Conversational analytics

Google Cloud Support centralized fragmented support BI in Looker on Google Cloud with BigQuery and Gemini Enterprise conversational analytics.The team shifted to governed semantic metrics and self-service conversational analytics to reduce BI bottlenecks, improve consistency, and speed decision-making for about 5,000 monthly active users.

Google Cloud SupportTech & Comms

The Stakeholder Company (TSC) built Genie, a cloud-native intelligence platform that unifies global media signals and proprietary stakeholder data for Fortune 500 and other clients.The platform uses BigQuery as a primary data warehouse and vector store, Gemini in Vertex AI for conversational reasoning, and GKE-based microservices to deliver a secure AI co-pilot for strategic dialogue with data.The system links real-time signals across 95 countries and millions of stakeholder data points to support evidence-based decision making.

The Stakeholder CompanyProfessional Services

Global engineering and consulting firm Tetra Tech consolidated 100+ ERP systems into Oracle E-Business Suite hosted on Oracle Cloud Infrastructure and then built an interactive analytics platform on Oracle Autonomous AI Lakehouse and OCI Generative AI so business users could access complex finance and project performance metrics on demand.Using Oracle Digital Assistant with text, chat, and voice interfaces, the platform lets finance and project teams ask nuanced questions in natural language, minimizes information delays and inconsistencies, and provides secure access to metrics across projects, customers, and regions.Oracle APEX and Oracle Analytics support role-based reporting, while the company is also planning AI agents for routine tasks such as transaction error fixing and time-entry streamlining.

Tetra TechProfessional Services

Customedia and its sister brand Custom Analytics moved from an incumbent platform to Google Cloud to improve the reliability and scalability of advertising data ingestion and client reporting.The solution combines BigQuery, Looker, Gemini, Cloud Run, and Cloud Storage to build a unified data stack, self-service reporting, and natural-language analytics for media and creative briefs.The companies report better customer trust and a 90% reduction in data ingestion failures.

CustomediaProfessional Services

Audika, Demant’s Hearing Care division, implemented Looker to create a modern self-service data environment that democratizes access while preserving global governance for dashboard logic.A semantic layer centralizes definitions so local teams can customize dashboards without breaking core logic, and a pilot plus repeatable onboarding playbook helped scale adoption across 10 markets and 3,000+ users.The organization also established a governed data foundation to support future Vertex AI predictive modeling and conversational analytics.

AudikaHealthcare

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.

HiPayFinance

AWS Technical Field Communities (TFC) built TARA, a conversational analytics assistant for internal operational decision support. It lets program leaders and field teams ask complex, multi-dimensional questions in natural language across multiple datasets instead of waiting for BI engineers to update dashboards.TARA combines Amazon Quick chat agent capabilities, Dataset Q&A, Quick Spaces, Quick Actions, and MCP integrations to unify curated datasets, live operational systems, and domain-specific research agents in a single interface. The article says the team was an early adopter of Dataset Q&A and used semantic definitions embedded at the dataset level to generate SQL at query time.The post emphasizes safe access for PII-sensitive information, real-time operational context, and explainable analytics for leaders making staffing, engagement, and performance decisions.

AWS Technical Field CommunitiesOther