Forward Deployed Engineering
Forward Deployed AI Engineers for Enterprise
When an internal team has models but needs senior engineers to ship the production system, we embed directly with them.
When to bring in a Forward Deployed Engineering team
- You've run AI pilots and proven the concept — but no one owns turning it into a production system.
- Your internal engineering team is stretched thin on core product work and can't take on a new integration effort.
- You need senior AI engineering talent for a defined period, not a permanent headcount addition.
- Your first production AI system needs to work — and set the pattern for the next ten.
What a Forward Deployed Engineer actually does
- Work from your office or environment, embedded with your team
- Integrate directly with your data, internal tools, and APIs
- Build evaluation and reliability infrastructure alongside the feature
- Hand off a system your team can operate and extend
Deployment is the starting point
Build the system. Build its improvement loop.
FDEs connect agents to real workflows and establish the evaluations, observability, and operating boundaries your team needs.
Enterprise RSI uses agreed production signals to propose bounded changes, evaluate them against fixed verifiers, and route them through approval gates. When a specific capability needs more practice, expert-created data, trajectories, and training tasks can support the next experiment.
Customer data and traces are used only for explicitly agreed purposes. A deployment does not grant permission to reuse them for model training or other customers.