Cool stuff Google Cloud customers built, Dec. edition: AI for better toys, reliable mapping tech, Gemini stumps an all-star & more

AI and cloud technology are reshaping every corner of every industry around the world. Without our customers, there would be no Google Cloud, as they are the ones building the future on our platform. In this regular round-up, we dive into some of the exciting projects redefining businesses, shaping industries, and creating new categories.  For our latest edition, we look into how Waze made its network more reliable; NBA superstar Stephen Curry gets quizzed by Gemini; a financial market transformation at CME Group; a multi-agent business forecasting platform from AppOrchid; Mattel crunches customer feedback with AI; VMO2 uses decentralized contracts for reliable data; Mercado Libre’s strategic use of Spanner; and how Ericsson enhances data governance. Be sure to check back next year to see how more industry leaders and exciting startups are putting Google Cloud technologies to use. And if you haven’t already, please peruse our list of 1,001 real-world gen AI use cases from our customers. Waze keeps traffic flowing with Memorystore Who: Waze (a division of Google parent company Alphabet) is a community-driven, crowd-sourced navigation app with tens of millions of users who share real-time data to provide optimal driving routes, traffic updates, and alerts for hazards, police, and more. What they did: Waze depends on vast volumes of dynamic, real-time user session data to power its core navigation features, but scaling that data to support concurrent users worldwide required a new approach. Their team built a centralized Session Server backed by Memorystore for Redis Cluster, a fully managed service with 99.99% availability that supports partial updates and easily scales to Waze’s use case of over 1 million MGET commands per second with ~1ms latency. Why it matters: Moving from Memcached’s 99.9% SLA to Memorystore for Redis Cluster’s 99.99% means higher availability and resiliency from the service. And because Memorystore for Redis supports partial updates, Waze can change individual fields within a session object rather than rewriting the entire record. That reduces network traffic, speeds up write performance, and makes the system more efficient overall. Learn from us: “Real-time data drives the Waze app experience. Our turn-by-turn guidance, accident rerouting, and driver alerts depend on up-to-the-millisecond accuracy. But keeping that experience seamless for millions of concurrent sessions requires robust and battle hardened infrastructure that is built to manage a massive stream of user session data.” – Eden Levin, Waze BE infrastructure developer & Yuval Kamran Waze site reliability engineer What Stephen Curry learned from a custom Gemini agent Who: Stephen Curry is arguably of the greatest three-point shooter of all-time in the NBA — as well as Google’s performance advisor and an all-around stats-obsessive. What they did: For a special engagement with Curry, the Google Cloud team wanted to showcase the power of Gemini for creative thinking, analysis, and data mining. They took every regular season, play-in, and playoff game from Curry’s career (through the end of the 2024-2025 season) and input the data into a custom-built agent using Google Cloud’s Agent Development Kit and Gemini APIs.The system could then be queried for obscure stats, to see if the team could stump Curry and teach him more about his game. Why it matters: For example, it found that his three-point shooting percentage after more than seven dribbles, with a minimum 105 attempts was 40.2%, and how many points Curry generated for his teammates off of screens since 2013: 1,105. Instead of countless hours of manual research, the team got query results in less than a minute. Some queries were so obscure, the team wouldn’t have reached a valid answer without the ability of the agent to analyze the rich data. Learn from us: “Gemini is going to be in my head this year, cause I'm going to be looking at all these details.” – Stephen Curry, Golden State Warriors point guard and 4x NBA champ How CME Group builds a faster, smarter exchange Who: CME Group has evolved from a nineteenth-century commodities exchange into one of the most advanced financial market infrastructures in the world. To support real-time trading and risk management at a global scale, the company launched a strategic partnership with Google Cloud. What they did: By migrating to Cloud SQL and adopting AI-powered insights, CME Group empowered developers, paid down technical debt, and unlocked new opportunities for data-driven innovation across financial markets. Why it matters: Cloud SQL has given CME a foundation for increased developer and team agility. Fewer performance issues mean more time focused on innovation: expanding CME’s analytics capabilities, accelerating AI initiatives, and exploring new ways to commercialize data responsibly. When teams stopped chasing outages, they unlocked more time to take bigger bets and build the future. Learn from us: “With Cloud SQL, we’ve found a way to keep our data layer as fast and dependable as the markets we serve. Cloud SQL gives our teams real-time visibility into what’s happening inside the database. When an application slows, we can identify the root cause in minutes instead of hours. Those insights are built into the platform, which means we don’t need custom tooling or manual analysis to keep operations steady.” – Kristofer Shane Sikora, Executive Director, Cloud Data Engineering, CME Group AppOrchid’s multi-agent system for superior business forecasting Who: App Orchid is an enterprise AI builder and a leader in making data actionable with AI, with a mission to make AI a force for good. Their goal is to empower every employee with trusted, understandable, and accessible data. What they did: The business forecasting agent is actually built on the foundation of two powerful, specialized AI agents: a prediction agent built by Google Cloud and App Orchid’s Data Agent offering. These agents work in concert to solve complex business problems, acting as complementary specialists. App Orchid’s agent possesses unparalleled understanding of an enterprise's past and present, while Google’s agent brings world-class capabilities in predicting the future. Why it matters: Adopting a multi-agent approach provides clear, tangible advantages that directly address the forecasting problems that often plague businesses, including improved accuracy; increased operational efficiency; faster insights; and reduced costs and increased revenue; and greater agility and adaptability. Neither of the underlying agents could achieve these results on their own, but working together, this agent is more than the sum of its subagents. Learn from us: “As the agentic era gets underway, it is evolving quickly. Our multi-agent approach demonstrates both how true agentic systems are most successful when multiple agents are at play, and the importance of finding strong partners with distinct capabilities to help build and assemble these agentic systems.” – Brian Mills, Director, Enterprise AI, Google Cloud & Taka Shinagawa, Gen AI Field Solution Architect, Google Cloud How Mattel uses AI for real-time product updates Who: Virgin Media O2 is one of Europe’s largest telecommunications and media providers, with 45.8 million broadband, mobile, phone, and home subscribers across the UK. To build AI products that are adaptable and data-driven, they needed a decentralized system that internal customers could count on for clean, reliable data. What they did: To improve its understanding of consumer sentiment, Mattel developed an AI-powered feedback classification system, which can analyze millions of customer interactions from a diverse range of sources in a matter of seconds. At its core, the system relies on BigQuery for storing and efficiently processing its massive customer datasets and then utilizes Vertex AI and Google’s multimodal Gemini models to refine and train the sophisticated consumer feedback model. Why it matters: Already, the new AI-powered system has delivered significant wins, delivering a staggering 100x boost in data processing capacity and reducing analysis times from a month to a single minute. By automating the analysis of many processes, analysts are now freed from the noise of everyday tasks, enabling them to focus on deeper research across the company’s iconic portfolio brand. Learn from us: “Our big motto is ‘From months to minutes,’ but it’s real. We were literally spending months-worth of analysis and just getting data into the place that an analyst could tally up all the sentiment — and now it’s just at our fingertips.” – Shaun Applegate, Director of Product Quality Analytics, Mattel Virgin Media O2 uses data contracts for scalable AI products Who: Virgin Media O2 is one of Europe’s largest telecommunications and media providers, with 45.8 million broadband, mobile, phone, and home subscribers across the UK. To build AI products that are adaptable and data-driven, they needed a decentralized system that internal customers could count on for clean, reliable data. What they did: New decentralized data contracts, built with Dataplex, serve as the data quality and assurance layer for VMO2’s data products; these ensure every dataset they publish is reliable, documented, and ready for consumption. Defined at the asset level, such as individual BigQuery tables or Google Cloud Storage buckets, data contracts are redefining how VMO2 manage and share data, enabling the creation of trusted and scalable AI products across their data mesh. Why it matters: The power of this approach lies in moving beyond static documentation. Because they are machine-readable, data contracts become living guarantees with continuous enforcement and real-time validation directly within data pipelines. This proactive monitoring allows teams to detect schema changes or SLA breaches early, transforming data quality from a reactive fix into a scalable, automated mechanism. Learn...