Customers

How Yahoo optimizes resources with flexible VMs in Managed Service for Apache Spark

Fri, 04 Sep 2026 16:00:00 +0000

As a global media and technology company connecting hundreds of millions of users to finance, sports, and entertainment platforms, Yahoo operates a massive data infrastructure where analytics workloads must run continuously at high speed. In deadline-driven data environments, relying on fixed virtual machine (VM) configurations creates a brittle system; if a specific machine shape faces a regional capacity constraint, cluster provisioning in Managed Service for Apache Spark (formerly Dataproc) can experience delays and stall critical data pipelines.

Yahoo utilizes flexible VMs in Managed Service for Apache Spark clusters to automatically absorb these resource fluctuations by defining a ranked list of acceptable VM shapes. This allows the system to dynamically search regional zones and maintain pipeline execution without manual intervention. To search for capacity across a region, teams must also enable Auto-Zone placement.

This optimization builds on Yahoo's broader data modernization journey, which involved migrating on-premises Hadoop and big data estates directly to Google Cloud. By transitioning those legacy workloads, the team established a cloud foundation capable of running high-scale batch and streaming analytics with dynamic resource flexibility.

This post provides a technical blueprint for configuring flexible VM instance rankings in Managed Service for Apache Spark to automatically manage capacity constraints and maintain pipeline execution.

Operational trade-offs of static configurations

Configuring clusters with a single, fixed machine type in a specific zone introduces constraints when regional zonal capacity fluctuations occur, potentially impacting cluster provisioning. Rather than manage these capacity variations through custom retry logic or manual intervention, using flexible configurations allows your infrastructure to automatically adapt. By accepting multiple VM shapes and searching across zones in the selected region, flexible configurations help streamline provisioning to better support high-scale analytics workloads.

Rules for configuring flexible clusters

Deploying flexible configurations requires aligning several connected design choices:

Enable auto-zone placement: You must pass a region(--region=${REGION}) or an empty zone string (--zone="") so Managed Spark can search for available capacity across the entire region.
Maintain core and memory symmetry: If your Managed Spark cluster uses autoscaling, all machine types in your flexible list must share a similar core count and memory size, even if they come from different VM families. A uniform CPU-to-memory ratio across primary and secondary workers prevents performance degradation, as the smallest ratio determines your effective container sizing.
Align component properties: Managed Spark calculates system properties based on VM cores and memory. When mixing machine shapes, you may need explicit property overrides to keep YARN and Spark resource allocations aligned with your expected worker behavior.

Two ways flexible VMs support massive workloads

For large-scale data environments, flexible configurations support operations in two ways:

Higher cluster creation success: Instead of failing when a preferred VM type is out of stock, Managed Spark selects from a ranked list to keep provisioning moving.
Better regional resource use: Auto-zone placement searches the entire region to find capacity, which reduces provisioning friction during high-demand periods.

gcloud example

code_block: <ListValue: [StructValue([('code', 'gcloud dataproc clusters create analytics-cluster \\\r\n --region=us-central1 \\\r\n --zone="" \\\r\n --num-workers=10 \\\r\n --master-instance-selection=\'{"machineTypes":["e2-standard-8"],"rank":0}\' \\\r\n --master-instance-selection=\'{"machineTypes":["n2-standard-8"],"rank":1}\' \\\r\n --worker-instance-selection=\'{"machineTypes":["e2-standard-8"],"rank":0}\' \\\r\n --worker-instance-selection=\'{"machineTypes":["n2-standard-8"],"rank":1}'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f1e0698b280>)])]>

API example

You can also build this capacity policy into your automated pipelines or Managed Service for Apache Airflow DAGS using the instanceFlexibilityPolicy field in the ‘Dataproc’ API:

code_block: <ListValue: [StructValue([('code', '{\r\n "projectId": "PROJECT_ID",\r\n "clusterName": "analytics-cluster",\r\n "config": {\r\n "gceClusterConfig": {\r\n "zoneUri": ""\r\n },\r\n "secondaryWorkerConfig": {\r\n "numInstances": 8,\r\n "instanceFlexibilityPolicy": {\r\n "instanceSelectionList": [\r\n {\r\n "machineTypes": ["n2-standard-8"],\r\n "rank": 0\r\n },\r\n {\r\n "machineTypes": ["e2-standard-8", "t2d-standard-8"],\r\n "rank": 1\r\n }\r\n ]\r\n }\r\n }\r\n }\r\n}'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f1e0698be20>)])]>

This API policy achieves the same goal: it establishes your preferred shape, documents valid fallbacks, and lets Managed Spark resolve resource constraints without breaking your automation scripts.

Establishing an infrastructure policy

Managing data at this scale requires standardizing a clear resource policy rather than relying on a single rigid machine type. Your configuration standards should outline:

Preferred and fallback VM families for secondary workers.
Default auto-zone placement to enable flexible provisioning.
Identical core and memory configurations when using autoscaling.
Uniform CPU-to-memory ratios across all worker groups to maintain predictable container sizing.
Explicit YARN or Spark property overrides to guarantee consistent runtime behavior across different machine lines.
Shuffle-safe patterns for Spark workloads running on Spot or highly elastic capacity.

By adopting flexible configurations, you turn infrastructure scarcity into a predictable fallback plan, keeping your critical data pipelines up and running.

Yahoo impact and results

By implementing flexible VMs in Managed Service for Apache Spark, Yahoo successfully reduced cluster provisioning failures by 85% which were caused by regional capacity stockouts. This flexible configuration allows their data infrastructure to automatically handle capacity constraints and successfully provision resources without requiring manual intervention. As a result, Yahoo ensures continuous workload execution and prevents downstream processing delays across their massive data pipelines.

"Managing high-scale data analytics at Yahoo requires resilient, automated infrastructure. Moving to flexible VMs in Managed Service for Apache Spark has transformed our approach; instead of stalling when a specific machine shape faces capacity constraints, our clusters now automatically pivot to our ranked fallback options. This has helped us reduce provisioning failures by 85%, providing the reliability we need to keep our global media platforms running smoothly." - Akshay Jain, Senior Software Developer Engineer, Yahoo!

Strategic benefits of flexible infrastructure

Adopting a flexible compute stack transforms your environment into a dynamic pool of resources that adapts to your operational needs. By moving away from rigid, single-machine type configurations, you ensure that your workloads reliably access the compute they need, regardless of supply fluctuations. This shift not only maximizes workload obtainability and reliability but also facilitates seamless hardware modernization by allowing you to prioritize newer VM generations while maintaining older types as reliable fallback options.

Build your resilient data pipeline

Transitioning to a fluid compute strategy ensures your critical analytics remain operational despite regional resource shifts. Here is how you can begin optimizing your infrastructure today:

Audit your workloads: Identify applications tightly coupled to specific VM families or zones and map out viable alternative hardware shapes.
Standardize resource policies: Explore the documentation for Managed Spark flexible VMs to establish your preferred and fallback VM families.
Align financial strategy: Utilize Flexible Committed Use Discounts (Flex CUDs) to maintain cost predictability when workloads dynamically pivot to alternative machine types.
Claim your credits: New customers may be eligible for $300 in credits to try Managed Service for Apache Spark and other Google Cloud products at no cost.

How BlackLine simplifies perimeter policy intelligence with VPC Service Controls

Tue, 01 Sep 2026 16:00:00 +0000

Establishing network-level perimeters with VPC Service Controls (VPC-SC) is a critical step that can help you protect your cloud environment against data exfiltration, compromised accounts, and insider threats.

Today, Google Cloud is excited to share new policy intelligence capabilities in VPC-SC that can help drive even greater operational simplicity. With our latest release of the VPC-SC violation analyzer and violation dashboard, we have simplified policy management and troubleshooting, to make managing and optimizing your security perimeter more efficient and straightforward than ever.

How BlackLine streamlines incident response

BlackLine, a leader in financial operations management, adopted the VPC-SC policy intelligence solution to maintain strict security perimeters. Chosen by over half of Fortune 500 companies, BlackLine uses Google Cloud's full suite of managed services and built-in security capabilities to protect sensitive customer financial data.

VPC Service Controls are the foundation of BlackLine's preventative compliance and security controls in our Google Cloud environment, helping us to mitigate data exfiltration risks and ensure clear separation between our higher and lower environments by establishing strong security perimeters.

Managing these complex perimeters is a continuous process. VPC Service Controls violation analyzer helps BlackLine cloud infrastructure administrators adapt to changing API connection requirements of the business by adjusting security perimeters through approved access levels, ingress policies, and egress policies.

With only the troubleshooting token or unique ID from any VPC-SC violation error message, we can produce a detailed report identifying the principals and target resources involved in a failed API request, and explaining why and how that API request violated BlackLine's service perimeters. We don’t need to write a Cloud Logging SQL query to extract the data.

The clear access context and actionable insights in the violation details report are an invaluable starting point as we collaborate to resolve violations, significantly reducing our mean-time-to-resolution (MTTR) for service perimeter issues, and helping BlackLine maintain our focus on our customers and continue to innovate on their behalf.

Streamlining the perimeter operations lifecycle

Our new policy intelligence tools — the VPC-SC Violation analyzer and Violation dashboard — simplify real-time monitoring and active incident response. These tools provide clear, actionable insights in the Google Cloud Console, offering greater speed and automation to help you confidently enforce least-privilege perimeters, and quickly resolve access denials.

Violation Dashboard aggregates and visualizes all service perimeter violations across your entire Google Cloud organization in a single pane of glass, helping your team identify trends, spot spikes in access denials, and shareable filters on violations by specific perimeters, projects, or identities.

Violation Analyzer streamlines investigating violations, eliminating the need to query Cloud Logging and manually piece together the details. When you click a troubleshooting token from the dashboard (or input a unique denial ID), the analyzer maps out the identity, source, target, and VPC-SC rule triggered, creating a report telling you why that specific request was blocked. This helps your team more quickly take action to determine whether to modify existing policy rules or create a new one, and resolve incidents more quickly.

Together, the new VPC Service Controls policy intelligence tools go beyond automated log analysis to provide unified visibility of violations and actionable insights to investigate them, making your perimeter deployment and management simpler and lower-risk.

Streamlining the VPC Service Controls lifecycle, from deployment to policy refinement.

With the new VPC-SC troubleshooting tools you can more easily:

Test new perimeters (deployment): Use the violation dashboard to visualize the impact of a service perimeter during your initial dry run phase, helping to verify that enforcement is accurate and predictable before it affects production traffic. Filter violations to track and resolve with prebuilt contextual filters for principals, service perimeters, enforcement type, and more.
Track perimeter denials (monitor): The violation dashboard offers a unified view of your perimeter health, allowing your security operations team to monitor status in real time, including dynamic agentic access denials.
Triage an event (investigate): Violation analyzer provides the identity, source, target, and operations for any violation. It cross-references identity and access management (IAM) permissions, resource ancestry, and context evaluation to identify which rule was triggered, reducing manual effort.
Fix the rule (refine policy): Instead of searching through configuration files, violation analyzer maps violations directly to the relevant line in your VPC-SC policy, allowing you to make updates more quickly and with less manual overhead.

The VPC Service Controls violation dashboard produces detailed reports to jump-start perimeter access investigations that are simplified using the violation analyzer.

Core VPC-SC operations: Simple perimeter enforcement

Our new troubleshooting capabilities build on VPC Service Controls’ foundational simplicity for designing, enforcing, and managing strong perimeters.

By using dry run mode, your teams can build precise, contextual ingress and egress rules based on observed traffic — without disrupting vital business workflows. Once you validate these access patterns, moving to full enforcement becomes a more confident, data-driven process. To keep perimeter maintenance more efficient and straightforward, scoped policies allow you to delegate management directly to project-level administrators, empowering the teams closest to the workload.

Getting started

Simplify data security with VPC Service Controls. With the new Violation Analyzer and Violation dashboard, you can spend less time investigating incidents and more time safely scaling your cloud initiatives. Your data is your most valuable asset — protect it with a perimeter that’s as simple to manage as it is effective in enforcing controls.

Learn more and get started with the VPC-SC violation analyzer and violation dashboard in our documentation.

Reimagining work: How Pythian’s internal AI playbook delivers customer ROI

Thu, 27 Aug 2026 16:00:00 +0000

When Pythian rolled out Google Cloud’s Gemini Enterprise across our 500-person company in 27 countries, the goal was simple: use our own company as a proving ground to discover how enterprise AI actually delivers ROI.

What we found changed our strategy entirely.

Since the rollout of Gemini Enterprise and our previous enterprise AI deployments, Pythian observed firsthand why so many enterprise AI initiatives stall out or fail.

Most organizations trap themselves in a tool-centric mindset — buying licenses, making tools broadly available, and assuming value will naturally follow. They get stuck chasing "nickel and dime" micro-efficiencies (like saving 5 minutes per user) while missing structural, high-ROI workflow transformations. Compounding the problem, even when custom agents are built, they frequently stall in pilot mode or break down in production because teams lack the operational capability to manage AI model drift, agent lifecycles, and ongoing observability.

To solve this, we engineered the Pythian AI Operating Model — a multifaceted, end-to-end framework designed to take enterprise AI from high-level strategy all the way into sustained production. While our dual center of excellence (COE) serves as the core execution muscle, it is the application of the entire framework, from Field CTO strategy and tooling deployment to the dual COE and XOps, that consistently unlocks million-dollar outcomes.

By proving this complete model internally first, Pythian drove a 3x surge in active user engagement and cut our database incident resolution times by 80%.

The four pillars of the Pythian AI operating model

To move past the common failure points of enterprise AI, our framework consolidates strategy, execution, and operations into a single continuous loop:

Field CTO strategy ──> tooling deployment ──> dual COE execution ──> production XOps

Field CTO strategy and governance: Generative AI is arguably the most academically challenging architectural shift in IT history. Led by former C-suite tech leaders, our Field CTO practice provides executive advisory to establish steering committees and clear value metrics. The team audits operations using 16 horizontal agentic patterns (like automated document processing and runbook creation) to build a prioritized backlog of high-ROI use cases before development starts.
Tooling and platform deployment: The team establishes a secure, production-grade foundation on platforms like Gemini Enterprise and connects AI directly into CRMs, ERPs, and database estates to ground models in real corporate context.
The dualCOE: This execution muscle is split into two specialized engines:

People productivity COE: This group handles adoption and change management. Instead of expecting non-technical teams (like HR or Procurement) to build its own agents, this COE builds no-code agents for them, focusing entirely on enablement.
Process productivity COE: This team engineers deep, custom-coded AI agents and complex agentic workflows that integrate into core data platforms for autonomous operations.

XOps (AI production management): While deploying an agent is 20% of the journey, maintaining accuracy in production is 80%. Because AI models and prompt structures naturally drift over time, this XOps practice provides the continuous monitoring, prompt tuning, and model observability needed to keep agents performing without breaking core workflows.

The difference between chasing minor, scattered efficiencies and driving structural enterprise ROI comes down to how you align your operating strategy:

Alignment element	Tool-centric approach	Pythian AI operating model
Primary metric	Individual minutes saved per user	High-impact workflow reimagination and ROI
Operational focus	Broad, unguided tool availability	Prioritized backlog via 16 agentic patterns
Execution muscle	Ad-hoc user experimentation	Dual COE (people and process productivity)
Production lifecycle	Unmonitored static deployments	Active XOps (Continuous accuracy and drift management)

Real-world impact: from database ops to global supply chains

Whether managing 70 manufacturing plants or 30,000 enterprise databases, AI succeeds when tied to structural, high-value workflows:

Pythian “as a customer:” Across 15,000 monthly database tickets, our Process COE deployed an agentic workflow that reads tickets, searches knowledge bases, and auto-generates mini runbooks before an engineer touches them. The result was slashed mean time to resolution by 80% and tripled active user engagement.
Knowledge management customer: We deployed autonomous IT support agents across 10,000 consultants. As a result, we were able to automate 10% of 20,000 annual IT tickets into "no-touch" resolutions, saving 1,000,000+ operational hours.
Supply chain customer: By building custom agentic supply chain tools on Gemini Enterprise, we compressed forecast-matching cycles from weeks down to 2–3 days across 70 global manufacturing sites.
Retail customer: We combined Gemini Agentic AI and computer vision to automate store product onboarding. As a result, we transformed a 20-minute manual task into a multi-second flow.

Ready to build your AI operating model?

Scaling AI demands more than tool-level experimentation. It also requires an end-to-end AI operating model. Learn how Pythian pairs with Google Cloud to operationalize strategy, streamline XOps, and fast-track your Gemini Enterprise journey.

How Uber improves network reliability while unblocking cloud migration

Wed, 26 Aug 2026 16:00:00 +0000

Uber has a lot in common with the cities it serves. Both are always changing and growing, both must carefully manage the resulting traffic to prevent congestion and sprawl.

Uber has continuously evolved its technical strategies to manage its expanding network, and this careful planning and constant evolution helps ensure that application traffic across its entire platform runs smoothly. Ultimately, maintaining a reliable, high-scale platform that operates seamlessly at any given time is key to preserving user trust.

One important solution in this effort has been application awareness on Cloud Interconnect. An industry-first tool for application prioritization across hybrid networks, application awareness on Cloud Interconnect has helped Uber prioritize critical traffic to ensure business continuity during potential network congestion events.

Uber acted as an early design partner for application awareness on Cloud Interconnect, helping ensure that this capability met the demands of Uber’s global-scale operations. It not only improved Uber’s daily operations, it also gave Uber the confidence to move forward with a Google Cloud migration, with confidence that there would be less risk of service interruptions during switchovers.

In this post, we’ll explain the features Uber most sought and why, the inner workings of application awareness on Cloud Interconnect, and how it can help other organizations as well.

Prioritizing critical traffic

When migrating distributed, hybrid, or multicloud applications at a global scale, network reliability becomes a primary concern. Even the most worthwhile migrations may not seem worth it if such migrations interrupt ongoing service. For organizations like Uber, moving vast amounts of data to support large data analytics workload — including emerging AI use cases — can saturate network links, resulting in increased reliability risk for their critical application traffic.

With standard cloud interconnect approaches, enterprises typically apply simple bandwidth overprovisioning to meet extreme infrastructure needs. But with today's hybrid cloud demands, and given the size of an organization like Uber, overprovisioning network capacity for peak usage is often too costly and unreliable.

The shortcomings of overprovisioning only become magnified with the integration of cutting-edge AI innovations. Uber needs systems in place that can take on massive data transfers without congesting its network and protecting the performance of business-critical applications.

With the benefit of application awareness on Cloud Interconnect, including the four major features of application awareness — traffic handling, congestion response, latency management, and cost efficiency — Uber was able to achieve the networking optimization its modern tech stack requires.

Starting with a private preview, Uber deployed this feature across its infrastructure, beginning with Google Cloud Interconnect deployments in Phoenix, Arizona, and Ashburn, Virginia. Application awareness on Cloud Interconnect allows Uber to classify and prioritize end-user application traffic over less time-sensitive data using DSCP marking and configured queuing profiles.

In the following chart, we look at the four key features of application awareness on Cloud Interconnect, how they differ from legacy approaches, and how they help provide better operational continuity for organizations like Uber.

Feature	Standard interconnect solutions	Application awareness on Cloud Interconnect
Traffic handling	All traffic treated equally (first-in, first-out)	Traffic classified into six distinct traffic classes
Congestion response	High-priority application traffic may be dropped during bursts	Business-critical traffic is protected via strict priority or bandwidth sharing policies
Latency management	Unpredictable latency for high priority applications	Predictable and consistent low-latency for time-sensitive workloads
Cost efficiency	Requires expensive overprovisioning to absorb peaks	Efficient bandwidth utilization and lower TCO

Uber's key takeaways

For Uber, the business value of being able to prioritize business-critical traffic on its networks by deploying application awareness on Cloud Interconnect was immediate. And in doing so, Uber has also created a blueprint that other enterprises with similar hybrid cloud challenges can replicate. The core elements of that blueprint include:

Ensuring business continuity: Uber can decide in real time which application traffic to prioritize during major, high-traffic events. This means that mission critical applications stay up and running during even extreme events (both planned and unplanned). Uber leadership has called application awareness on Cloud Interconnect important for its global operations.
Efficient bandwidth utilization: Instead of blindly overprovisioning bandwidth to prevent congestion, application awareness allows Uber to better utilize their existing Cloud Interconnect capacity aligned with their expected network bandwidth needs. The result is lower total cost of ownership for network infrastructure.
Unblocked workload migration: By protecting critical applications from network congestion, Uber was able to migrate significant workloads to Google Cloud and, in the process, dramatically reduce operational overhead.

"Application awareness on Cloud Interconnect was the key that unlocked our ability to migrate more strategic workloads to Google Cloud and is critical for maintaining service reliability during peak global demand. By allowing us to intelligently prioritize traffic, it helps us ensure that we can protect our higher priority services and make our infrastructure more efficient, lowering our total cost of ownership. This wasn't just a feature deployment; it was a deep engineering partnership that delivered a solution critical to our business." – Harry Liu, Director of Engineering, Uber

Securing network reliability for AI and beyond

As more enterprises integrate cloud-based AI models, distributed applications, and data analytics, it's becoming a business imperative to be ready to handle the massive data transfers that follow. But in doing so, they also have to ensure they never compromise the reliability of their critical applications.

With application awareness on Cloud Interconnect, Uber demonstrated that moving beyond simple bandwidth overprovisioning to protect business-critical traffic was an essential step to building the stability required to embrace modern hybrid and multicloud strategies.

You can read our blog about the potential of Cloud Interconnect across industries to learn more about what the service can bring to your organization, and if you’re ready to explore more, our team of networking and industry experts are ready to help.

How Box is unlocking multimodal enterprise agents with Gemini Embeddings 2

Tue, 18 Aug 2026 16:00:00 +0000

Enterprise content management is experiencing its biggest architectural shift since the cloud migration era.

For years, enterprises have stored trillions of gigabytes of critical data in Box: financial models, clinical trial protocols, M&A due diligence rooms, engineering schematics, and legal compliance playbooks. Up to this point, text-based search and retrieval-augmented generation (RAG) have successfully unlocked the vast narrative knowledge within these repositories, establishing a powerful and highly effective baseline for enterprise AI intelligence.

Traditional RAG architectures have mastered text processing, but the agentic era demands more. The next logical evolution is to extend this framework to capture the inherently multimodal, deeply spatial, and highly structured elements that exist alongside text. While text embeddings excel at indexing prose, multimodal architectures unlock a major new capability: For example, they preserve the strict row-column semantics of financial tables, interpret visual evidence like clinical data, and map the logic of multi-page flowcharts without losing their spatial layout.

To deliver next-generation capabilities that can handle the vast universe of digital content, Google Cloud and Box are integrating advanced multimodal capabilities into Box's Agentic Platform, powered by Gemini Multimodal Embeddings 2 merging Box’s industry-leading Intelligent Content Management platform with Google Cloud’s advanced AI embeddings.

Benefits of improved embedding: Extending the dimensions of document content

Preserving visual and spatial geometry: Complex document elements like multi-column tables or financial matrices rely on their spatial layout to convey meaning. Converting these elements into a flat string of text can disassociate column headers from their corresponding data points. Multimodal embeddings allow systems to interpret the document exactly as a human does, maintaining the integrity of spatial relationships.
Illuminating the visual modality: Enterprise documents are filled with visual indicators: technical charts, process flowcharts, branding assets, and product photography. Multimodal capabilities ensure that these elements are no longer invisible to search systems, allowing users to query images and text simultaneously.
Connecting hybrid file formats: Real-world business workflows rarely live in a single document format. An agent may need to cross-reference a PDF policy, a spreadsheet tracking log, and a presentation deck. Extending RAG with multimodal embeddings creates a unified understanding across these varied formats.

The Architectural Solution: Gemini Multimodal Embeddings 2

Google Cloud’s Gemini Multimodal Embeddings 2 introduces a unified, multimodal vector space capable of embedding text, raster images, document pages, rendered spreadsheet tables, and visual charts into the same semantic representation space.

Key product capabilities unlocked by gemini-embeddings-2:

Crossmodal retrieval (text-to-visual / visual-to-text): Enables natural language queries to retrieve highly specific visual components, such as locating a target chart or diagram within a massive library of slides, without requiring manual tagging.
Layout-aware document embedding: Rather than breaking files into arbitrary text blocks, the system can embed document page renderings directly, preserving visual hierarchies, callout boxes, and structural context.
Heterogeneous format bridging: Native support for seamlessly bridging content across .docx, .xlsx, .pdf, .pptx, .png, and .csv without losing modality-specific structural information.

Three core patterns of multimodal enterprise agents

By leveraging multimodal embeddings within Box, we have identified three uniqueprimary design patterns that illustrate how organizations can extend traditional RAG to support complex, visual workflows.

Pattern 1: Complex financial & analytical reporting

The challenge

Corporate finance, research, and audit teams analyze highly structured documents where vital data resides in embedded tables, growth charts, and footnote annotations. Text-only indexing can separate these numbers from their context, making automated analysis challenging.

The multimodal advantage

Structural alignment: The embedding model captures the physical structure of tables and charts, allowing financial agents to understand that a column header applies to a specific row of metrics.
Visual trend analysis: Agents can cross-reference written summaries with visual trends in accompanying bar or line charts, identifying and pointing out discrepancies between written claims and source data.
Contextual sourcing: Users can query complex portfolios and instantly retrieve the exact page, table, or chart supporting a specific metric.

Pattern 2: Multimodal clinical decision support & assisted diagnosis

The challenge

In healthcare and clinical environments, critical patient data is fragmented across vastly different, unstructured visual and textual formats — ranging from external physical photos (visual evidence) and microscopic pathology slides (lab reports) to structured risk matrices (triage grids). Traditional text-based systems or isolated analysis tools cannot synthesize these cross-modal relationships simultaneously, which can delay critical diagnoses or risk missing immediate, life-threatening procedural complications.

The multimodal advantage

Cross-modal clinical synthesis: Evaluates physical symptoms alongside cellular-level laboratory evidence simultaneously by indexing clinical photos, histopathology imagery, and triage grids into a single space.
Granular anomaly identification: Connects niche visual patterns under a microscope (like parasitic cyst walls) with medical knowledge to rapidly isolate rare conditions.
Risk-aware decision support: Cross-references findings against triage frameworks to deliver instant warnings about immediate patient risks, such as life-threatening anaphylactic shock.

Pattern 3: Cross-document multimodal synthesis & data reconciliation

The challenge

Enterprise information is fragmented across disconnected files and formats (e.g., PDF minutes, Excel charts, PNG flyers, and email threads). Traditional tools analyze these files in isolation, failing to connect the dots when verifying details or resolving data contradictions across independent documents.

The multimodal advantage

Cross-file synthesis: Connects information across entirely different formats (PDFs, spreadsheets, images, emails) simultaneously to answer complex business queries.
Conflict resolution: Flags and resolves contradictions between assets, such as catching outdated pricing on an image by cross-checking it against the latest financial spreadsheets.
Visual-to-text auditing: Audits visual or scanned files against text-based records (e.g., verifying a signed PDF contract against a legal review email) to catch missing clauses or changes.

The future of agentic enterprise content management

The integration of gemini-embeddings-2 into Box’s Agentic Platform is an important new capability to improve the next era of content intelligence. Multimodal embeddings help Box to move beyond basic search to active, intelligent collaboration.Box's Intelligent Content Management platform represents a fundamental shift in enterprise AI infrastructure — moving beyond passive document storage to deliver a governed, semantically indexed reasoning layer where AI agents can interrogate, cross-reference, and act on content with full compliance and security controls already in place.

Powered by multimodal embeddings and a suite of native AI agents spanning search, metadata extraction, research, analysis, and composition, Box enables organizations to proactively surface insights such as flagging stale pricing data, expiring contract clauses, or cross-document contradictions before they become business risks. For high-complexity industries like financial services, life sciences, and legal operations, Box's ability to reason across text, tables, charts, and images makes multimodal understanding a competitive requirement.

Designed to interoperate with the broader enterprise AI ecosystem, Box serves as the single governed content foundation that ensures every AI-driven workflow is grounded in authorized, auditable enterprise data.

When you think about it, the enterprise data landscape was always multimodal. Now we have the technology to make the most of it. By integrating gemini-embeddings-2, Box helps its users unlock unprecedented value from unstructured enterprise content. Product leaders who embrace multimodal-first architectures, rigorous precision benchmarking, and audit-ready grounding will lead the next wave of enterprise productivity and innovation.

The team would like to thank Ken Ikeda, Afshaan Mazagonwalla, and Samip Thakkar for their work on this project.

Building operational resilience with agentic AI in financial services

Tue, 18 Aug 2026 14:00:00 +0000

For financial institutions, operational resilience has long been embedded in regulatory and supervisory expectations — to say nothing of the high expectations of consumers. With the implementation of the European Union’s Digital Operational Resiliency Act (DORA), those expectations have become even more stringent, with more explicit, harmonized, and evidence-driven requirements. Firms must now demonstrate that their critical business services and supporting digital infrastructures can withstand disruption, support coordinated response, and recover with control.

To meet these conditions, Deutsche Bank developed an AI-powered agentic resilience platform that modernized its regulatory tabletop resilience exercises at scale and turned manual preparation into context-aware and evidence-ready simulations grounded in actual operational data. The platform builds enterprise context from architecture, data flows, logs, incident history, alerting signals, and operational telemetry to generate scenarios, simulated operational evidence, structured session records, and regulator-ready artifacts.

At many large banks with operations that span interdependent applications, data flows, and third-party services, this is a critical and even existential shift. Across financial services, supervisory expectations are evolving and as they do, banks’ tabletop exercises must reflect their production dependencies, real operating conditions, and compliance with consistent evidence standards more directly.

As Deutsche Bank considered how to successfully and efficiently make this shift at scale, it looked to its long-time partner, Google Cloud, and its growing suite of agentic AI tools.

From tabletop exercises to resilience intelligence

With its agentic resilience platform, DB has been able to transform its tabletop exercises from manual preparation to a continuous intelligence model. And it’s been able to extend the same agentic layer to root-cause analysis when real operational context is needed.

This means that every scenario it runs is based on real enterprise signals. The platform can then reflect true system dependencies, failure patterns, and business impact instead of relying on static inputs that are more likely to return assumptions than real-time insights.

By using Gemini Enterprise Agent Platform, DB has been able to migrate this operational context into structured scenarios with clear timelines, decision points, and expected responses. This has ensured that each exercise is grounded in real system behavior that produces consistent, audit-ready evidence that meets regulatory expectations.

Dual orchestration for control and flexibility

In order to deliver both regulator-grade control and operational flexibility, DB’s platform introduced a dual-orchestration architecture that separates workflows into two complementary execution models.

First, for regulator-aligned execution, the bank is using LangGraph to ensure that it generates every scenario through a traceable, deterministic process — with clear lineage from input context to output — that supports the auditability required for supervisory review.

Next, for its adaptive and investigative scenarios, DB is using Google Agent Development Kit (ADK) to enable agent-driven coordination. This approach allows the bank’s platform to dynamically analyze conditions and generate responses without predefined execution paths.

Figure 1. Architecture for context assembly, orchestration, and scenario generation.

With this architectural separation, the platform can combine governed execution with adaptive investigation while preserving a common intelligence layer. The same agents and tools can reason over architecture, data-flow diagrams, logs, and code artifacts across tabletop scenario generation and related incident-analysis workflows. Importantly, this supports a consistent resilience model across both planned exercises and real operational events.

Deutsche Bank’s objective with this platform was to engineer a resilience model for critical financial systems that meets regulatory expectations — even within highly complex, distributed environments. By linking dynamically generated scenarios to real business context and combining governed orchestration with adaptive analysis, the platform has given us an intelligent, continuously adaptive model for operational resilience.” – Sanjay Tripathi, Managing Director, Global Head of Surveillance Technology & Compliance Cloud & AI Transformation Lead, Deutsche Bank

Powering generation and governance with Google Cloud

Google Cloud’s suite of agentic tools is providing the foundation for scaling Deutsche Bank’s platform across its many governed, enterprise-grade resilience workflows. Here’s how:

Cloud Run supports elastic execution of scenario and evidence-generation services.
Gemini Enterprise Agent Platform transforms operational context into structured resilience scenarios.
Google ADK enables adaptive agent coordination.
Cloud SQL provides durable persistence for scenarios, session artifacts, and review records.

Collectively, these services give DB support for the traceable generation, controlled execution, and persistent evidence record required for compliance review and continuous improvement.

Scalable, evidence-ready resilience testing

Every scenario generated by Deutsche Bank’s platform drives a structured tabletop session for the teams that run response, escalation, and recovery. Because these exercises are grounded in real enterprise context, they reflect operational reality while also strengthening consistency across teams and creating audit-ready evidence that meets regulatory expectations. For institutions that operate under DORA or similar frameworks, this makes it easier to demonstrate controlled, coordinated, and disciplined response at scale.

This model is now being applied across multiple DB portfolios, which is helping the bank establish more consistent and scalable resilience paradigms and a replicable blueprint for the broader financial sector.

In this model, root-cause analysis acts as the feedback loop between real incidents and future resilience testing. The resulting insights from production events can inform future tabletop scenarios, while exercise outcomes can strengthen response playbooks, escalation paths, and recovery readiness.

All of this extends the platform’s value from planned resilience exercises to real operational events while keeping scenario-based resilience testing as the primary use case.

As adoption expands, this platform brings consistency by embedding Google Cloud’s methodology for context-aware resilience. It eliminates fragmented manual approaches and establishes a cross-functional, AI-informed operating model across the bank.

Toward resilience intelligence

The bank’s next step is to extend this approach into a broader resilience intelligence layer, which is possible because it can deploy the same patterns to support playbook refinement, recovery-readiness assessments, and continuous validation of controls against evolving system conditions.

For financial institutions, this is a strategic shift. As systems become more distributed and regulatory expectations more demanding, banks must move from periodic resilience testing to continuous, intelligence-driven capabilities. At Deutsche Bank, Google Cloud is making that transition simple across the organization.

Learn more about Google Cloud’s methodology for context-aware resilience in this article.

How WPP operationalizes platform and data engineering for AI marketing

Mon, 10 Aug 2026 16:00:00 +0000

Between chaotic levels of market fragmentation and economic volatility, marketing and communications agencies can no longer rely on the human intuition they’ve traditionally used to win clients and optimize their ad spend. WPP is replacing that guesswork with an AI-powered view of shifting market dynamics, giving brands predictive certainty that lets them invest with confidence while moving at the speed of the market. That’s the value of WPP Open, its agentic marketing system.

But before it could begin applying sophisticated AI models to power those insights, WPP had to overcome a critical engineering challenge: the marketing data that made up the models was fragmented across hundreds of global agencies. While this dynamic made it nearly impossible to deploy AI tools efficiently and securely, access to models was only part of the equation. And until it built a reliable way to ingest, clean, and serve data to those models, WPP couldn’t unlock the true potential of generative AI.

To solve this, WPP partnered with Google Cloud to construct a unified data backbone and custom platform engineering path. Now, by standardizing its serverless compute patterns and data processing workflows, WPP is able to securely deploy targeted marketing campaigns in days instead of months.

Architecting a centralized, service-based data foundation

An important part of this effort was accelerating data availability and centralizing management. To do this, WPP adopted a service-based project structure for its current production environment. Rather than isolating every workload into separate silos, its engineering team centralized Google Cloud Storage (GCS) and BigQuery into dedicated, shared data projects, while also segregating the compute and processing workloads into distinct processing projects.

This structure simplified the core team’s user experience and ensured that all data consumers interacted with a unified source of truth. Because data from WPP’s various product lines lives in shared infrastructure, it was essential that security be strictly enforced at a granular level. By directly applying identity and access management (IAM) controls at the individual GCS bucket and BigQuery dataset levels, the company’s teams only see the data they’re authorized to access.

At the same time, raw data from various partners lands in dedicated GCS buckets in order to keep the raw inputs organized and isolated. From there, Managed Service for Apache Spark executes custom Apache Spark jobs to cleanse, normalize, and canonicalize information into standardized cohort definitions (SCDs). By utilizing a serverless architecture combined with Kubeflow for pipeline orchestration, WPP’s data engineering team avoided the overhead that often results from managing cluster infrastructure. This allowed them to focus entirely on the data transformation logic fueling the downstream GCS and BigQuery layers that ultimately feed the company’s audience & performance AI models.

What made our collaboration with Google Cloud successful was the balance they struck between uncompromising professionalism when it comes to best practices and timely delivery of incredibly pragmatic, real-world solutions.- Jonas DahlbaekSenior Data Engineering Lead, WPP

Standardizing data into unified cohorts

When raw data enters WPP’s processing zone, its platform converts it into SCDs that become core concepts used throughout the framework for keying purposes. These are based on five keys: age, gender, geo, product, and interest. But these underlying data definitions are fluid and continuously canonicalized to reflect evolving marketing concepts. As a result, this uniform structure allows WPP to join and aggregate data on a global scale without exposing sensitive underlying particulars or relying on shared identifiers.

The platform's core processing engine was built in type-safe Scala to ensure comprehensive visibility and compliance This custom framework tightly controls how data is transformed, and it inherently supports full source traceability while guaranteeing that every data point within the curated datasets can be traced back to its origin. This is a crucial level of traceability when building enterprise AI applications, as data scientists and auditors must understand exactly what information feeds into the models, even as WPP concurrently prepares to transition to Google Cloud Knowledge Catalog for automated, enterprise-wide data governance in the future.

Working with Google Cloud has been instrumental in accelerating and standardizing our engineering efforts. In a world where massive volumes of fragmented data present a daily challenge, having the right infrastructure is paramount to thriving in the AI age and helps our developers and AI marketers alike.
-Suleman KhanProduct Manager for OI & Google Partnerships, WPP

Standardizing the enterprise software lifecycle

For WPP, even with all these steps in place, processing data is only half the battle. To serve applications and manage the underlying infrastructure, the company’s platform engineering team developed a suite of reusable and centralized GitLab continuous integration and continuous deployment (CI/CD) templates. With this, WPP reduced the cognitive load on individual development teams and ensured that all deployments met strict corporate security standards.

These templates manage various enterprise workloads autonomously. The suite includes universal Cloud Run templates for full-stack web applications and batch data processing and scheduled pipelines. It also includes a deploy-only template for multi-stage workflows and a Cloud Run functions deployment template for event-driven microservices.

Implementing zero-rebuild promotion

Rebuilding container images in a production environment can introduce unnecessary risk and the potential for configuration drift. In order to maintain environmental consistency, WPP embraced a "build once, deploy many" methodology that applied cross-project IAM logic and Google Cloud Artifact Registry configurations.

As part of this process, developers build and test container images in the development environment. Once those exact, immutable container images are validated, they’re promote directly to production. This zero-rebuild promotion ensures total parity across deployment stages and eliminates unexpected production behaviors. The CI/CD templates also facilitate progressive traffic migration, which allowed teams to route a small percentage of traffic to new revisions before initiating a full rollout.

Immutable deployments. Traceable data. Unshakable trust. When you know exactly what goes into your AI, you can ship at the speed of light.
- Ranjith K PoldasAssociate Director , Devops (I&P), WPP Media

Automating security and intelligent networking

With this modern architecture, enterprise security acts as a foundational enabler for WPP, so it integrated Wiz security scanning directly into the pre-push phase of the CI/CD pipeline to catch vulnerabilities before code merges. The company also utilized Google Cloud Identity-Aware Proxy to enforce zero-trust access across its internal applications.

To further simplify operations, WPP adopted templates with intelligent virtual private cloud (VPC) logic. This configuration automatically identifies and resolves networking conflicts between legacy VPC connectors and modern Direct VPC access. This automated networking prevents deployment failures and accelerates the release cycle.

Monitoring operational health and driving ROI

Because a resilient platform foundation requires deep observability, WPP’s engineering team now monitors strict operational metrics instead of relying solely on deployment frequency. The team tracks request latency across p50, p95, and p99 percentiles, alongside 4xx and 5xx error rates. It also monitors container startup times to mitigate cold starts, while tracking overall CPU and memory utilization. This granularity ensures that both data pipelines and serverless infrastructure always remain highly available.

"Navigating a transformation of this scale across multiple complex workstreams—spanning data engineering, platform infrastructure, and AI integration—required more than just alignment; it demanded deep, mutual trust. Working as true partners, Google Cloud and WPP moved in lockstep to deliver production-ready platform capabilities on time."Yang Yue , Program Manager , Google Cloud

For WPP, operationalizing its data and AI stacks at this velocity provided the necessary infrastructure for its advanced workloads, and the business impact was clear and quantifiable. By building this dual foundation, the company reduced creative and strategy time from four weeks to just three hours. It also saw a 70% gain in production efficiency, a 33x increase in content volume, and a 2.8x increase in campaign return on investment. In short, by partnering with Google Cloud and implementing a broad suite of products and tools, WPP was able to quickly realize a significant ROI and boost productivity, efficiency, reliability, and security across the company.

How Malachyte solves retail’s cold-start problem with managed real-time AI

Mon, 10 Aug 2026 16:00:00 +0000

What’s the best way to recommend products to little-known users?

We’ve spent our careers trying to solve this problem for major companies like Spotify and Priceline, and it’s why Sidd founded Malachyte, an AI-powered ecommerce recommendation platform. These days, consumers have come to expect content that feels personalized and relevant, and online services competing for their attention have no choice but to do this exceptionally well.

Malachyte was inspired by some unique insights into how advanced AI models, and large language models in particular, could be applied in new ways to old challenges like personalization and recommendations.

As Malachyte set out to win potential customers’ business, we needed secure, scalable, reliable and, above all, leading-edge AI infrastructure to continue building the personalization algorithm we had always envisioned. By utilizing Google Cloud tools like Bigtable and Managed Service for Apache Kafka, Malachyte has been able to help some of its retailers double and sometimes even triple their sales.

This is the story of how we built it, and the ways any founder can use services like these to start deploying AI foundation models in new ways.

How Malachyte lifted sales for their users

For Malachyte, the aha moment was discovering that it could use neural networks with attention mechanisms — the same concept powering large language models — to personalize retail search and product pages. This approach is what enables LLMs to derive meaning from the relative order of items in a sequence, in their case the order of words and syllables in a sentence. When it comes to a retail website or app, what Malachyte wanted to capture was the sequence of customer interactions with the site.

What if we predicted the next thing a user wants on an ecommerce website just like LLMs predict the next word in a sentence?

A pre-GPT language model might have tried to look at a specific sequence of words or even fragments of words (what we now know of as tokens), but those earlier models wouldn’t examine what happens if the words were in the comparable order but weren’t contiguous or were re-arranged. The breakthrough came — in part through Google’s work on transformers — when LLMs gained the ability to understand complex and long-range dependencies within a sequence of items.

This more sophisticated method has delivered dramatic results — both for the proliferation of gen AI in general, and for Malachyte’s application of the technology.

To make this work in practice, Malachyte creates a vector of everything known about a visitor when they arrive on a site. Most users are visiting for the first time, so little is known about them. This is what’s known as the “cold start” problem. The trick is to use every interaction with a user to refine this vector. Each new addition to the vector, like a click or a query, does two things: it drives a prediction about the next thing the user wants, and it provides more information about the user.

Malachyte’s platform then updates the user vector and the prediction at the same time. This not only enhances the understanding of the individual user and their preferences, it also improves the overall model with the anonymized user data. With every inference, the context of both the average and the specific shopper grows.

The company further innovates by not just using attention-based neural networks but combining that with updating user profiles 100 milliseconds at time.

Malachyte’s recommendation and search agents populate the next page’s search results or recommendation carousels based on what users clicked on previous pages.

To be sure, this idea isn’t in itself new. Retailers have long used collaborative filtering recommendations systems to identify similar users and items that required massive sets of interaction history. These models typically required a lot of data, including third-party cookie-based profiles and demographics.

By focusing on the sequence of interactions in a session, retailers can achieve far more personalization — with less required data or spend — than by focusing only on a user’s profile. As a bonus, retailers can now offer their users more privacy by not relying on long-term cookie data.

This works because of the model structure and multimodal vectors that encode everything they know about a user, including browser data, click history and searches. The output, too, is multimodal: The same model can be applied to on-site search product pages, category pages, and add-to-cart carousels.

To make this work, each product in the catalog is embedded into the same space as the user vector, which gets updated and subsequently moves the vector closer to relevant products and further from those that aren’t. The neural network computing the embedding is being continuously trained across retailers who work with Malachyte, improving the quality for everyone. The system effectively becomes a data cooperative with each retailer's user helping make the model smarter for everyone.

A user session represented as a vector in a space of products.

To make this delivery for every user at every inference in 100 milliseconds, Malachite found real benefits in building onGoogle Cloud’s real-time AI stack.

With this system, every behavioral event streams into a Managed Service for Apache Kafka cluster. Rather than queuing for a future training job, each event immediately becomes an update to the user’s profile in Bigtable. The Kafka cluster allows the customer’s front-end to persist, so the user session signals quickly with little worry about how they fit into the user vector.

Bigtable allows Malachyte’s services to look up and update the right user vectors, and it and Kafka operate at the order of 10 milliseconds per step, which allows the entire recommendation loop to complete with no disruption to the user experience.

The three layer real-time AI architecture: a retailer’s website, Malachyte’s AI models and serving front ends, and context management infrastructure.

In addition to a fast core, a second layer of product catalog updates, inventory signals, and retailer-specific dimensional data keeps product data up to date. This flows through Cloud Pub/Sub, which offers globally accessible REST APIs that enable connections retailers can use without deep integration work. Malachyte agents run on Google Kubernetes Engine (GKE), with model inference on Google Compute Engine (GCE).

Continuous ingestion of external data, such as product catalog updates, operates through Pub/Sub’s global messaging system.

With its migration to Google Cloud’s AI architecture, Malachyte demonstrated that production AI inference and training are about more than GPUs and storage. They require real-time continuous learning infrastructure that includes a fast key-value store, a streaming layer, and a managed messaging system, all integrated with the foundation model architecture.

This approach also shows that even a small team like Malachyte’s can have a big impact in an industry. It just needs access to powerful infrastructure and core AI managed services.

Try it for yourself

Looking to shake up your industry or stay ahead of the competition like Malachyte? Try Managed Service for Apache Kafka, Cloud Pub/Sub, and Bigtable. New customers can receive $300 in Google Cloud credits.

GOL! How TelevisaUnivision streamed the FIFA World Cup to millions with Google Cloud

Fri, 07 Aug 2026 16:00:00 +0000

Live sports broadcasting represents the ultimate stress test for digital media infrastructure, where operational success or failure is measured in milliseconds and observed live by millions of viewers simultaneously. During the 2026 FIFA World Cup, the stakes reached a high for TelevisaUnivision, the leading Spanish-language media conglomerate. With Mexico serving as both a primary host nation and a core contender on home soil, fan engagement created unprecedented demand across Latin America and TelevisaUnivision's ViX streaming platform.

For a marquee broadcaster like TelevisaUnivision, high-stakes events carry direct, long-term brand and reputational implications. Audiences demand uninterrupted, pristine access to every critical moment of play. Playback interruptions during a key goal, login latency at kickoff, or degraded stream resolutions immediately impact customer satisfaction, risking subscriber churn and brand dilution. When streaming tier-1 global sports events, technical execution directly impacts consumer trust, requiring an absolute commitment to zero-downtime availability and flawless performance on the part of the provider.

Why TelevisaUnivision selected Google Cloud

Navigating Latin America's complex networking ecosystem, which is marked by heavy ISP fragmentation and cross-border transit bottlenecks, required more than a standard vendor relationship. TelevisaUnivision needed a strategic partner willing to make joint investments in network capacity, infrastructure resiliency, and custom feature development. TelevisaUnivision selected Google Cloud's Media CDN based on two foundational differentiators: its architecture, and Google Cloud’s customer focus.

Platform architecture

Media CDN provided a resilient, globally distributed infrastructure built specifically to absorb massive live-stream traffic spikes while protecting origin infrastructure. Core architectural advantages included:

In-ISP deep edge caching: Media CDN embedded cache nodes deep within local ISP networks across Mexico and Central and South America, placing video segments within a single network hop of viewers.
Direct ISP peering: By establishing direct peering connections with major regional telecommunications operators such as América Móvil and Telefônica, the architecture completely bypassed congested international transit routes.
Dedicated capacity reservations: TelevisaUnivision reserved live event capacity with dedicated allocated headroom in-region. This isolated livestream traffic from "noisy neighbor" risks and comfortably absorbed peak traffic surges.
Sub-millisecond sessions with Valkey 9.0: To handle massive traffic spikes during the World Cup, TelevisaUnivision migrated its session store to Memorystore for Valkey 9.0, operating as serverless microservices on edge compute. This architecture delivered sub-millisecond response times for critical authentication and entitlement checks, while providing automatic scaling to process peak API traffic without the need for manual capacity reservations.

Obsession for customer success

Beyond technical capabilities, TelevisaUnivision chose Google Cloud for its joint co-engineering model and deep operational alignment.

Joint 24/7 war rooms: For all 104 matches, TelevisaUnivision engineers and Google Cloud specialists operated side-by-side in unified command centers.
Proactive monitoring as a service: Google Cloud’s Customer Reliability Engineering teams provided round-the-clock proactive monitoring and automated alerting.
Joint operational authority: Combined teams performed extensive pre-tournament stress tests and simulated failovers. During live matches, unified telemetry empowered joint leads to dynamically route traffic and adjust CDN configurations instantly as regional ISP congestion emerged.

Summary

The strategic partnership between TelevisaUnivision and Google Cloud during the 2026 FIFA World Cup established a new benchmark for global sports broadcasting. Across 39 consecutive days of tournament execution, TelevisaUnivision reported that the joint infrastructure delivered:

Total matches broadcast	104 live matches
Platform availability	100% platform availability (0 downtime)
Cumulative viewership	675 million views across TelevisaUnivision & ViX

By uniting localized edge delivery, sub-millisecond serverless compute, and dedicated operational co-engineering, TelevisaUnivision and Google Cloud solidified a battle-tested blueprint for executing marquee live streaming events at record global scale.

Mirendil taps AI Hypercomputer TPUs and GPUs for pre- and post-training applications

Thu, 06 Aug 2026 13:00:00 +0000

Nearly every major AI lab uses Google Cloud infrastructure, including for training of models, inference for agents, and new frontier research. Google Cloud also continues to be the platform of choice for new, high-growth AI startups who are driving much of the industry’s research and innovation.

Today, we’re announcing that Mirendil, an exciting frontier AI lab focused on accelerating AI development, will also utilize Google Cloud’s AI Hypercomputer. This includes using a mix of Google’s TPU AI accelerators and full-stack NVIDIA AI infrastructure running on Google Cloud; this purpose-built AI infrastructure will support model pre-training and post-training applications for Mirendil.

The Mirendil team is building new AI systems that can help accelerate and democratize AI research and development. This means managing complex, end-to-end training workflows from initial model pre-training through post-training, and powering reinforcement learning on a massive scale. The ability to choose a mix of both TPU and NVIDIA’s full-stack accelerated computing platform through Google Cloud meant that Mirendil could access critical compute very quickly, and continue to match its workloads to the architecture best-suited to it over time.

We closely partnered with Mirendil on end-to-end design and deployment of combined TPU and NVIDIA AI infrastructure across compute, storage, networking, and control planes. We also collaborated on a system that uses managed training clusters running in Gemini Enterprise Agent Platform, which effectively streamlines the provisioning and management of both TPU and GPU environments for Mirendil. Mirendil is already live with a cluster of TPU v5P chips, with NVIDIA AI accelerated computing systems coming online soon.

"Progress in AI has been bounded by how fast humans can run the research loop - designing experiments, evaluating results, and iterating," said Behnam Neyshabur, cofounder and CEO of Mirendil. "We're building AI systems that can accelerate and improve that loop itself. Expanding on Google Cloud gives us the scale and flexibility to push those systems further and put frontier AI research capabilities in the hands of many more scientists and engineers to run that loop faster and at a greater scale."

You can read more about our partnership on Mirendil’s blog.

Scaling agentic AI: How UiPath built its high-performance GPU platform on AI Hypercomputer

Wed, 05 Aug 2026 16:00:00 +0000

As a market leader in enterprise agentic automation and business orchestration, UiPath is helping to pioneer an industry shift toward agentic AI. With it, the company is deploying autonomous agents to actively reason, make decisions, and execute complex business processes across its disparate systems.

This transition from simple task automation to cognitive decision-making agents requires a massive surge in computational power and powerful infrastructure that’s reliable enough for the needs of the world's largest enterprises.

Being able to orchestrate hundreds of GPUs in perfect harmony can be what makes the difference between just running a research experiment and building a global AI platform. Such orchestration requires balancing massive training jobs with real-time inference, all without letting costs spiral or latency spike.

To do so, UiPath re-architected its infrastructure to support high-scale intelligent document processing (IDP) using UiPath IXP and moved from isolated clusters to a shared Google Cloud GPU fleet, balancing A3 VM instances (NVIDIA H100 GPUs) for training with G4 VM instances (NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs) for inference. This architecture lets UiPath solve its “spiky workload” problem and count on predictable costs and open-source patterns that the company’s engineering teams can use to replicate this architecture themselves.

"Realizing the full potential of enterprise agentic AI requires an infrastructure that matches our ambition. Google Cloud provides the scale and flexibility we need to train specialized models and deploy them globally. This partnership allows us to deliver high-precision intelligent document processing and autonomous agents that don't just chat, but actively drive business outcomes for our customers." – Raghu Malpani, Chief Technology Officer, UiPath

The context: heavy-duty math

UiPath has run its full-stack automation platform on Google Cloud for years, but as its agentic AI initiatives expanded, it faced a series of new infrastructure challenges.

Core capabilities like IDP, computer vision, and LLM-powered reasoning require heavy-duty math, so UiPath’s engineering team utilizes LLAMA model grounding that allows its robots to "see" interfaces with human-like clarity. And with specialized document models built on the Qwen architecture, the team can extract valuable data from messy, real-world paperwork.

These models live on the UiPath cloud infrastructure, where cutting every possible millisecond of latency is essential. Moving from a "cool demo" to a reliable production tool without exploding costs meant the team had to rethink its underlying silicon.

The challenge: more demand than supply

In the past, when a team at UiPath needed to train a new model or run inference, it provisioned GPU nodes on demand and scaled up or down depending on whether the workloads were spiking or slowing.

This was a functional strategy when cloud capacity was cheap, abundant, and perfectly elastic. But as its AI ambitions grew, UiPath found this approach could no longer keep up with its operational complexity. It now faced three new challenges:

Spiky workloads: To ensure it had sufficient power for peak demand, UiPath often had to buy extra capacity that sat idle during quieter periods, wasting expensive headroom. The company needed intelligent, on-demand scaling that didn't require paying for silicon that wasn't crunching numbers.
Supply bottlenecks: For large-scale fine-tuning, the price-to-performance ratio on gold standard high-end A3 VM instances with 8-cluster H100s is unbeatable. But global demand for those chips has outstripped supply, making it nearly impossible to scale training efforts at the speed UiPath desired just by adding nodes.
Operational overhead: UiPath was also struggling with geographical inefficiency because stable inference demand still meant maintaining dedicated clusters in multiple regions to ensure low latency for international customers. Further, managing GPU infrastructure for both training and inference added inefficient layers of operational overhead.

The solution: a shared GPU fleet

With all of that in mind, UiPath decided to treat its GPUs as a shared strategic resource instead of a product-centric elastic infrastructure.

As a result, its engineering team designed a platform-level shared GPU fleet managed by its machine learning services (MLS) platform, which prioritizes work across teams and time windows while balancing demand across workflows. During the day, the fleet serves real-time inference and latency-sensitive workloads, and at night or during off-peak hours, it automatically switches to batch training and long-running jobs.

By coordinating workloads at the fleet level, MLS lets UiPath maximize utilization while reducing contention, all without relying on per-instance elasticity. It also enables the company to schedule capacity in advance, which improves predictability for both research and production use cases.

Why Google Cloud: AI Hypercomputer architecture

To support its growing scale, UiPath leveraged Google Cloud AI Hypercomputer, which offers a system-level approach integrating performance-optimized hardware, open software, and flexible consumption models into a unified environment. AI Hypercomputer also minimizes the friction between hardware and software layers, which allows engineering teams to focus on model performance rather than infrastructure management.

Once it settled on a shared fleet model, UiPath needed a cloud partner that could offer reliable GPU availability, competitive pricing, and burst capacity. That’s why it chose to expand its existing Google Cloud footprint with a highly specialized AI stack running on Google Kubernetes Engine .

Now, UiPath can take advantage of predictable capacity by leveraging Google Cloud’s Dynamic Workload Scheduler (DWS) to solve its supply bottleneck. The company knew Google Cloud could secure its GPU capacity consistently with notice windows measured in days. DWS allows the engineering team to schedule training runs in advance and secure capacity for short bursts, and it can now plan for capacity rather than having to react to scarcity. Today, UiPath runs all its training and most of its IDP model inference workloads on Google Cloud.

While UiPath uses A3 VM instances for heavy-duty training and fine-tuning, not all of its tasks require that level of power. That’s why it now deploys Google Cloud G4 VM instances as a net-new optimization for inference workloads. These instances offer a cost-effective balance of performance and price, which allows UiPath to run lighter inference tasks without occupying the high-performance clusters reserved for training.

"The shift to a shared fleet on Google Cloud transformed our operational model. We moved from reactive provisioning to a predictable, high-performance engine that powers our most advanced IDP and agentic AI workloads. With tools like Dynamic Workload Scheduler and a mix of A3 and G4 instances, we have the flexibility to optimize for both cost and speed. This ensures our engineers spend their time innovating rather than waiting for compute." - Arthur Wilcke, director of AI infrastructure, UiPath

Practical validation: differentiated models at scale

With consistent access to Google Cloud GPUs, UiPath can now bring advanced models into production. It can also schedule large training jobs without blocking production inference, letting it balance research experimentation with production reliability.

This allows UiPath to deliver advanced IDP capabilities that extract data from highly unstructured and variable documents with high accuracy. For example:

Omega Healthcare uses UiPath to automate over 100 million transactions with 99.5% accuracy, a 40% reduction in processing time, and 15,000 less hours of repetitive tasks per month.
Thermo Fisher Scientific uses UiPath to extract data from PDFs like invoices and purchase orders and is now able to process 53% of its invoices without human involvement, while cutting processing time by 70%.

Lessons learned

UiPath’s most significant wins so far have been increased availability and reliability. As its workloads continue to transition and it decommissions its legacy GPU resources, the company expects to see additional cost improvements.

For engineering teams looking to build similar platforms, some key takeaways include:

Decouple capacity: Instead of tying hardware to specific products, pool resources to smooth out usage spikes.
Schedule, don't react: Using tools like DWS to book compute in advance guarantees availability and stabilizes costs.
Right-size the silicon: Use A3 VM instances for training, but choose efficient options like G4 VM instances for inference.

Next steps

After its recent infrastructure evolution, UiPath is still refining its MLS platform to support the next evolution of AI innovation. To replicate this success in your own organization, use the resources below:

Build it: explore engineering patterns on GitHub.
Optimize it: Get started with Google Cloud G4 VM instances.
Learn more about UiPath - IXP

How Target is enhancing retail discovery and cutting database maintenance by 50% with Spanner Graph

Tue, 04 Aug 2026 16:00:00 +0000

In today’s retail environment, shoppers expect highly personalized product discovery experiences and conversational assistance that feels genuine, natural, and genuinely helpful. Today, successful product discovery is about understanding semantic meaning and the rich, connected relationships between products, categories, and guest intent. It is no longer just about keywords and basic browsing.

At Target, this work is handled by our Guest Product Confidence platform team. They are responsible for building the features that establish trust and guide purchasing decisions, such as ratings, reviews, and AI-driven digital shopping assistants. An exciting example of this is our Gift Finder chat agent, which we launched during the 2025 holiday season online and in the Target app to help shoppers discover the perfect items through friendly, conversational dialogue.

To deliver real-time personalization and context-rich semantic responses like these at global scale, we identified a critical architectural need to move away from a fragmented data ecosystem toward a unified data platform. We needed a solution capable of supporting high-throughput transactional workloads, highly connected graph relationships, vector similarity search, and full-text keyword search all at once.

In this post, we’ll explore how we achieved all four with Spanner.

Overcoming fragmented architecture

Previously, Target’s discovery data ecosystem relied on a combination of Elasticsearch clusters for search and inverted indexes, alongside separate NoSQL datastores for our transactional data. While functional, this fragmented architecture presented significant operational and technical challenges.

Disconnected context: Keeping separate search, vector, and transactional databases in perfect sync was a constant challenge. Siloed information led to missing context, disconnected attribute relationships, and inconsistent query results.
High operational overhead: Managing independent clusters, tuning search indexes, and handling complex, custom synchronization and aggregation logic required intensive manual intervention from our engineering teams.
Expansion bottlenecks: Expanding our retail data domains required adding new database collections, maintaining complex joins, and navigating weak transactional guarantees across our discovery and core transactional systems.
Siloed intelligence: We lacked the ability to query graph relationships, vector similarity, and keyword search indexes in a single transaction.

To build the next generation of AI-driven guest experiences, we needed to consolidate on one platform.

Building the enterprise ontology on Spanner Graph

We evaluated multiple specialized technologies, including standalone vector databases and niche graph databases. However, adding more single-purpose databases would have only worsened our operational complexity and data synchronization pipelines.

We ultimately chose Spanner Graph to build our enterprise ontology, which is a "graph-of-graphs" paradigm that allows us to construct a massive, generative AI-powered shopping graph.

By unifying our data, we bring semantic data, graph relationships, vector embeddings, and operational transactions under one roof. This establishes Spanner as our single authoritative source of truth for both transactional state and semantic intelligence.

Our high-level architecture now consists of three core pillars:

1. Enterprise augmentation
This layer captures our enterprise retail catalog, aggregates relevant metadata from multiple backend sources, and utilizes generative AI for agentic data enrichment to dramatically improve the quality and depth of the product data we ingest.

2. Unified graph, vector, and search store
Instead of shifting data across multiple databases, Spanner Graph stores our entity nodes, relationship edges, and vector embeddings in the same database engine. Spanner Graph natively supports multi-hop graph traversals, semantic vector similarity, and full-text keyword queries over our relational tables. Because this multi-model synergy is native, we get strict ACID transactions for absolute correctness across distributed workloads without the need for fragile external sync pipelines.

3. Orchestration and AI layer
This layer powers our conversational guest interfaces, utilizing rich, structured context fed directly from Spanner Graph to ground our LLMs. It extracts highly specific product relationships to power tools like the Gift Finder while governing responsible AI processes and evaluating generated outputs.

A smooth, zero-downtime incremental migration

Transitioning critical search and discovery infrastructure that millions of guests rely on required a cautious, zero-downtime approach. We executed this migration in four structured phases.

Schema and ontology mapping: We defined the specific retail entities, such as products, categories, brands, and guest preferences, and their corresponding relationships within the Spanner Graph schema.
Data integration and parallel replay: We built mutation-based data integrations in a parallel pipeline. This allowed us to continuously replay live transactional updates, apply schema transformations, generate embeddings, and write them directly into Spanner Graph in real-time.
Canary deployment: We gradually shifted live read traffic to the new Spanner Graph-backed platform, validating query performance, semantic accuracy, and database stability under real retail workloads.
Cutover and cleanup: Once performance was thoroughly verified, we fully transitioned all search and discovery traffic to Spanner and deprecated our legacy Elasticsearch stack, entirely removing the maintenance burden of those clusters.

Business impact

By building directly on Spanner Graph, we unlocked measurable technical and business outcomes:

The ultimate GraphRAG foundation: Traditional RAG relies on flat vector similarity, which often misses the structured associations between products, such as matching a toy with its compatible accessories or age-appropriateness. By combining deep graph traversals with semantic vector search in a unified GraphRAG architecture, we grounded our LLMs with highly precise context. This directly improved our recommendation relevancy, enhanced guest satisfaction, and boosted our Net Promoter Score.

Consolidated SQL + GQL interoperability: With Spanner Graph, our developers query structured relational catalog data and connected graph relationships in a single query using standard SQL and GQL (Graph Query Language). This eliminates the need for data duplication, latency, or complex ETL pipelines to bridge these paradigms.

Serverless scalability with zero growth ceiling: Spanner automatically handled massive, unpredictable traffic spikes during peak retail events like Black Friday and Cyber Monday. Spanner's built-in autoscaler dynamically adjusted computing capacity to handle burst traffic during high-intensity, limited-time promotional offers without sacrificing performance.

50% reduction in infrastructure maintenance: By consolidating our transactional NoSQL and search index databases into a single managed Google Cloud service, we eliminated the operational burden of maintaining separate database clusters. Our developers now spend 50% less time on database administration and infrastructure upkeep, allowing us to build and deploy new, customer-facing AI features much faster.

Migrating to Spanner Graph has accelerated our generative AI roadmap, serving as the ultimate proof of what is possible when you build on the right data foundation.

Want to supercharge your AI apps? It starts with databases with the right graph capabilities at virtually unlimited scale. Discover how Spanner Graph can turn data into action for your organization.

How Deutsche Bank unlocked agility with an API-ready ecosystem

Tue, 04 Aug 2026 16:00:00 +0000

When people think about digital transformation in banking, they often focus on the visible results: mobile apps and new digital services. But there's an invisible infrastructure making all these services possible: APIs. At Deutsche Bank, we recognized that APIs aren't just technical plumbing; they're the nervous system of modern banking.

A few years ago, our application landscape was dominated by monolithic systems. As we evaluated how to break them into modular, reusable APIs, one thing became clear: we couldn't just decompose our work into APIs — we needed a central API management platform (APIM) to manage what would emerge. We needed something where documentation, security policies, and governance all had to be built in from the start, not bolted on later.

The question wasn't just how to modernize, but how to best serve our customers and position ourselves for tomorrow's opportunities, especially with emerging technological paradigm shifts.

Needing a system that was adaptable, scalable, reliable, secure, and AI-ready for the demands of modern banking, we chose Google Cloud's Apigee as our APIM platform.

Building the backbone: four key capabilities

Today, Apigee manages our API ecosystem — from open banking APIs connecting us with fintech partners, to the internal microservices powering our various banking platforms, and even the client-facing applications that enable seamless digital experiences such as online banking.

Here are four important capabilities the platform offers us:

1. Unified governance without sacrificing speed

Apigee is the foundation of our API catalog. Every endpoint, version, and dependency is documented and discoverable. Development teams find and reuse existing APIs rather than rebuild functionality. We've moved from "Where's that customer data API?" — which took days — to a searchable, real-time catalog accessible to any developer.

But governance isn't about bottlenecks, it's about guardrails, and with Apigee's policy framework, we automatically enforce standards. OpenAPI specifications, schema validation, and error handling are now baked into the platform. Teams move faster because they work within consistent frameworks.

2. Security: the employee onboarding analogy

When thinking about API security, imagine onboarding a new employee. You don't give them access to every system on day one. You follow the least privilege principle, so they get exactly the permissions needed for their role. If they switch departments, their access rights will be updated. If they leave the company, access is revoked immediately. Apigee works the same way for our services and applications.

When connecting a new service — say, one that accesses customer accounts — we don't open the floodgates. Through OAuth2 scopes and API key management, we define precisely what that agent can access:

Read account balances? Yes.
Initiate wire transfers? No.
Access 90-day transaction history? Yes.
Full historical data? Only with elevated permissions.

Like employee access, these permissions are centrally managed, regularly audited, and instantly revocable. Just as we track employee activity for compliance, Apigee logs every API call to see who accessed what data, when, and why.

This becomes critical with high-volume automated systems. An automated service doesn't take breaks and can make thousands of calls per minute if misconfigured. Rate limiting and quota enforcement ensure that even when something goes wrong, the blast radius is contained.

3. Resilience and performance at scale

Banking doesn't have downtime. When customers check balances at 3 a.m. or markets surge with trading activity, our APIs must respond instantly and reliably.

Apigee's load balancing and auto-scaling evenly distribute that traffic. Health checks and circuit breakers automatically route around struggling services, and for frequently accessed data, Apigee's caching delivers sub-millisecond responses without hitting backends.

4. Observability: measuring everything

Before Apigee, understanding API performance was like assembling a jigsaw puzzle with pieces from different boxes. Now we have unified dashboards showing real-time traffic, error rates by service, usage analytics by consumer, and compliance metrics. This visibility serves operations, product managers who track partner value, and security teams who identify anomalies.

Apigee provides a central suite of capabilities for managing the full API lifecycle

The path forward

We built this infrastructure for the API economy, and in doing so, we have also built a strong foundation for the future of digital banking. As the industry evolves, this API-first approach will be critical for integrating next-generation services.

As digital banking continues to advance, a shift toward intelligent services that can react, predict, and assist in real time is underway. Capabilities such as real-time pattern recognition, predictive insights, and AI-powered assistants are becoming part of everyday digital experiences, with their visibility and impact increasing as adoption accelerates. Each of these capabilities will consume APIs — and they will introduce new requirements: ultra-low latency, high-throughput data flows, and secure orchestration across multiple APIs.

Because we invested in a flexible API platform with Apigee, we are well-positioned to adapt and optimize our infrastructure for these future needs, rather than having to rebuild it.

Emerging standards: MCP, A2A, and the future

The industry is exploring new integration standards. Protocols like Model Context Protocol (MCP) and Google's Agent2Agent (A2A) are interesting because they build on existing API infrastructure.

Our Apigee-managed APIs are well-positioned to leverage these advancements. For instance, MCP could benefit from our OpenAPI specifications, and A2A could leverage our OAuth2 framework, with both relying on the governance we've built.

We're also exploring patterns like placing new types of servers behind Apigee proxies to maintain security controls while enabling modern workflows. Our "always-API" pattern ensures that services benefit from centralized management, no matter how they are accessed.

MCP and A2A are complementary, MCP has a tools and resources focus, while A2A is focused on peer collaboration

The vision: APIs as universal interface

Every banking capability will eventually be exposed as an API. That’s not because APIs are trendy, but because they're the most flexible, composable, and governable way to share functionality, whether consumed by mobile apps, partner fintechs, analytics platforms, or other automated agents.

At Deutsche Bank, this shift is already taking shape. The same API foundation that powers our core platforms is now enabling our evolution toward more intelligent, AI-supported services across the bank. That foundation provides the consistency, governance, and scalability needed to bring these capabilities to life, ensuring that as new intelligent services emerge, they can be integrated seamlessly, securely, and at enterprise scale.

Apigee makes this possible by providing governance that scales across all use cases. It's not about controlling innovation; it's about enabling it safely.

Lessons learned

Invest in excellent documentation. Semantic summaries and clear schemas aren't extras; they're foundational for both developers and AI.
Treat security like employee onboarding. Least privilege and role-based access apply equally to APIs.
Observability is a competitive advantage. Unified analytics enable data-driven decisions.
Plan for the future now. Your API management infrastructure becomes your advanced integration layer.
Stay curious. Experiment with emerging standards. Flexibility wins.

Conclusion

We're at an inflection point. The API economy enabled fintech and open banking. Now, the same infrastructure can serve as the backbone for the next wave of innovation. Our investment in the API platform wasn't just about managing APIs better; it was about building a foundation for whatever comes next.

As the industry transforms, we’re ready. The future belongs to organizations that move fast without breaking things. For us, that future is powered by Apigee.

Best Buy scales AI workloads and secures access with Workforce Identity Federation

Tue, 28 Jul 2026 16:00:00 +0000

As Best Buy expanded its use of Google Cloud for advanced analytics and AI, its technology teams faced two significant scaling challenges: Mitigating risk and managing administrative friction when syncing thousands of backend users from Microsoft Entra ID.

The retailer solved both problems and paved the way for a massive cloud expansion by implementing Google Cloud's Workforce Identity Federation. This direct approach allowed developers to access cloud resources securely using their existing Microsoft credentials without a separate identity store, giving technical leadership confidence that access remains strictly controlled, auditable, and manageable at scale.

Replacing service accounts with direct federation

Best Buy historically maintained complex synchronization pipelines to copy backend users from Entra ID to Google Cloud. Because the organization used Cloud Identity without a Google Workspace deployment, it needed a more direct approach. Previously, Best Buy's Power BI integration with BigQuery relied on service account credentials.

This pattern can work at a small scale, but quietly becomes a liability as your team grows.

Manually rotating keys for service accounts meant tracking the credentials each team held, and accepting that every key was a potential security vulnerability. Service account keys created daily friction for the Best Buy security and platform teams, and the technical debt compounded as data access requirements grew more complex.

To support tens of thousands of users, Best Buy modernized its identity architecture. The team adopted Workforce Identity Federation to federate existing Entra ID identities directly into Google Cloud.

Now, when developers access BigQuery through Power BI, they authenticate as themselves using their existing Entra ID identity. They no longer need to rotate keys, worry about credentials exposed in chat messages, or guess who performed an action in the audit log.

The architecture relies on two components working together: Entra ID handles authentication, Workforce Identity Federation brokers the trust relationship between Entra ID and Google Cloud. This federation is stateless on Google's side. It validates tokens at the moment of access instead of syncing user records. Removing the service account key layer greatly reduces the credential management burden.

Architecture

The diagram below shows how identity flows from Entra ID through the Workforce Identity Federation to the services teams use at Best Buy. The key change from the previous approach is the removal of the service account key layer entirely; there is no credential to manage between Entra ID and Google Cloud.

Identity flows from Entra ID through the Workforce Identity Federation to the services teams use at Best Buy

Key implementation decisions

When implementing this architecture, Best Buy made several important technical choices:

Separate provisioning and SSO apps in Entra ID: The configuration follows the Entra ID provisioning and single sign-on (SSO) setup guide. You should separate the provisioning application from the SSO application in Entra ID. Running them as two distinct enterprise apps provides a cleaner separation of concerns; provisioning changes do not affect SSO configuration, and vice versa.
Place the automation OU carefully: You need to place the Entra ID provisioning service account in a separate organizational unit (OU) and explicitly disable SSO for that OU. This prevents a bootstrapping problem: If you enforce SSO globally, the provisioning account cannot authenticate to set up the provisioning in the first place.
Understand that syncless means stateless on Google's side: Workforce Identity Federation does not create or maintain user records in Cloud Identity. It validates tokens at the moment of access. This makes the architecture viable for Best Buy's target scale, because it eliminates synchronization lag, stale record cleanup, and separate provisioning pipelines.

Secure authentication for developers

For developers, the change was practically invisible. They authenticate once through their corporate Entra ID credentials, and access to BigQuery works automatically, whether through Power BI or direct API calls. The SSO experience matches everything else they access through their Microsoft identity.

For the security and platform teams, the benefits are significant. The attack surface from credential management disappears. Audit logs now show individual users instead of shared service account identities, and you can revoke access quickly based on the enterprise identity lifecycle rather than waiting for manual key rotation.

If you currently manage service account keys for developer access to Google Cloud, moving to Workforce Identity Federation is worth the effort. You gain significant security benefits, and the operational simplicity grows as your team expands. Best Buy is currently scaling this secure access to a broader workforce to power its future retail operations.

Expanding Workforce Identity Federation support

Google Cloud continues to make it easier for all organizations to bring their own identity providers. Recent updates simplify the setup for Ping Identity users and extend access to online billing accounts.

Ping Identity integration: If you use Ping Identity, you can follow a new, dedicated setup guide to configure federation. This guide provides step-by-step instructions so you can securely connect your workforce to Google Cloud resources.
Online billing support: Google Cloud now supports customers with online billing accounts. You can use Workforce Identity Federation for secure, syncless access without needing an enterprise billing agreement.

Get started

Google Cloud is committed to removing friction from cloud adoption and making it simpler for organizations to secure their environments. To explore these new capabilities and connect your organization's identity provider, read more about how Workforce Identity Federation allows you to federate identities directly, and explore our supported Google Cloud services.

The Blueprint: How Voicify makes AI-enabled ordering a delight for customers

Thu, 23 Jul 2026 16:00:00 +0000

Welcome to The Blueprint, a new feature where we highlight how Google Cloud customers are tackling unique and common challenges across industries using the latest AI and cloud technologies. We hope to inspire others looking to innovate in their work.

Founded in 2018, Voicify reimagines the traditional phone call with the goal of transforming every call into a seamless and engaging experience.

The challenge:

When we started Voicify in 2018, our vision was to help organizations build confident, pragmatic, and technically grounded voice-driven assistants for any channel, including phones and chat. But the pandemic changed everything.

We shifted our focus to telephone use cases primarily in the restaurant and healthcare sectors where, at the time, call volume and staffing posed significant challenges. Restaurants could potentially miss up to 20% of their calls and lose orders as a result, and healthcare providers struggled to keep up with call volume with the required 100% accuracy when integrating appointment information into a practice management system. We realized that specialized, purpose-driven AI assistants were the key to businesses maintaining excellent service at scale.

To succeed, we had to overcome four primary challenges:

Transactional precision: Our voice assistant needed to reason with complex customer requests against point of sale and practice management systems with 100% accuracy.
Traffic spike management: Our LLM usage needs to be provisioned accurately to keep costs down and maintain customer services in spite of the common (and extreme) spikes in traffic seen in restaurants and healthcare organizations.
Latency: Any delay in the assistant’s response can cause customers to hang up. We needed superfast time to first token, with minimal delay from when a user sends a voice or text request to when the AI model generates its first piece of output.
Security and compliance: Since our founding in 2018, we’ve ensured that we’re HIPAA, SOC2, ISO27001, and PCI-compliant, and that our security is enterprise-grade. We needed architecture and infrastructure that employs all possible safeguards to safeguard data integrity and security.

The solution:

Our conversational orchestration platform builds and validates restaurant orders against a point-of-sale system before submission to ensure accuracy. Under the hood, Gemini Flash, served via Gemini Enterprise Agent Platform, vastly improves latency, minimizing user wait times and preventing hang-ups. With it, we also see approximately 25% to 30% savings compared to our previous use of other LLMs, and with greater reliability too.

To grow the business — and call volume — and to handle traffic spikes, we switched from Google AI Studio to Vertex AI and its current incarnation in Gemini Enterprise. We wanted the enterprise guarantees the latter provided, which we needed for scaling as well as for security and compliance for our healthcare clients. Specific Gemini Enterprise Agent Platform features help us manage high call volumes without experiencing service interruption or dropped responses.

These enterprise-grade services may have carried an increased cost over AI Studio, but they were well worth it to ensure reliable uptime, and the premium pay-as-you-go feature made scaling much easier for us. For example, we used a combination of provisioned throughput and premium pay-as-you-go with Vertex AI to accommodate all-time high usage the day before Thanksgiving, and we saw no rate limiting issues.

The architecture:

The outcome:

Gemini has reduced the burden on our in-house programmatic tools for pulling context and building menus. We’ve seen great improvements in performance and reliability, with lower latency and greater reliability with Gemini. And, the increased stability of our Gemini-powered assistants has made client onboarding much more efficient. Now it only takes one to two days to get a restaurant ready to test after gaining access to the POS system, down from what previously took one to two weeks.

With Google solutions for scale and enterprise-grade service, we’ve optimized our critical time-to-first-token metric, minimizing customer wait times. Using Vertex AI’s provisioned throughput and pay-as-you-go features, we’ve ensured 100% uptime, prevented dropped responses and rate-limiting issues, even during periods of all-time high usage. We’re now able to easily manage the spiky nature of restaurant traffic.

In terms of technology, we anticipate moving beyond conversational order capture to more proactive assistance, using context from conversations or POS activities. Your typical Friday night order from your favorite Japanese restaurant? Someday soon it might be Voicify’s voice assistant proactively placing it for you.

The details:

Our industry focus presents a few unique challenges that we had to spend time solving within the backend.

The core component of our Voicify solutions is our voice orchestration platform, which manages the entire phone AI stack and is designed for enterprise-grade scalability and security. This is also the node where industry solutions are called depending on user needs.Our voice orchestration platform sits close to the customer and coordinates backend services like Gemini and the different components of the voice assistant. We use it to manage functions like automated speech recognition, text-to-speech, and text generation, which is not purely generative but includes programmatic elements.

One of the unique architectural decisions we made was how to manage large, complex restaurant menus. We decided to avoid putting the entire menu into a single prompt, and we include only certain information in the initial prompt and then gather more details as the conversation progresses. This improves response times and helps manage the complexity of larger orders by focusing on only the relevant parts of each menu in a given interaction.

We also designed the architecture from the outset of our company to meet the high standards of enterprise clients for security and compliance, particularly in healthcare. We are making sure that our scalability is enterprise-grade. Architecturally we’re also employing all safeguards to ensure data integrity and safety too. Lastly, our platform is designed to support a multicloud environment as part of our strategy for achieving the highest possible level of availability.

From maintenance to innovation: Checkout's migration to Managed Service for Apache Airflow

Wed, 22 Jul 2026 14:00:00 +0000

Data engineering teams often face a “Day 2” operational reality after building a data platform: the ongoing work of maintaining the orchestrator itself.

For the Data Platform team at Checkout.com, managing a self-hosted Apache Airflow environment on another hyperscaler was consuming time the team wanted to spend elsewhere as server management, patching, and incident response were pulling focus from building pipelines.

By migrating to Managed Service for Apache Airflow (Gen 3), Google Cloud’s fully managed Airflow service, Checkout.com transformed its reliability and cost structure. Here’s how they built a more scalable, cost-efficient, and robust data foundation.

The starting point: self-managed Airflow

Before the migration, Checkout.com ran Airflow on self-managed infrastructure. While functional, maintaining the underlying resources required significant attention. Patching, upgrades, and server management created regular interruptions.

Operational data from the past year illustrates some of the challenges the company was navigating:

Reducing operational friction: In its self-managed environment, Checkout.com faced stability challenges, particularly during high-load periods.
Complex dependency management: Upgrading packages and ensuring compatibility was a constant, manual struggle. With Managed Airflow (Gen 3), the company was able to simplify this by handling dependencies at the image level, ensuring seamless compatibility out-of-the-box during routine environment upgrades.
DAG sync time: Syncing DAGs to the scheduler took approximately six minutes after deployment to S3, which affected iteration speed.
Manual processes: Scaling required manual intervention, and onboarding new teams meant manually creating secrets and variables for dbt.

The solution: Managed Service for Apache Airflow (Gen 3)

Checkout.com’s team migrated to Managed Airflow to offload infrastructure responsibility and take advantage of Google Cloud's managed scalability. The results were immediate and measurable across three areas: reliability, cost, and developer velocity.

Dynamic scaling in action

In the previous elastic container service setup, the team allocated the maximum number of workers required for peak loads. This meant paying for peak capacity around the clock, regardless of actual usage.

Managed Airflow provides built-in dynamic scaling, eliminating the need for manual resource management. The environment automatically adjusts the number of workers based specifically on the workload demands. When tasks spike, the system scales up; when they drop, it scales down to save resources. Similarly, moving from fixed provisioning to dynamic scaling reduced monthly costs by an estimated 30%.

Reliability and DAG isolation

Achieving increased stability was a primary driver for Checkout.com’s migration since in the past, a single problematic DAG could affect its entire environment.

Managed Airflow introduced a number of critical architecture improvements:

DAG isolation: Each DAG runs in its own execution environment. If one DAG fails or consumes excessive resources, it doesn’t affect the entire environment.
Managed operations: Google Cloud handles patching and upgrades during scheduled windows, removing the need for manual upgrade management.
Improved visibility: Integration with Google Cloud Monitoring and Cloud Logging provides clear visibility into task execution. Engineers can now debug issues independently without escalating to the platform team.

Faster developer workflows

The migration also improved day-to-day workflows for Checkout.com’s data engineers.

Faster deployments: Using Cloud Storage for DAGs enabled near-instant syncing.
Simpler onboarding: Teams no longer needed platform support to create variables before onboarding.
Modernizing dbt execution: One of the company’s most significant wins was changing how it runs dbt. Previously, its engineers had to manually install and manage complex virtual environments for every supported dbt version. By leveraging containerized dbt runs, Managed Airflow (Gen 3) eliminates dependency bottlenecks. This ensures complete dependency isolation, allowing teams to run any required dbt model with minimal setup and no manual infrastructure overhead.
Environment updates: The company no longer needs to redeploy the entire Airflow environment to add new roles or update Python packages.
AI-powered troubleshooting with Gemini Cloud Assist: In a self-managed environment, a failed task often triggered a frantic hunt through fragmented logs and metrics. With Managed Airflow, Checkout.com can initiate a Gemini investigation directly from its Airflow DAG UI in the Google Cloud console.

Gemini doesn't just provide generic error messages; it generates a scorecard that evaluates different hypotheses with both supporting and contradictory evidence, which can drastically reduce mean time to recovery.

Conclusion

For Checkout.com, the move to Managed Airflow (Gen 3) marked a strategic shift, one that freed its engineers to focus on delivering value.

"With Managed Service for Apache Airflow, we’ve achieved significant improvements in efficiency, scalability, and reliability. Managed infrastructure, automated scaling, faster deployments, and isolated execution environments have transformed how we operate." — Keisi Mancellari, Data Platform Engineer, Checkout.com

With a stable, scalable, and cost-efficient platform in place, Checkout.com is now able to focus on the future of its data pipelines, confident that its orchestration layer is ready for whatever comes next.

Learn more about Managed Service for Apache Airflow and how it can support your data platform.

_{Special thanks to the following contributors to this post: Serge Bouschet and Keisi Mancellari}

SOCRadar powers rapid threat detection with AlloyDB and Gemini Enterprise

Wed, 01 Jul 2026 19:00:00 +0000

Editor’s note: SOCRadar is a leading cybersecurity company that provides threat intelligence to businesses worldwide. As the volume of cyber threats continued to grow, SOCRadar needed to modernize its data infrastructure to deliver faster insights to its customers. By migrating from PostgreSQL to AlloyDB, SOCRadar achieved a 20x performance boost, reduced operational overhead, and is now better positioned to innovate and grow.

How SOCRadar supercharges rapid threat detection with AlloyDB

SOCRadar provides external threat intelligence to help organizations across 30+ countries defend against cyberattacks. On the front lines of cybersecurity, timely intelligence is everything and a delay of a few minutes can mean the difference between a blocked exploit and a full-scale breach.

As SOCRadar’s business scaled and cyber threat volumes exploded, their on-premises, self-managed PostgreSQL database hit a wall. The database simply couldn't keep pace with the simultaneous demands of high-velocity data ingestion and heavy, real-time analytical queries. This created a severe data bottleneck, slowing down the delivery of critical insights to customers and pulling engineers away from innovation to focus on constant manual database tuning.

Evaluating database alternatives: The hunt for scalability

The engineering team realized their traditional PostgreSQL environment had reached its absolute performance limits. To scale, SOCRadar needed a high-performance fully managed database that could dramatically slash operational overhead while elegantly handling a complex, hybrid workload.

They evaluated alternatives and selected Google Cloud's AlloyDB for PostgreSQL. Because AlloyDB is fully PostgreSQL-compatible, it offered a low-risk migration path while promising a specialized architecture built to handle both high-volume transactions and real-time analytics simultaneously. To accelerate the transition, SOCRadar partnered with NGC, a Premier Business Partner, who meticulously validated the architecture before executing a precision cutover with minimal downtime.

Taming a "triple-threat" workload

Migrating to AlloyDB transformed how SOCRadar processes massive, diverse cyber telemetry. Today, AlloyDB effortlessly manages what SOCRadar’s engineering team calls a "triple-threat" query environment, maintaining sub-second lookup latency even as processing volumes scale.

To understand the performance leaps, it helps to separate the system’s velocity (handling live data streams) from its depth (analyzing historical data):

High-Velocity Transactional Ingestion (OLTP): The platform constantly ingests real-time telemetry from thousands of disparate, fast-moving sources—including Dark Web forums, botnet logs, and social media feeds. AlloyDB handles these continuous INSERT and UPSERT operations with a 3.2x boost in live ingestion velocity, ensuring that the newest threat indicators are immediately recorded and available for detection.
Real-Time Operational Point-Reads: When a security analyst is actively investigating a live incident, speed is everything. Baseline performance testing under zero-load conditions for random ID lookups on indexed fields (e.g., querying a specific Indicator of Compromise by ID) showed that standard queries requiring 3 to 3.5 seconds were completed in just 1 second on AlloyDB.
Deep Analytical Aggregations (OLAP): When a client requests a complex sectoral report such as correlating the most prevalent attack vectors in the finance sector over an entire year, the database must execute deep scans across vast historical datasets. Leveraging AlloyDB’s built-in In-Memory Columnar Engine, these analytical queries run up to 20x faster than standard PostgreSQL.

More than just speed: Reclaiming 45 TB and 75% of DBA time

While the raw performance gains were massive, the operational and financial impact completely changed how SOCRadar's engineering team works day-to-day.

Thanks to AlloyDB's advanced automation, including intelligent memory management and write-ahead log (WAL) optimization, the need for constant, manual database tuning evaporated. The database administrator's (DBA) workload dropped significantly, requiring a system health check just “about once every two or three days." This freed up 75% of SOCRadar’s DBA resources, allowing them to pivot away from maintenance and focus entirely on core platform innovation.

Financially, AlloyDB’s dynamic storage management solved a massive cost efficiency issue. Unlike traditional database environments that lock you into paying for fixed, provisioned storage even after data is purged, AlloyDB automatically scales storage down to match actual data footprints. By clearing out legacy, unnecessary logs, SOCRadar was able to instantly reclaim over 45 TB of storage, achieving massive, automated cost optimization.

Fighting alert fatigue with integrated Gemini Enterprise Agent Platform

Beyond scaling infrastructure, AlloyDB has allowed SOCRadar to redefine the core architecture of their threat response using artificial intelligence.

Security operations centers (SOCs) globally are plagued by "alert fatigue"—the sheer volume of security alarms makes it easy to miss a critical attack. To solve this, SOCRadar integrated Gemini Enterprise Agent Platform as a core component of their solution architecture, linking it directly to their Alarm Management framework running on AlloyDB.

By running Gemini AI-native filtering directly on their active data workloads, SOCRadar can automatically distinguish between true positives and benign false alarms. The AI categorizes, filters, and routes alerts before they ever reach the end-user. This ensures security analysts are insulated from noise and receive only the most critical, validated, and actionable intelligence.

Expanding capabilities: The future of agentic threat hunting

With a high-performance foundation firmly established, SOCRadar’s dedicated AI team is transitioning from passive analytics to active automation. The company is currently testing Agentic AI workloads, with plans to roll them into production in subsequent phases.

By integrating Real-time Data Agents with Gemini Enterprise and AlloyDB, SOCRadar is transforming with autonomous agents that don't just store data, but actively hunt threats, reason over context, and take action. Their upcoming production roadmap includes:

Natural Language Querying (NLQ): Allowing analysts to conduct rapid threat hunting using conversational language, lowering the technical barrier to querying massive database sets.
Intelligent Semantic Similarity Search: Leveraging native vector embeddings and Gemini Enterprise to allow Data Agents to independently surface hidden patterns across historical logs that traditional keyword searches would miss.
Automated Incident Summarization: Instantly transforming hundreds of lines of complex, deeply technical logs into concise, plain-language executive summaries for security analysts during critical incidents.

By consolidating transactional velocity, historical depth, and built-in AI intelligence into a unified platform, SOCRadar has eliminated its data bottlenecks and built a highly automated, future-proof framework for global cybersecurity defense.

Ready to modernize your database infrastructure? AlloyDB provides a fully managed, PostgreSQL-compatible database with high performance for transactional, analytical, and AI workloads. Learn how you can reduce costs, eliminate management overhead, and build intelligent applications.

How Schrödinger sped up molecular discovery by 4x with Alphaevolve

Tue, 30 Jun 2026 16:00:00 +0000

Computational chemistry researchers have traditionally faced a frustrating trade-off when simulating molecular interactions: use fast classical force fields that sacrifice precision or rely on accurate quantum-mechanical methods that run too slowly on large jobs.

Machine-learned force fields (MLFFs) close that gap by training neural networks on high-fidelity quantum data. When it comes to modern drug discovery and materials design, though, there’s demand for even faster processing speeds to handle massive chemical libraries involved. To overcome such performance constraints, Schrödinger partnered with Google Cloud to deploy AlphaEvolve, an evolutionary AI coding agent developed by Google DeepMind that iteratively generates and refines algorithms to find the most efficient code path overcoming the algorithmic bottleneck.

A collaborative duet with AlphaEvolve

Schrödinger — a leader in developing scientific software for over three decades — identified two critical algorithms within their MLFF training pipeline that limited performance: neighbor list computation and Ewald summation. These algorithms aggregate data from atomic neighbors and calculate long-range potentials, but both became limiting factors in training and inference speed.

Schrödinger's primary technical goal was speeding up AI model training for energy and force calculations. Specifically, they targeted the Ewald summation, a critical but computationally demanding function used in molecular mechanics. The Ewald sum was the main performance constraint in Schrödinger's PyTorch code. It had no established vectorized algorithm and often relied on simple for-loops that ran slowly on large simulations.

By incorporating AlphaEvolve into their models, the system could generate a batched implementation of the Ewald summation using parallel batch matrix multiplication. This would evolve the PyTorch code to outperform existing custom kernels.

Evaluation metrics

Schrödinger used a rigorous multi-layered evaluation framework to confirm the evolved code was both performant and scientifically accurate:

Inverse time (primary metric): The core objective was to maximize throughput by reducing calculation time, from a baseline score of 7.9.
Functional correctness: All evolved programs had to pass a full test suite, including regression tests on complex systems such as disordered water models.
Success rate: This was measured by the share of programs that were both functionally correct and faster than the baseline.

“AlphaEvolve allows us to explore larger chemical spaces faster and more efficiently than ever before. Faster MLFF inference carries real business impact, shortening R&D cycles in drug discovery, catalyst design, and materials development, and enabling companies to screen molecular candidates in days rather than months.” — Gabriel Marques, technical lead of machine learning, Schrödinger

Results: a 4x speedup and breaking bottlenecks

By applying AlphaEvolve, Schrödinger replaced simple for-loops in the Ewald summation code with parallel batch matrix multiplication. This optimization raised the program success rate from less than 1% (40 out of 5,000 evaluations) to more than 60%, while improving the performance metric from the baseline of 7.9 to nearly 30.

Optimizing these foundational algorithms delivered a 4x speedup in both MLFF training and inference. This acceleration lets researchers compress molecular screening timelines and directly benefits several key research areas:

Drug discovery: Identifying viable therapeutic candidates quickly to address urgent medical needs.
Catalyst design: Developing efficient chemical processes for industrial applications.
Materials development: Designing next-generation materials with custom properties for electronics and energy storage.

The next evolution

Schrödinger plans to apply this evolutionary approach to custom GPU kernels to test whether AI-generated code can outperform human-engineered implementations.

Read the full technical paper on AlphaEvolve to learn how evolutionary AI agents optimize scientific codebases, or contact the Google Cloud AI team to discuss accelerating your research workflows.

How growing UK midsize businesses are building in the AI era

Wed, 17 Jun 2026 08:00:00 +0000

The UK’s 5-million-plus small and midsize businesses and enterprises (SMBs) are the backbone of our economy. Today, we’re seeing these critical businesses begin to put AI to work, to operate more efficiently, move faster, and ultimately deliver better outcomes for their customers.

This shift is driven by tangible day-to-day results. According to recent research from Enterprise Nation published in partnership with Google, 71% of AI adopters surveyed in the UK say the technology helps them save time on routine tasks, and 64% report a direct boost in productivity. On top of this, AI-enabled productivity tools (like Google Workspace with Gemini) are delivering a 20% boost in productivity for SMBs, which effectively hands them back one full working day every single week.

At Google Cloud, we have a front row seat to this shift: SMBs have long utilized platforms like Google Workspace, and today they’re transforming with Google’s AI platform and models. In fact, we’ve seen the number of UK-based SMBs using Google Cloud AI nearly double year-over-year. This includes our Gemini models and products like Gemini Enterprise and AI Studio, which are helping SMBs do things like:

Roll out better customer support systems to help escalate and resolve customer support calls more quickly.
Automate repetitive actions in areas like payroll and accounting.
Help more employees understand and leverage data at work — even those not trained as data analysts.
Rapidly create and implement new designs for marketing collateral.
Help more people build their own AI agents to help them in their everyday jobs.
Conduct complex research projects at a speed and price point previously unavailable.

At today’s Google Cloud London Summit, we’re showcasing a number of innovative SMB customers who are actively using our AI tools to transform how they work, including companies who have recently expanded their work with us:

Neural Alpha, a sustainability fintech company, is using Gemini models to read unstructured environmental and corporate sustainability reports to automatically find and organize thousands of key facts, cutting months of slow, manual research down to a fraction of the time.
Sep 2, a digital security provider, uses Gemini Enterprise to deploy autonomous AI agents for 24/7 threat monitoring — accelerating incident detection and quickly neutralizing security threats for its customers.
Sunhouse, a strategic brand design agency, uses Gemini Enterprise to easily find archived design work stored on Google Drive, enabling its teams to spend less time hunting for files and more time growing its business with global brands.
Terrapinn, a global B2B events company, is transforming its operations by leveraging Gemini models, NotebookLM, Looker, and BigQuery to turn manual tasks into automated workflows, accelerating how its teams design, market, and deliver world-class conferences.
VoCoVo, a telecommunications provider, is integrating Google Cloud AI across its systems to turn isolated data into actionable intelligence and build autonomous workflows, streamlining routine operations so their team can focus on high-impact innovation.

Empowering Your Team: AI Upskilling Resources for Growing British Businesses

To help midsize teams maximize their impact and confidently navigate the modern AI landscape, we’ve developed a suite of dedicated, no-cost upskilling resources. Whether you want to train your existing teams or democratize data tools across your entire workforce, these programs will help you build an AI-ready organization:

SMB-Focused Programs: Explore our new SMB Learning Path or enroll in the Gemini Enterprise Agent Ready (GEAR) program for specialized training in agentic AI.
Google Skills for Organizations: Access our no-cost, on-demand learning platform featuring over 3,000 AI courses and hands-on labs created by experts at Google Cloud and Google DeepMind.
Get Certified: Ready to validate your team's expertise? This premium, cohort-based program offers instructor-led training, technical mentorship, and AI-infused skill badges designed to prepare your team for industry-recognized certifications.

By offering a full suite of SMB technology and training — from productivity in Workspace, to all our Ads services, and now powerful AI tools — Google is helping small and midsize firms thrive, no matter where the future takes us.

From AI potential to agentic reality: Driving the UK’s next chapter

Wed, 17 Jun 2026 08:00:00 +0000

The United Kingdom, and London in particular, continues to be one of the great hubs for AI development in Europe and the world. We’re home to Google DeepMind, of course, as well as significant AI unicorns — and Google Cloud customers — like Ineffable Intelligence, which is today announcing an important partnership with us.

A year ago, we joined you for the London Summit to showcase the vast potential of generative AI, including a major investment in upskilling the UK civil service. Today, as we welcome our partners once again to the historic vaults of Tobacco Dock, that potential has become an industrial-scale reality. In my conversations with leaders across both Whitehall and The City, the focus has moved from chatbots and media experiments to full-production execution. This is the moment of the agentic enterprise, where we shift from systems that simply chat with us to systems that can reason, plan, and execute multi-step workflows.

This transition is the cornerstone of the UK’s projected £400 billion economic boost from AI by 2030. At Google Cloud, we are the only provider offering the full integrated stack — custom silicon, frontier models, and planet-scale infrastructure — required to turn the Agentic Enterprise into a reality.

The new frontier of British enterprise and research

The banking sector is a key proving ground for this shift. And HSBC, one of the largest and most important financial institutions in the world, is showing the way. Today, we’re announcing a multi-year transformational partnership with HSBC to accelerate AI adoption across HSBC’s products and services globally. This new collaboration will further accelerate the shift towards AI-enabled ways of working across HSBC’s global operations.

HSBC will work with Google Cloud and Google DeepMind engineering teams to collaborate on new AI-powered tools and programmes, with access to Google’s latest agentic AI capabilities – including Gemini models and the Gemini Enterprise Agent Platform. The initial delivery focus on three areas: hyper‑personalised wealth management support, stronger financial crime risk management, and AI tools to enhance frontline/relationship manager client service

UK startups also continue to break new ground with technology, and AI in particular, as demonstrated by the work of frontier labs like Ineffable Intelligence. The company, which launched earlier this year, has chosen Google Cloud as its preferred cloud partner, utilizing Google’s full stack of AI-optimized hardware and tools to build and train Ineffable’s first generation of foundational models.

Led by David Silver, a former Google DeepMind researcher who was instrumental in the AlphaGo project, Ineffable Intelligence is taking a unique approach to AI development. The team are building systems that learn primarily through their own experience through reinforcement learning, instead of relying on the large-scale human-generated datasets behind language models. The ambition is to create a “superlearner” that develops knowledge through trial and error. This year, Ineffable Intelligence set a record for a European seed funding round of $1.1 billion, and now Ineffable Intelligence will support its training work by deploying one of the largest clusters of A5X, powered by the NVIDIA Vera Rubin NVL72 platform on Google Cloud, delivering massive computational scale.

To move from experimentation to true industrial production, businesses need more than just models; they need a roadmap. To help show them the way, we’re expanding our partnership with Deloitte, which will open a new AI Studio at its London campus. Developed in collaboration with Google Cloud, the studio will help British organisations move beyond AI experimentation to deploy autonomous, action-oriented AI systems at scale.

Deloitte is also committing to upskill 1,000 members of its UK AI and data workforce on Gemini Enterprise. This certification program will ensure that Deloitte’s AI and data engineers’ are equipped with the technical expertise to implement Google’s most advanced agentic architecture, providing UK clients with one of the largest pools of certified AI talent in the region.

Building a future-ready public sector

The blueprint for a modern digital government requires moving away from rigid legacy contracts toward agile, AI-driven public services. In collaboration with the Ministry of Housing, Communities and Local Government (MHCLG), the i.AI incubator, Google Deepmind, and Faculty, we are delivering tangible public sector reform and tools for reinvention that directly support the national goal to "get Britain building."

Agencies like MHCLG are already using a tool called Extract which was built using Google technology to help transform planning processes by reducing document processing times from two hours to just two minutes. Simultaneously, we are supporting trials of an AI planning tool — co-created with local planning authorities in Barnet, Dorset, and Camden — which aims to cut decision times for everyday applications by 50%. Furthermore, the Department for Transport (DfT) is utilizing Gemini to streamline public consultation analysis, a move projected to save £4 million annually.

Innovation on this scale also requires a secure, sovereign foundation. That is why Google Cloud is working to strengthen our UK data residency commitments, including measures like making Gemini 3.5 Flash, which features in-country AI processing, available by late June 2026 for sensitive sovereign use cases. We are giving British organizations the confidence to innovate within strict compliance boundaries.

To help keep businesses safe from the challenges posed by bad actors using AI and other digital threats, we also recently announced a comprehensive AI-powered cybersecurity platform — Google AI Threat Defense — which combines Wiz, Mandiant, Gemini & CodeMender to find, fix, and protect our customers from vulnerabilities.

Proven impact from the high street to public service

Autonomous agents are no longer a future prospect; they are delivering value across the UK economy today. Our work with THG Ingenuity, an ecommerce solutions provider, has delivered an 8x higher conversion rate via its AI Shopping Assistant. Starling is similarly empowering customers with "spending intelligence" tools for instant habit analysis around purchases and expenses. And Rightmove, has launched a beta version of an AI-powered conversational property search, built with Google’s Gemini models, enabling users to search for homes in their own words.

The breadth of this impact is visible across every sector: Kingfisher is pioneering retail-specific agentic applications; Openreach is driving field service optimization in telecommunications; andUnilever is using AI at scale across the entire value chain to drive growth and build desirable brands in the new era of consumer goods.

Meanwhile, VMO2 is streamlining complex data operations; Vodafone is executing a $1 billion partnership to redefine network performance; and WPP is integrating Gemini across creative workflows, whether that's generating high-fidelity campaign assets at speed and scale, powering AI agents, or training robotic camera operators.

Empowering the engine of growth for small to medium businesses and startups

The true measure of Britain’s AI success lies in its small and medium enterprises and startup ecosystem. Our AI Works research highlights a pivotal moment: AI has the potential to boost productivity for small and medium enterprises by 20% and unlock £198 billion in output for the UK economy. With 56% of smaller firms already seeking guidance, we have launched the AI Works for Britain upskilling initiative to ensure no business is left behind.

We also continue to foster the next generation of British unicorn startups through our ongoing partnership with Tech Nation at the London AI Hub. This sustained commitment ensures founders have the resources and community needed to scale, and this September, we will further this mission by hosting the Gemini Startup Forum: Cybersecurity in London to help startups build secure-by-design AI applications.

The Model Garden at Platform 37

Our belief in the UK’s potential is reflected in our physical footprint, too. We are continuing to invest in the UK's digital infrastructure to support growing demand: Our state-of-the-art data center in Waltham Cross launched in September 2025, a key part of our two-year, £5 billion investment to help power the UK's AI economy. And earlier this year, we opened our new office in London in Kings Cross, Platform 37, along with plans for The AI Exchange, a new public space dedicated to deepening understanding of AI.

Building on this momentum, we are excited to introduce The Model Garden at Platform 37, launching in the fourth quarter of 2026. This London-based hub is far more than a physical space; it serves as a strategic investment designed to fundamentally elevate how we engage with our most important customers. Blending the timeless aesthetics of a classic English garden with immersive, high-tech innovation — from living digital walls to a three-story atrium — The Model Garden acts as a physical marketplace for our best ideas.

The blueprint for the agentic enterprise

For UK businesses, civic leaders, and organizations to continue to lead in the AI moment, they must not only rethink the technology they use but also fundamental aspects of how we work. As we support thousands of organizations and millions of teams here and around the globe, we see three core strategies helping achieve success with AI:

Culture: We must reimagine our organizations for the future. True transformation means getting teams excited, enabled, and equipped to work with AI agents in completely new ways. It is about human-AI collaboration, not just automation.
Responsibility: We must build with safety and security in mind from day one. Protecting your users, your customers, and your brand is paramount. Our frontier models are built on a foundation of rigorous AI principles and secure-by-design infrastructure.
Sustainability: In an era of rising compute demands, we must scale in a way that is both financially viable and positive for our planet. At Google, we are committed to carbon-free energy 24/7, ensuring that the UK’s AI growth does not come at the cost of our climate goals.

Architecting the future together

Google Cloud is the primary partner for the UK’s agentic transition. We are moving beyond the hype of experimentation into the rigor of production. From the research labs of King's Cross to the diverse enterprises powering the high street, we are architecting a resilient, sovereign, and prosperous future for the United Kingdom.

Thank you to everyone who’s joining us in London — yesterday, today, and into the future. This year we’ve packaged up an exclusive on-demand experience, allowing you to stream the defining London Summit moments, available anywhere, anytime.

Customers

How Yahoo optimizes resources with flexible VMs in Managed Service for Apache Spark

Operational trade-offs of static configurations

Rules for configuring flexible clusters

Two ways flexible VMs support massive workloads

gcloud example

API example

Establishing an infrastructure policy

Yahoo impact and results

Strategic benefits of flexible infrastructure

Build your resilient data pipeline

How BlackLine simplifies perimeter policy intelligence with VPC Service Controls

How BlackLine streamlines incident response

Streamlining the perimeter operations lifecycle

Core VPC-SC operations: Simple perimeter enforcement

Getting started

Reimagining work: How Pythian’s internal AI playbook delivers customer ROI

The four pillars of the Pythian AI operating model

Real-world impact: from database ops to global supply chains

Ready to build your AI operating model?

How Uber improves network reliability while unblocking cloud migration

Prioritizing critical traffic

Uber's key takeaways

Securing network reliability for AI and beyond

How Vodafone is using gen AI to enhance network life cycle

How Box is unlocking multimodal enterprise agents with Gemini Embeddings 2

Benefits of improved embedding: Extending the dimensions of document content

The Architectural Solution: Gemini Multimodal Embeddings 2

Three core patterns of multimodal enterprise agents

Pattern 1: Complex financial & analytical reporting

The challenge

The multimodal advantage

Pattern 2: Multimodal clinical decision support & assisted diagnosis

The challenge

The multimodal advantage

Pattern 3: Cross-document multimodal synthesis & data reconciliation

The challenge

The multimodal advantage

The future of agentic enterprise content management

Building operational resilience with agentic AI in financial services

From tabletop exercises to resilience intelligence

Dual orchestration for control and flexibility

Powering generation and governance with Google Cloud

Scalable, evidence-ready resilience testing

Toward resilience intelligence

How WPP operationalizes platform and data engineering for AI marketing

How Malachyte solves retail’s cold-start problem with managed real-time AI

How Malachyte lifted sales for their users

Try it for yourself

GOL! How TelevisaUnivision streamed the FIFA World Cup to millions with Google Cloud

Why TelevisaUnivision selected Google Cloud

Platform architecture

Obsession for customer success

Summary

Mirendil taps AI Hypercomputer TPUs and GPUs for pre- and post-training applications

Scaling agentic AI: How UiPath built its high-performance GPU platform on AI Hypercomputer

The context: heavy-duty math

The challenge: more demand than supply

The solution: a shared GPU fleet

Why Google Cloud: AI Hypercomputer architecture

Practical validation: differentiated models at scale

Lessons learned

Next steps

How Target is enhancing retail discovery and cutting database maintenance by 50% with Spanner Graph

Overcoming fragmented architecture

Building the enterprise ontology on Spanner Graph

A smooth, zero-downtime incremental migration

Business impact

How Deutsche Bank unlocked agility with an API-ready ecosystem

Building the backbone: four key capabilities

The path forward

Emerging standards: MCP, A2A, and the future

The vision: APIs as universal interface

Lessons learned

Conclusion

Best Buy scales AI workloads and secures access with Workforce Identity Federation

Replacing service accounts with direct federation

Key implementation decisions

Secure authentication for developers

Expanding Workforce Identity Federation support