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OpenAI Manufacturing Asia-Pacific & Oceania

ENEOS Materials

Document Processing · Enterprise Knowledge Search

ENEOS Materials needed secure AI that could help a shrinking workforce perform research, plant design, training analysis, and other knowledge-heavy manufacturing tasks faster without exposing proprietary information.

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80% of employees reported significant workflow improvements during pilot phase
90% reduction in HR data aggregation and analysis time
Investigations reduced from months to minutes with deep research
Over 1,000 custom GPTs created

Reported impact

Business outcome

ENEOS Materials achieved broad weekly adoption, sharply reduced research and data-analysis time, improved design speed and safety, and created a repeatable pattern for company-wide AI use in manufacturing under secure enterprise controls.

  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

ENEOS Materials adopted ChatGPT Enterprise as a secure company-wide foundation, then scaled usage with a cross-functional team that built more than 1,000 custom GPTs for departmental workflows. Engineers use deep research to search local-language and specialized technical sources, translate them into Japanese, and compress investigations that used to take months into minutes. Plant-design teams use a custom GPT grounded in company standards to generate specifications from inputs such as flow rate, pipe diameter, pressure loss, and material requirements while also flagging corrosion or material-selection risks. HR teams use a separate custom GPT to analyze post-training feedback and an internally built tool, created by a non-programmer with ChatGPT support, to automate data aggregation. Rather than isolate AI in one lab, ENEOS made the enterprise workspace broadly available so research, design, analysis, and employee upskilling could all run on a governed common platform.

Data flow

Technical documents, external research, plant-design parameters, and training feedback enter ChatGPT Enterprise and department-specific custom GPTs. Deep research retrieves and translates relevant materials, specialized GPTs generate design or analysis outputs, and employees review those results before applying them in operations.

Solution architecture

3 components · 2 layers
  1. Serving
    • ChatGPT Enterprise Provides the secure enterprise workspace for company-wide generative AI use with proprietary information.
    • deep research Performs multilingual technical investigations across specialized external sources.
  2. Orchestration
    • custom GPTs Capture specialized workflows for plant design, HR analysis, and other departmental use cases.

Architecture clues

  • Cross-functional rollout team partnered with OpenAI
  • Custom GPT for plant design is grounded in company standards and engineering inputs
  • Deep research translates local-language technical materials into Japanese
  • Non-coders built internal tools with ChatGPT support

Evidence from the source

"Investigations that took months now finish in minutes"
"more than 90% of employees used ChatGPT at least weekly"
"over 1,000 custom GPTs"
"the time required for data aggregation was reduced by approximately 90%"
Transparency Evidence & methodology 95/100 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 value95/100
  • 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 specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale5/10
    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

    ENEOS Materials customer story

    First-party
    https://openai.com/index/eneos-materials/
    Source type
    Vendor-published customer story
    Published
    Sep 24, 2025
    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 Sep 24, 2025 · 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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