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Google Cloud Financial Services France

Tardigrade AI

Real-time Analytics · Risk Management

Tardigrade AI needed a cloud-native climate-risk platform that could unify huge public, partner, and insurance data sets, orchestrate analysis automatically, and turn complex model outputs into understandable guidance for non-specialists.

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Reported impact

Business outcome

Tardigrade AI made climate and disaster-risk analysis easier to understand and act on, while giving clients a more scalable way to explore and operationalize adaptation decisions.

  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

Tardigrade AI uses Google Cloud to address this challenge: Tardigrade AI needed a cloud-native climate-risk platform that could unify huge public, partner, and insurance data sets, orchestrate analysis automatically, and turn complex model outputs into understandable guidance for non-specialists. Cloud Run executes the workloads behind the Adapt platform and its climate-risk analyses, serving as the compute layer. BigQuery centralizes heterogeneous data sources and exposes a unified analytical layer for climatologists, econometricians, and AI experts, serving as the storage layer. Cloud SQL manages user-account and report data for the platform, serving as the storage layer. Cloud Storage stores report and supporting data assets in the platform, serving as the storage layer. Cloud Workflows orchestrates the end-to-end cloud-native process together with messaging primitives, serving as the orchestration layer.

Data flow

Insurance, public, partner, and Copernicus data is centralized in BigQuery, Cloud Run executes the core analytical workloads, Cloud Workflows and Pub/Sub orchestrate the process, and Gemini transforms the resulting climate-risk outputs into readable reports and natural-language exploration paths.

Solution architecture

7 components · 4 layers
  1. Compute
    • Cloud Run executes the workloads behind the Adapt platform and its climate-risk analyses
  2. Storage
    • BigQuery centralizes heterogeneous data sources and exposes a unified analytical layer for climatologists, econometricians, and AI experts
    • Cloud SQL manages user-account and report data for the platform
    • Cloud Storage stores report and supporting data assets in the platform
  3. Serving
    • Gemini turns model outputs into natural-language risk summaries and helps users query climate data more directly
  4. Orchestration
    • Cloud Workflows orchestrates the end-to-end cloud-native process together with messaging primitives
    • Pub/Sub supports event-driven orchestration across the climate-risk processing pipeline

Architecture clues

  • Gemini contextualizes water-level projections against more than 440,000 French natural-disaster recognition orders
  • The platform uses generative AI to convert climate-model output into actionable recommendations
  • The team plans Looker dashboards and Google Earth visualization for clearer risk exploration

Evidence from the source

Tardigrade AI is working on the possibility of querying BigQuery in natural language with Gemini.
Tardigrade AI relies on Gemini models: they interpret the results and render them in accessible language.
The modernization relies on Cloud Run to execute workloads, BigQuery to centralize and exploit data, Cloud SQL and Cloud Storage to manage user accounts and reports, and Cloud Workflows and Pub/Sub to orchestrate the entire process.
Transparency Evidence & methodology 71/100 reference value · 72% 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 value71/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence72%
  • 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 evidence15/20
    3 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 specificity8/10
    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

    Tardigrade AI customer story

    First-party
    https://cloud.google.com/customers/tardigrade-ai
    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 Feb 7, 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 Jan 29, 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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