OpenAI is making implementation a visible part of its enterprise AI strategy. In an Oct. 22 account, Colin Jarvis, the company’s head of forward-deployed engineering (FDE), describes engineers embedding with customer teams to identify difficult work, build production systems and make successful approaches reusable. [1]

Closing the deployment gap

Jarvis says he founded OpenAI’s FDE team in early 2025 to address what the company calls a deployment gap: the distance between a model’s apparent capability and what an organization can reliably use in daily work. The team works with customers where models are sufficient and brings unmet needs to OpenAI’s product and research groups. [1]

OpenAI’s service page describes FDEs observing workflows, connecting systems and context, testing and securing solutions, and helping customer teams scale adoption. The company’s stated sequence is to map processes, define business value, prove a solution in production and expand what works. This places the role between software implementation, domain discovery and product feedback rather than conventional API support alone. [2]

From one-off builds to common tools

OpenAI illustrates the approach with a semiconductor customer. It says about 15 FDEs worked across more than 10 use cases in mid-2025, including test generation, debugging and an architecture assistant. OpenAI reports that all reached production within three to six months, and cites roughly 48% time saved on AI-based directed test generation. These are company-reported results, not independently audited measurements. [1]

The company says it later rebuilt solutions around Codex and shared skills and plugins, reducing custom code per project by roughly 90%. In one example, OpenAI says an initial deployment took two months, while expanding it to a second team took one day. These figures describe OpenAI’s account of a particular engagement, not a general implementation benchmark. [1]

OpenAI’s broader Deployment Company announcement also described engineers working inside organizations to build production systems. Together, the initiatives suggest customer implementation is a route the company sees as important for moving model capability into business use. Buyers will need to assess whether such engagements leave behind maintainable systems and reusable value. [3]

Sources

[1] OpenAI: What forward-deployed engineering means at OpenAI [2] OpenAI Forward Deployed Engineering [3] OpenAI: Launching the OpenAI Deployment Company