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Google Cloud Financial Services France

HiPay

Enterprise Knowledge Search · Real-time Analytics

HiPay needed to replace a legacy BI environment that was causing dashboard fatigue, low self-service adoption, and duplicated calculations, while still preserving strong metric governance and security for payment analytics.

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drives 50% higher BI adoption

Reported impact

Business outcome

HiPay made BI easier to use, increased adoption, and preserved strong governance by tying conversational access and predictive modeling back to a single semantic definition layer.

  • Productivity Improvement
  • Risk Reduction

Solution

HiPay migrated its BI environment to BigQuery, Looker, and Gemini Enterprise Agent Platform so business users could ask questions conversationally instead of wrestling with complex dashboards. BigQuery supplies the analytical compute layer, while Looker’s semantic model and version-controlled LookML centralize business definitions and let calculations run directly on the warehouse without data replication. HiPay then connected a natural-language interface to that semantic layer using governed API endpoints, which means row- and column-level security still flows through Looker User Attributes and access filters. The same platform also supports predictive modeling through Model Garden on Gemini Enterprise Agent Platform. By grounding conversational access in the governed semantic layer, HiPay increased BI adoption, reduced dashboard fatigue, and freed engineers from routine reporting requests.

Data flow

Payment analytics data is processed in BigQuery and modeled in Looker with governed business definitions. Business users access the data through a natural-language interface backed by Looker's semantic APIs, while predictive models can be deployed through Gemini Enterprise Agent Platform.

Solution architecture

3 components · 3 layers
  1. Storage
    • BigQuery Provides the warehouse compute layer for governed payment analytics and predictive modeling workloads.
  2. Serving
    • Looker Supplies the semantic model, security controls, and conversational analytics interface for business users.
  3. Orchestration
    • Gemini Enterprise Agent Platform Hosts predictive modeling and natural-language AI capabilities connected to Looker's semantic layer.

Architecture clues

  • LookML business definitions are version controlled
  • Looker User Attributes and access filters enforce row- and column-level security
  • Model Garden on Gemini Enterprise Agent Platform supports predictive models

Evidence from the source

Because Looker’s conversational framework queries the semantic model using governed API endpoints
Centralizing their business definitions inside version-controlled LookML code allowed HiPay to eliminate data replication entirely
Deploying Looker has triggered a massive fifty percent year-over-year surge in business intelligence utilization
HiPay migrated legacy BI environment to Looker and BigQuery, increasing user adoption by 50 percent through governed conversational analytics.
This natural language interface has completely eliminated dashboard fatigue
Transparency Evidence & methodology 85/100 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 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 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

    HiPay customer story

    First-party
    https://cloud.google.com/customers/hipay-looker
    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

  • Sitemap last modified Jun 18, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Jun 18, 2026 · Google Cloud · Unknown Legacy extracted date without source-page confirmation.
    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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