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OpenAI Media and Entertainment Europe

Paf

Application Refactoring · IT Operations Automation

Paf is an international gaming company founded in 1966 in the Åland Islands by the Red Cross, Save the Children, and Folkhälsan, and is an industry leader in responsible gaming. With around 315 employees from 29 countries, Paf has contributed over 447.5 million euros to benefit society since it was founded.

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Paf is an international gaming company founded in 1966 in the Åland Islands by the Red Cross, Save the Children, and Folkhälsan, and is an industry leader in responsible gaming. With around 315...

Reported impact

Business outcome

Paf is an international gaming company founded in 1966 in the Åland Islands by the Red Cross, Save the Children, and Folkhälsan, and is an industry leader in responsible gaming. With around 315 employees from 29 countries, Paf has contributed over 447.5 million euros to benefit society since it was founded.

  • Productivity Improvement
  • Scalability

Solution

Paf is an international gaming company founded in 1966 in the Åland Islands by the Red Cross, Save the Children, and Folkhälsan, and is an industry leader in responsible gaming. With around 315 employees from 29 countries, Paf has contributed over 447.5 million euros to benefit society since it was founded. Paf adopted ChatGPT Enterprise across its entire company, with engineers using custom GPTs on a daily basis to speed up routine development tasks. Paf also integrated ChatGPT Enterprise into the grit:lab coding academy (gritlab.ax), training the next generation of software developers using an AI-augmented, systems-architecture mindset from day one.

Data flow

Paf adopted ChatGPT Enterprise across its entire company, with engineers using custom GPTs on a daily basis to speed up routine development tasks. Paf also integrated ChatGPT Enterprise into the grit:lab coding academy (gritlab.ax), training the next generation of software developers using an AI-augmented, systems-architecture mindset from day one.

Solution architecture

4 components · 3 layers
  1. Compute
    • OpenAI API Embeds model capabilities inside the customer’s application or internal workflow.
    • GPT-4 Provides the reasoning layer used to generate and evaluate outputs.
  2. Serving
    • ChatGPT Enterprise Provides a governed assistant environment for employees working across the deployment’s core workflows.
  3. Orchestration
    • custom GPTs Package prompts, instructions, and curated context for repeatable internal tasks.

Architecture clues

  • Embeds model capabilities inside the customer’s application or internal workflow.
  • In their search for the ideal generative AI solution, Paf experimented with LLAMA, Claude and GPT‑4.
  • Paf adopted ChatGPT Enterprise across its entire company, with engineers using custom GPTs on a daily basis to speed up routine development tasks.
  • Paf rolled out ChatGPT Enterprise to the entire team of 100 developers, and now finds ChatGPT Enterprise indispensable for their daily tasks.
  • Provides a governed assistant environment for employees working across the deployment’s core workflows.

Evidence from the source

"I use ChatGPT 20 times a day for tasks like boilerplate code creation or learning a new programming language,"
In their search for the ideal generative AI solution, Paf experimented with LLAMA, Claude and GPT‑4.
Paf adopted ChatGPT Enterprise across its entire company, with engineers using custom GPTs on a daily basis to speed up routine development tasks.
Paf is an international gaming company founded in 1966 in the Åland Islands by the Red Cross, Save the Children, and Folkhälsan, and is an industry leader in responsible gaming.
Transparency Evidence & methodology 85/100 reference value · 87% 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 value85/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 provenance8/10
    The publication date comes from publisher-controlled metadata.
  • 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

    Paf customer story

    First-party
    https://openai.com/index/paf/
    Source type
    Vendor-published customer story
    Published
    Jun 18, 2024
    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

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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