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OpenAI Financial Services Europe & UK

Univé

Document Processing · Enterprise Knowledge Search

Univé needed to build organization-wide AI capability across insurance operations so employees could safely redesign their own work instead of waiting for centralized software projects or isolated AI pilots.

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85% of licensed users active every week
97% of ChatGPT Enterprise licences activated
Approximately 1,500 custom GPTs created by employees
Employees average 40 prompts per active user each week

Reported impact

Business outcome

Univé achieved broad sustained adoption, shifted employees from passive users to active builders, and accelerated insurance operations such as claims preparation and underwriting without removing human accountability.

  • Customer Experience
  • Productivity Improvement
  • Risk Reduction

Solution

Univé combines ChatGPT Enterprise, governance-by-design, and employee-led experimentation to build what it calls an AI-ready workforce. Leadership sessions reframed AI from an IT rollout into an organizational capability, while enterprise authentication, inherited connector permissions, privacy assessments, security reviews, and responsible-AI monitoring created the confidence to scale adoption safely. Within that foundation, employees spend hundreds of hours each week redesigning tasks, building approximately 1,500 custom GPTs, and experimenting with Workspace Agents that proactively prepare claims, underwriting, and other recurring work. In pet insurance, a Workspace Agent assembles files, reviews invoices, checks policy conditions, highlights anomalies, and prepares a traceable recommendation before a human claims professional makes the final decision. Underwriters likewise begin the day with pre-structured queues grounded in approved enterprise sources. The result is a cooperative insurer where governance accelerates experimentation and employees become the builders of internal AI capability.

Data flow

Employee prompts and enterprise records flow through ChatGPT Enterprise, custom GPTs, and Workspace Agents under inherited permission controls. Agents prepare evidence-backed claims or underwriting cases before humans review, decide, and feed successful patterns back into new GPTs and workflows.

Solution architecture

3 components · 2 layers
  1. Serving
    • ChatGPT Enterprise Provides the secure enterprise workspace employees use for day-to-day AI adoption.
    • Custom GPTs Encode task-specific workflows that employees create to solve internal operational challenges.
  2. Orchestration
    • Workspace Agents Proactively prepare claims and underwriting work using approved enterprise data sources.

Architecture clues

  • Employees collectively spend hundreds of hours every week redesigning work
  • Governance includes connector permission inheritance so AI access never exceeds source-system permissions
  • Managers were trained to create the conditions for responsible innovation rather than merely approving IT projects
  • Workspace Agents proactively prepare recurring work across approved enterprise systems

Evidence from the source

"AI prepares the work; people make the decision."
"Employees have created approximately 1,500 custom GPTs"
"Pet insurance claims that previously took hours to prepare can now be ready for decision in minutes"
"We chose to scale AI by creating more builders."
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

    Univé customer story

    First-party
    https://openai.com/index/unive/
    Source type
    Vendor-published customer story
    Published
    Jul 31, 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 Jul 31, 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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