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OpenAI Commercial and Professional Services Asia-Pacific & Oceania

Simplex

Application Refactoring · IT Operations Automation

Simplex wanted a repeatable way to measure and scale AI-driven software delivery across design, implementation, testing, and operations instead of relying on ad hoc developer experiments.

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17% less time needed for internal integration testing
40% less time needed to design each screen
70% less time needed to develop each screen

Reported impact

Business outcome

Simplex quantified substantial efficiency gains across design, development, and integration testing, created a primary AI-agent standard for the organization, and established an AI-first delivery model that can be rolled out across more projects.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Simplex adopted ChatGPT Enterprise and Codex as the foundation of an AI-first development model that spans design, coding, testing, and remediation. After establishing a center of excellence to validate AI-native development processes, the company selected Codex as its primary coding agent so teams could accumulate know-how and compare impact across projects. Codex is used not only for code generation, but also for implementation from design documents and reference examples, unit-test generation, review and remediation of nonfunctional requirements, and fixes for issues found during internal integration testing. The team is also validating workflows in which Codex CLI runs Python scripts and iterates from server implementation through test failures. Instead of mapping AI one-to-one onto traditional stages, Simplex is redesigning software delivery around rules, repeated integration, and automated evaluation, with people retaining final accountability for quality and delivery decisions.

Data flow

Design documents, reference implementations, and test results are fed into Codex and Codex CLI, which generate code, tests, review outputs, and fixes. Teams then evaluate the results quantitatively across projects and keep people responsible for final quality, sign-off, and operating-model changes.

Solution architecture

3 components · 2 layers
  1. Serving
    • ChatGPT Enterprise Provides the company-wide enterprise foundation for governed AI use and knowledge sharing.
  2. Orchestration
    • Codex Acts as the primary coding agent across design, implementation, test generation, and remediation work.
    • Codex CLI Runs automated local workflows that move from implementation through testing and fixes.

Architecture clues

  • Center of excellence validated AI-native development processes before company-wide scale-out
  • Codex CLI workflows run Python scripts and iterate through test failures
  • Codex is designated as the primary coding agent
  • Organization is redesigning development around repeated integration and automated evaluation

Evidence from the source

"40% fewer hours to design each screen"
"70% fewer hours to develop each screen"
"creation of test code including unit tests"
"selected Codex as its primary coding agent"
Transparency Evidence & methodology 96/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value96/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. OpenAI

    Simplex customer story

    First-party
    https://openai.com/index/simplex/
    Source type
    Vendor-published customer story
    Published
    May 7, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page May 7, 2026 · OpenAI · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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