AI and cloud technology are reshaping every corner of every industry around the world. Without our customers, who are building the future on our platform, there would be no Google
Cloud. 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 learn how Urban Outfitters sped up its order management; BASF uses AlphaEvolve algorithms to map global supply chains; the unification strategy for UKG’s workforce intelligence; WPP’s secrets to training humanoid robot camera operators; how Breuninger piloted Virtual Try-On APIs; creating automated video clips with Glance; and Movix improves the production of dental aligners. Be sure to check back next month 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 1,302 real-world gen AI use cases from our customers.
Who: Urban Outfitters, Inc. (URBN), the popular clothing and home goods retailer, relies on IBM Sterling OMS as the nerve center of its global ecommerce operations. However, the foundation of this critical system — a massive 11TB Oracle database — was increasingly becoming a bottleneck.
What they did: URBN completed a major infrastructure upgrade, migrating its IBM Sterling OMS from an Oracle database to
Why it matters: The migration to AlloyDB has fundamentally reshaped URBN’s data strategy, delivering a more favorable total cost of ownership through an optimized storage and compute architecture, without sacrificing performance or reliability. Furthermore, the shift to a PostgreSQL-compatible database gave URBN the flexibility of an open-source ecosystem, providing freedom from vendor lock-in, as well as **significant speed improvements **that enhanced responsiveness.
Learn from us: "URBN’s successful migration serves as a blueprint for organizations looking to modernize their mission-critical infrastructure and future-proof their environment for AI expansion. This journey proves that even the most complex, mission-critical migrations can be achieved through deep cross-organizational partnership and a phased, risk-mitigated approach." – Rob Frieman, CIO, Urban Outfitters &** Raj Pai**, VP, Product Management, Databases, Google Cloud
Who: BASF Agricultural Solutions manages a complex network of 180 production sites with more than 5,000 distinct value chains. Currently, human planners make thousands of local decisions every day on what to produce, when to produce it, and how much safety stock to hold.
What they did: To understand how local decisions ripple across their entire global network, BASF turned to
Why it matters: By running thousands of experiments, AlphaEvolve developed a clear, human-readable algorithm that explains how the BASF network truly operates. The final algorithm successfully mirrored the actual historical performance of the supply chain, significantly reducing the error rates compared to the initial seed model. It automatically discovered factually correct, domain-specific supply chain rules, providing a clear foundation for optimizing asset utilization globally.
Learn from us: “We had several attempts to build a digital twin. … By using AlphaEvolve, we cannot only map the complex network based on system data, but at the same time understand and copy the human decisions that drive our daily operations.” – Dr. Goetz Krabbe, vice president for global supply chain at BASF
Who: UKG is one of the leading providers of human capital management (HCM) and workforce management (WFM) solutions, but years of growth led to backend sprawl. They have 126 application teams, dozens of tech stacks, and more than 12,000 database instances.
What they did: To bring the full UKG suite onto one real-time foundation, the company built People Fabric, a new data and intelligence platform powered by
Why it matters: People Fabric gives UKG a complete and consistent view of people, work, pay, and culture data that’s updated continuously and ready for AI to use in real time. For engineering teams, People Fabric acts as a database-as-a-service that accelerates development and supports modernization without customer disruption. Additionally, migrating core person and employment data off their on-prem monolith has generated cost savings significant enough to fund half of People Fabric.
Learn from us: “ As we continue expanding People Fabric, we’re laying the groundwork for deeper agentic automation, more responsive analytics, and a growing set of AI-driven capabilities — all on a trusted, scalable foundation built for what’s next.” – Radhi Chagarlamudi, Group Vice President, Product Engineering, UKG & Heather White, Cloud Data Architect, Google Cloud
Who: WPP is one of the world’s largest marketing organizations, handling $70 billion of media for enterprise clients. They work on some of the most complex commercial film shoots and were eager to test the viability of robotic cameras to capture more footage, but this required complex training of physical models AI.
What they did: WPP used the new Why it matters: WPP was able to utilize a P2P topology that moves data directly between GPUs without the bottleneck of central processing. They saw speed increases in excess of 10x, taking training times down to less than one hour. Through high-volume simulation, the humanoid robots learned how to respond to small changes and bridge the tough "sim-to-real" gap, helping ensure the robot's simulated adaptability translated to safety and stability in the real world.
Learn from us: "Our process for mastering complex, natural movement on a film set can be replicated across industries to overcome the massive computational complexity of training robots." – Perry Nightingale, SVP of Creative AI, WPP
Who: Breuninger, a fashion and lifestyle company based in Germany, thought emerging generative media models could be a good fit to answer the question every online fashion shopper asks: "How will this look on me?"
What they did: Working with Google Cloud, they built a virtual try-on experience that lets shoppers see high-end fashion on their own bodies using a simple selfie. Using the
Why it matters: During a six-week A/B test over Black Week and the holiday season, the team found that shoppers who used the virtual try-on **converted purchases at a higher rate **than those who didn't. Customer surveys reinforced the numbers: shoppers responded well to the high image quality and the personalized experience.
**Learn from us: **“Breuninger continues to refine the experience based on how customers actually use virtual try-on in everyday shopping — the same user-first approach that shaped the project from the start.” – Daniel Rascher, Senior Product Owner, Breuninger & Dr. Michael Menzel, Customer AI Specialist, Google Cloud
Who: Glance, a mobile-first content platform, processes 1-2 hour videos from sources like podcasts, news reports, movies, and web series, and transforms them into 30 to 180-second vertical clips optimized for mobile lock screens.
What they did: The goal was to create a complete pipeline that takes a long-form landscape video (16:9) and outputs multiple ready-to-publish short-form portrait videos (9:16). The final technical solution uses
Why it matters: With daily volume projected to grow from 3,500 to over 10,000 videos per day, manual editing wasn’t a realistic path forward. Glance’s video pipeline demonstrates what becomes possible when AI handles the repetitive, judgement-intensive work of video editing. The system transforms thousands of long-form videos into mobile-ready clips each day, preserving narrative context while optimizing for vertical viewing. Rather than choosing between scale and quality, automated pipelines can deliver both.** Learn from us:** “Glance’s video pipeline demonstrates what becomes possible when AI handles the repetitive, judgement-intensive work of video editing. … The approach offers a template for any organization sitting on long-form video archives. Rather than choosing between scale and quality, automated pipelines can deliver both.” – Himanshu Aggarwal,
Machine Learning Engineer, Glance & Sharmila Devi, AI Consulting Lead, Google Cloud
Who: Movix is building one of the first agentic AI solutions for dental appliance manufacturers and dental labs, to help solve a serious shortage of skilled dental technicians in aligner manufacturing.
What they did: Movix developed custom models for deep learning, computer vision, and 3D mesh analysis over a five-month period, using Google Cloud infrastructure. Once defects are detected, they use the
Why it matters: Movix’s agentic solutions automate data entry and quality control, which are traditionally manual, time-consuming, and error-prone tasks. The automation and higher level of accuracy the QC agent delivers can save $300 per remake for an aligner manufacturer, and speed up the appliance manufacturing process with quicker turnaround times.
Learn from us: “We plan to build hybrid solutions … designing an architecture that connects our cloud-based AI agents with older, on-premises software that many conservative labs still use — through lightweight local connectors and standardized APIs. This will allow us to access a large market segment that has not yet migrated to the cloud.” – Marina Domracheva,** **CEO, Movix & **Bakit Dzhumagulov, **CTO, Movix