Simplex · Quantification of AI original software delivery

A Evidence level

Simplex: Quantification of AI original software delivery

Publisher: OpenAI · Vendors ' official customer information · Direct sources at the case level

Claim origin: Public disclosure by manufacturer or customer · Independent verification: No · Accessed: 2026-09-19

Evidence level measures whether a source can be located and reviewed; it does not mean vendor-reported claims were independently audited.

Standard case | Useful reference with remaining gaps 9 / 12

Business context1 / 2

Transformation workflow2 / 2

Technical workflow2 / 2

Human roles & governance / 2

Measured outcomes2 / 2

Source traceability2 / 2

Remaining gaps: Business context, Human roles & governance

The focus of this case is to move from a one-time analysis or a single-point tool for the “quantified certification of AI original software delivery” to a production stream that can be embedded in real business, verifiable and sustainable.

Business problem

The system integration project needs to know exactly which link AI is designing, developing, testing and actually saving time, rather than deploying on the basis of a sense.

Solution

AI CoE was established to quantify the design, coding and integration testing of ChatGPT Enterprise/Codex and to extend the effective model to the project.

Technical architecture & production workflow

Step 01

Code library/Issue/log/internal specifications

→

Step 02

Codex retrieves relevant codes and context

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Step 03

Agent develops the plan and generates the changes.

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Step 04

CLI/ compile/test/security check to generate definitive feedback

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Step 05

Engineer Review High Risk Change

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Step 06

CI/CD merger and continued improvement with production feedback

Key technology & infrastructure components

CodexChatGPT EnterpriseCoESoftware life cycle measurementTest

Human roles & accountability

Engineers verify the generator and are responsible for the delivery of quality.

FDE delivery actions

Select high-frequency and measurable production streams with the system development team, rather than simply deploying chat portals
Identifying the business context, system privileges, tools and security boundaries required for the model
Connect models to real software/data/business processes and establish tests, Evals or validation of certainty
Design the Human-in-the-lop and failed upgrade paths to ensure clear boundaries of responsibility
Continuous succession of Prompt, context, tools and processes based on production usage, errors and user feedback

Reusable delivery patterns

Enterprise AI effects depend on context, tools, validation and adoption rates, and not only on model capabilities
Quantifiable workflows first followed by product/platformization of successful models
As far as you can, Agent has access to tests, CI/CDs and security checks to form a closed loop.
High-risk industries must leave professional responsibilities and establish sustainable Evals

Business outcomes & delivery results

Officially, single screen development time was reduced by 70 per cent, design by 40 per cent and internal integration testing by 17 per cent.

Evidence boundaries & verification notes

The official case of OpenAI clearly discloses efficiency indicators measured by SDLC links.
The source can be directly located in the case; the value remains disclosed by the source and does not represent an independent audit.

Primary source: Simplex: Quantification of AI original software delivery ↗