Service 07 / 08
Forward Deployed Engineering.
For enterprises that want outcomes delivered inside their walls — engineers who deploy, adapt, and ship on-site until the number moves.
The practice
What this engagement looks like.
Forward deployment is the delivery model that made Palantir's software land inside the world's hardest institutions, and it's the model the leading AI labs now hire for. The premise: enterprise software fails at the last mile — between a capable platform and a messy, political, exception-riddled operating reality. Someone senior has to stand in that gap, with commit access.
Our FDEs are that someone. They sit with your fraud analysts, your claims processors, your planners — deploy Vibalitics and Zorat against live systems in the first week, and then reshape the deployment daily based on what the field actually needs. No requirements documents that age into fiction; the working system is the requirements document.
The other half of the job points inward: everything an FDE learns in your operating reality flows back into our platforms as product. You get software that fits like it was built for you — because, increasingly, it was.
What you get
Deliverables, not decks.
FDE pod embedded on-site or hybrid
Platform live against your data in week one
Weekly shipped increments, demoed on your cases
Field-to-product feedback loop with a named owner
Powered by our platforms
How it works
The model, in detail.
01
Why forward deployment works
Traditional delivery separates the people who understand the problem from the people who can change the software. FDEs collapse that distance to zero: the engineer watching an analyst fight the queue at 3pm ships the fix by 6. Cycle time from observation to production is the whole advantage.
02
The operating rhythm
Mondays set the week's outcome target with your operators. Tuesday through Thursday is build-and-deploy against live workflows. Friday is a demo on your real cases — not a slide about progress. Every week ends with something running that wasn't running before.
03
Pod composition
A typical pod is two to three people: a forward deployed engineer (full-stack, platform-fluent), an ML engineer when models need tuning in the field, and a part-time domain architect. Small enough to move fast, senior enough to not need supervision.
04
FDE vs. embedded teams
Embedded teams join your roadmap and ship what you prioritise. FDEs own an outcome and bring their own playbook — they deploy our platforms, decide what to build next based on field evidence, and are accountable for the metric, not the backlog. Choose FDE when the outcome is clear and the path isn't.
Next practice