OpenAI: Simplex cut screen development time by 70% after adopting Codex

OpenAI shared a case study of Simplex — a technology partner using ChatGPT Enterprise and Codex as the foundation for AI-first development. On initial projects, the company has already measured the impact: screen design time reduced by 40%, development time reduced by 70%, and internal integration testing time reduced by 17%.

How Codex Was Implemented

Simplex approached the implementation not as a purchase of a new programmer assistant, but as a transformation of the entire work model. After ChatGPT's launch, the company created an internal center of excellence in 2023 to validate AI-native processes and prepare employees. The next step was deploying ChatGPT Enterprise across the entire organization and selecting Codex as the primary code agent.

The choice was explained by a balance of cost, accuracy, and features, as well as a desire to standardize one main tool and accumulate internal expertise faster. At Simplex, Codex is used not only for code generation. The agent helps convert design documents and reference implementations into front-end and back-end code, writes unit tests, validates non-functional requirements, and makes corrections after internal integration checks.

The company is also testing automated chains where Codex CLI runs Python scripts and brings server implementation to fixes found in end-to-end tests. This way the company plans to make the agent's application repeatable across teams and projects.

"People maintain the final decision and responsibility for quality, while AI takes on implementation, validation, and fixes," explains

Simplex CEO Kazuhya Udzihiro.

Where Results Appeared

So far, Simplex is focusing on CRUD web applications — this is the starting scenario on which the company is testing new approaches to software delivery with AI participation. The important point is that this is not about team impressions, but about quantitative evaluation across multiple development stages. For corporate implementation, this is more important than any general promises about "acceleration."

The measured impact looks like this:

• 40% fewer hours for designing each screen
• 70% fewer hours for developing each screen
• 17% fewer hours for internal integration testing
• Less dependence on specific developer experience
• More opportunities for small teams to drive design and review

The company says the benefit is not limited to saving engineering hours. Codex helps verify specifications distributed across multiple files more accurately and transfers some of the expertise of senior specialists into a more reproducible process. As a result, team roles become more clearly defined: people make final decisions and are responsible for quality, while the agent handles routine implementation, review, and fixes.

From Tool to Process

The key idea of the case is that Simplex does not try to simply attach AI to the familiar linear scheme "requirements → design → development → testing → operation." Instead, the company wants to restructure the process around rules, constraints, repeated integrations, and automatic quality assessment. In other words, the goal is not to speed up individual steps, but to make the development pipeline itself AI-first.

The company believes that as catalogs of APIs, databases, and standardized design rules develop, Codex will be able to take on even more implementation and validation work. For relatively simple systems, Simplex sees even a scenario where a product could be automatically assembled directly from an RFP. They also acknowledge that in some tasks, it could be more efficient for the AI agent to perform business operations directly rather than first formulating them as source code.

What This Means

The Simplex case shows that the next stage of corporate AI adoption is not "yet another copilot," but the choice of a primary agent, measuring impact across stages, and reorganizing the development process around a new model of responsibility between people and machines. For large teams, this is also a way to make expertise less dependent on individual people.