What Is a Forward Deployed Engineer, and Why Your AI Project Needs One
Most AI projects do not fail on model quality. MIT's GenAI Divide research (July 2025, preliminary findings) found that 95 percent of enterprise GenAI pilots produced no measurable return, and the driver was the learning gap: tools that never fit the real workflow. The fix the successful few converged on is organizational, not technical: put an engineer inside the business.
That role has a name. A Forward Deployed Engineer (FDE) is a senior engineer who works inside the customer's organization, learns the actual workflow, and builds against it. Palantir invented the role, and the leading AI companies now hire FDEs as their standard way of making AI land in real businesses. The Pragmatic Engineer has a good explainer on why the role is suddenly everywhere.
Why proximity wins
Traditional delivery hands requirements over a wall: the business writes what it thinks it needs, engineering builds what it thinks it read, and the gap between the two ships to production. An FDE closes that gap by sitting where the work happens. Requirements are discovered, not transcribed. Edge cases show up on day two instead of month six. And because the engineer sees the workflow, the software fits it, which is exactly the property MIT found separating the 5 percent that get returns from the 95 percent that do not.
The limit of the model, and what we did about it
Every FDE model shares one constraint: the engineer's day ends. Discovery happens at the speed of conversation, but building happens one person at a time.
Cohort runs the model with a second speed. Your Forward Deployed Engineer scopes the smallest valuable increment with you by day; our AI engineering team advances that increment overnight; you start the next morning with a demo of validated progress. Increments, not whole projects: honest scope is part of the model. One leader owns the engagement end to end, and every engagement is a fixed price agreed before work starts.
Offshore firms lack the embedding. Boutiques cannot staff the overnight shift. Building both into one team is what makes the model hard to copy, and it is how new capability shows up in weeks, not months.
See how the delivery model works, or pick a starting point: a productivity pilot, a discovery, or a strategy.