HiPay
HiPay has 2 source-linked AI deployments documented in AIUseCaseHub, across 1 industry and 1 country.
Hyperscaler mix
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How HiPay builds AI
Build / Buy / Compose across this company's documented cases
2 of 2 cases classified (100%) · Compare all use-case types
Reported outcomes
1 case reports measurable results
−22.5%
Time & speed
median · 2 metrics
Medians of results published in HiPay cases, normalized for comparability. See all benchmarks →
Technology snapshot
What HiPay uses across visible cases
Capability flags and technologies mentioned in the indexed use cases on this page.
- Top use case
- Agent
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- Tech names
- 12
All Use Cases (2)
HiPay: governed conversational analytics on BigQuery + Looker with Gemini Enterprise Agent Platform
HiPay, an independent European payments service provider, modernized a legacy BI environment by moving from self-hosted Tableau and replicated data fragments to a serverless Google Cloud analytics stack built on BigQuery and Looker.The company centralized business definitions in LookML, used dbt and Airflow for transformations, and deployed a natural-language chatbot called Transaction Expert on top of the Looker semantic layer so employees could ask plain-text questions about transaction data across countries.HiPay also uses Gemini Enterprise Agent Platform and Google Cloud Agent Builder, including Gemini Flash APIs for OCR workflows, and plans to extend conversational agents and threshold alerts while keeping governance and security tied to the semantic layer.
HiPay Enhances Payment Services with Google Cloud AI and BigQuery
HiPay, a French payment service provider offering over 50 payment solutions, faced challenges managing growing transaction volumes and optimizing internal processes.By outsourcing infrastructure management to Google Cloud with a hybrid cloud strategy, HiPay improved resilience, scalability, and developer productivity.HiPay leveraged Google Cloud's BigQuery, Vertex AI, PaLM 2, Cloud Vision, and Document AI to develop AI-driven anti-fraud and risk-scoring models and automate internal processes, reducing manual errors and technical debt.