Forward Deployed Engineering means putting engineers close to the people doing the work. They learn how a problem happens, build a solution with the team, and help get it running in the environment where it will be used.
OpenAI Deployment Company describes FDE as working directly with domain experts, examining high-impact problems from first principles, delivering value early, and expanding from there. For a business, the practical idea is one team carrying the work from understanding the problem through to building and running the system.
Understand the Business
Why context matters when building AI
AI can handle less structured information, but it needs the context behind it. A customer request may mean different things depending on the product, customer relationship, and delivery date. An exception one factory can approve locally may require escalation at another.
Much of that context lives in experienced employees’ knowledge, spreadsheet notes, chat histories, and everyday habits. Working alongside users helps engineers uncover those details and check that they have understood them correctly.
Roles and Responsibilities
How FDE compares with other services
From advice to implementation
Consulting can help set a direction and design a plan. FDE also takes on implementation: connecting systems, managing permissions, handling errors, and helping people use the result.
Support continues after delivery
A conventional software contract may end when the specified features are delivered. FDE continues into day-to-day use, where feedback shows what needs to change for the system to produce the intended business result.
Built around your existing processes
Standard software serves many customers through a common design. FDE starts with your existing processes, considers what to keep or change, and builds the connections and agent permissions around those decisions.
Discover: Walk through the job with the people who do it as well as their managers.
Define: Agree on the outcome, data access, permissions, exceptions, and approvals.
Build: Connect the tools and test a limited set of day-to-day tasks.
Operate: Review failures and corrections, update the rules, and expand when ready.
Complete Workflow
What should you expect from a project?
Expect a complete, usable workflow with a clear scope. You should know who starts a task, what the agent reads and does, where it waits for approval, where the result goes, and who gets notified if it fails.
Your team has a role too. People who know the work need time to explain decisions, share exceptions, and review results. Their input is essential to building something the business can rely on.
Complex work still benefits from a small first project. Begin with one clearly defined problem. Once the workflow holds up in daily use, its methods and components can inform projects in other departments.
Supercaly brings this approach to custom AI agents. We work with your team from understanding the task through to building, connecting, and supporting the system.
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