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Google Cloud Financial Services Germany

SIGNAL IDUNA

Document Processing · Enterprise Knowledge Search

SIGNAL IDUNA needed to spread AI safely across a large insurance workforce, make a century of internal policy knowledge searchable, and let teams build their own agents without creating a fragmented, insecure shadow-AI environment.

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80% weekly active adoption rate with a +45 NPS in first five months
Scaling AI to 10,000+ employees in six months
find the exact right information in under 20 seconds
weekly users has skyrocketed by 400%

Reported impact

Business outcome

SIGNAL IDUNA achieved rapid enterprise AI adoption, made complex policy knowledge broadly accessible, and created a secure foundation for both internal and future customer-facing insurance agents.

  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

SIGNAL IDUNA rolled out Gemini Enterprise across its workforce and used Google Cloud to turn internal insurance knowledge into a searchable, agentic asset. Employees and sales partners access the Gemini Enterprise app through an internally branded environment, while the company’s Co SI Health knowledge agent uses Document AI to extract and structure policy content and Gemini Enterprise Agent Platform plus Gemini models to orchestrate reasoning across thousands of documents and more than 600 tariffs. The result is a centralized system that can return the right knowledge in under 20 seconds, while also letting employees build their own custom agents for daily work. Because the platform is company-wide and security is handled as part of Gemini Enterprise, SIGNAL IDUNA could scale adoption rapidly instead of limiting AI to small pilots. The same foundation is now positioned to support future customer-facing self-service experiences on top of the internal rollout.

Data flow

Internal policy documents are extracted and structured with Document AI, orchestrated through Gemini Enterprise Agent Platform, and reasoned over by Gemini models. Employees then access answers and custom agents through the Gemini Enterprise app.

Solution architecture

4 components · 4 layers
  1. Ingest
    • Document AI Extracts and structures internal policy and tariff documents for downstream reasoning.
  2. Compute
    • Gemini Reasons across the structured insurance knowledge base and returns relevant answers quickly.
  3. Serving
    • Gemini Enterprise app Provides the company-wide employee and sales-partner interface for secure AI adoption.
  4. Orchestration
    • Gemini Enterprise Agent Platform Orchestrates the Co SI Health knowledge agent and custom employee-built agents.

Architecture clues

  • Co SI Health spans thousands of internal documents for over 600 different tariffs
  • Document AI structures the source data
  • Gemini Enterprise Agent Platform orchestrates all the data
  • The rollout was executed company-wide to employees and sales partners

Evidence from the source

80% weekly active adoption rate with a +45 NPS in first five months
It helps every employee find the exact right information in under 20 seconds.
Scaling AI to 10,000+ employees in six months with Gemini Enterprise
We’re using Document AI to extract and structure the data, Gemini Enterprise Agent Platform to orchestrate all the data, and we’re leveraging Gemini models to reason across the entire knowledge base.
the number of weekly users has skyrocketed by 400%
Transparency Evidence & methodology 91/100 reference value · 79% 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 value91/100
  • 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 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 confidence79%
  • 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
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • 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. Google Cloud

    SIGNAL IDUNA customer story

    First-party
    https://cloud.google.com/customers/signaliduna
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Sep 27, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Aug 20, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    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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