Building OpenAI with OpenAI

Chief Commercial Officer, Giancarlo ‘GC’ Lionetti, kicks off our series sharing internal examples of how OpenAI is using its own technology and APIs. These tools are being used internally, only at OpenAI, and are shared here as illustrative examples of how frontier AI is supporting use cases across our teams. We’re also sharing the internal tool names for a clearer look at how frontier AI helps our teams get work done.

AI has moved beyond an experiment. It now operates as infrastructure for work, shifting from pilots to systems that shape daily decisions. While our models improve in speed, cost, and capability, adoption rarely moves in a straight line. Deployments often outpace the change needed for organizations to leverage this technology.

Inside OpenAI we see the same tension. Running our business on AI means facing the questions every customer asks: where to start, how to align new tools with existing workflows, how to measure progress as the ground shifts. When I meet customers, the question they all ask me is, “How does OpenAI use OpenAI?”

Our approach is to treat AI as a practice that elevates craft.

Every company depends on expertise. The salesperson who builds trust, the support lead who solves the hardest problem, the engineer who finds order in complexity. AI encodes that expertise and distributes it across teams, scaling the impact of each discipline. 

This is how we build. Our GTM, product, and engineering teams study their everyday workflows, define what good looks like and deliver changes in weeks instead of quarters. We decided to focus on a few high-leverage systems with outsized impact. Each team tests them in live deployments, building the same muscles our customers do. 

OpenAI on OpenAI

Today we’re launching OpenAI on OpenAI, a series that shows how we use AI inside our business. Each story covers a real problem and the solution we built. Our goal is to share patterns companies can adapt.

We start with a few examples: