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OpenAI

OpenAI Deployment

Forward deployed engineers bringing AI to enterprises

We built The OpenAI Deployment Company to help organizations solve high-impact problems using AI—starting from first principles and deploying systems in real-world environments.

What is forward deployed engineering (FDE)?

Forward deployed engineering (FDE) is how OpenAI brings AI into production for complex, real-world use cases.

Instead of starting with a generalized product, FDE teams build bespoke AI systems directly inside the complexity of real-world enterprise environments—where security models, permissions, governance, compliance requirements, operational controls, and legacy infrastructure are core constraints, not edge cases. The work is centered on solving high-value customer problems in production environments where the stakes are real and the impact is measurable. This approach helps organizations move from AI experimentation to reliable deployment.

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How FDE teams work

FDE teams operate in high-ambiguity environments where traditional software approaches break down.

We believe the best AI systems are built from first principles, with a relentless focus on speed and real-world impact. Our teams work alongside domain experts to understand how problems show up in practice, then deliver value early and iterate quickly toward scale. The goal is simple: build AI systems that work in the real world, not just in theory.

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Case studies

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BBVA

BBVA partnered with OpenAI to help build an AI native bank at global scale. What began as an early deployment of ChatGPT Enterprise quickly expanded across the organization, helping employees improve workflows, enhance decision making, and deliver better customer experiences. Today, the collaboration is scaling to 120,000 employees across 25 countries with AI embedded into the core of how the bank operates.

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John Deere boosts customer engagement

OpenAI partnered with John Deere to deploy AI-powered recommendations for farmers during planting season. After reviewing hundreds of real-world examples with domain experts, building custom evaluation systems to measure accuracy, and iterating quickly to improve model performance, John Deere was able to help farmers reduce chemical usage by up to 70% and increase customer engagement.

From deployment to real, product solutions

By solving real customer problems, our forward deployed engineering teams identify repeatable patterns that evolve into product capabilities. This cycle—build, prove, generalize—connects deployment to product development across Agent SDK, AI-assisted authoring systems, model benchmarking and reliability tools, and more.