
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