Please welcome CEO of Google and Alphabet, Sundar Pichai.
Good morning, everyone. Welcome to Gemini at Work. We are glad you are here with us at Hangar One, now part of the NASA Ames Research Center. They tell me this building is so big that a cloud once formed inside it. Naturally, on its own, no migration plans, no procurement cycle, no six-month pilot. This is also where scientists and engineers gather to tackle problems that nobody had solved before. So it's a very inspiring and fitting setting for today, as we work together to enable a new frontier of work.
At Google Cloud, we are seeing a lot of momentum on this front. Enterprise adoption is moving faster than most people realize. Nearly 80% of all Google Cloud customers are actively using our AI products. And they are not just using it. They're using it intensely. Nearly 500 enterprise customers have each processed over one trillion tokens on our platform in the last year. This kind of scale tells us we are moving from a period of experimentation to fully integrating AI into your core business.
This rapid scale is only possible because of Google's full-stack approach. You've heard me talk about it before, because it's so integral to how we are able to deliver for you all, our customers. For example, our research and models are so foundational to our progress, and we are getting together here at an exciting moment. Last week, we announced our new frontier model, Gemini 4 Argon. It's an extremely capable model, so it's been built with frontier safeguards. We've been sharing it with a subset of cyber defenders through our Fairwind Program, and we are working to get it to more people as safely as possible, as soon as possible. Let's look at what it can do.
Argon delivers frontier performance and complex workflows across knowledge work, real-world software engineering and cybersecurity defense. Going deeper on knowledge work, where Argon really excels. Here, you can see Argon leads on a Vals Index, which measures economic impact across finance, coding, legal and tax work. On coding, you can see our lead here on DeepSWE, which measures real-world, long-horizon software engineering tasks. And it's not just what the benchmarks are showing. Googlers are feeling the difference internally, too.
Just in the past four weeks, Argon in Antigravity has been driving a more than 35% increase in agentic code submissions. It's not just improving velocity. It's improving quality out of the gate, reducing code rollbacks by nearly a third. It's also enabling Googlers to do things they couldn't do before. For example, a team in Chrome, a product that has more than 3 billion users, has been using Argon to refactor features that span millions of lines of code. Normally, this would take months. They've done it in weeks.
It's helped deliver two major wins. First, it enables a new class of UI experiences in Chrome, so a live web page can move fluidly between a tab, a side panel, a floating window or other experiences without being reloaded. Second, it helps eliminate an entire category of lifecycle crashes to make the Chrome browser more stable. In addition to knowledge work and software engineering, Argon is state-of-the-art at cyber. It's so good, in fact, that Wiz is already using Argon for protecting critical infrastructure. In one example, the model uncovered and helped fix a critical vulnerability in healthcare software used by hospitals worldwide, something previous frontier models had missed.
I'm really glad some of our Cloud customers, many of whom are here today, have been testing Gemini 4 Argon alongside us. Thank you. We've heard great feedback that the model excels at processing huge, unstructured inputs into more cohesive outputs, and that it also executes complex enterprise workflows to completion. And today, we are expanding to more customers around the world. As these examples show, we built Argon to be really capable across a range of domains important to how you all get work done.
These capabilities are also necessary to unlock the next era of agents, for both businesses and consumers. We have one billion users now on the Gemini app, and we are seeing a clear shift. They are not just asking Gemini to know things. They are using Gemini to do things. For businesses, you are already ahead of the curve on agents. Last year at Gemini at Work, we announced Gemini Enterprise, a front door for building agents for different purposes and tasks. And today, nearly 90% of Fortune 100 use Gemini Enterprise at work. So do thousands of start-ups, small businesses and companies of all sizes in between.
We are using it extensively at Google across finance, legal, marketing and many other teams. As the next step, we are evolving Gemini Enterprise to make how you get things done even simpler. Gemini will now become your universal agent for work. It's always on, ready to go, not only ready to answer your questions, it will write the code, complete longer and more complex tasks, whatever you need to get the job done. And because it knows your organization, it works with the security, administration and governance required by your company.
Launching these types of powerful agents needs to be done safely. By bringing it to businesses first, we are able to solve the harder problems around security, scale and performance. We are planning to roll this experience out to consumers soon. It's a really exciting time, and Enterprise is once again at the forefront. To show you more, let me turn it over to Thomas Kurian, CEO of Google Cloud.
Thank you, Sundar. Gemini is transforming how work is getting done. I want to start with what is already happening, because it's further along than most people think. It's happening every second of the day, somewhere around the world. Right now, it's evening in Europe. Nokia cuts telecom network troubleshooting time by up to 80%. Commerzbank has made quality assurance for document development up to 20 times faster. Joining them, Berenberg, Bosch, Bouygues Telecom, Siemens and Starling, manufacturing, telecom and banking.
It's now the middle of the night in Asia, and the work hasn't stopped. At NTT Docomo, data agents reduced time to go from data collection to insight, from two weeks to instantaneous, which has given them back 450,000 hours a year, all back to the business. Sompo has built specialized agents on company-specific insurance expertise alongside general capabilities like document search and summarization for employee efficiency.
Wesfarmers has scaled agents across its entire retail portfolio, an internal agent at Bunnings saved their staff half a million hours while customer-facing agents drove conversion rates up to three times higher for Kmart and Officeworks. Across the ocean, halfway around the world, it's early afternoon in Brazil. Where Globo reviews ad creative three times faster, Banco BV personalizes client communications 80% faster, Grupo Marista, one of their largest hospital systems, improves claims analysis time, reducing it from three days to just four hours.
iFood in delivery, Livelo in loyalty, Yduqs in education, and Sebrae in public services are all built on the very same platform. And starting their day with Gemini Enterprise this morning in the United States, Wal-Mart, Verizon, PayPal, McDonald's, Goldman Sachs, Major League Baseball, Newsweek and Intel. Notice who's on that list. The most regulated, most conservative buyers in the world. 25 banks, insurers, stock exchanges, hospital systems, law firms, state governments. These are not organizations that have traditionally moved first or traditionally moved fast. But they have moved.
The companies building what comes next are here, too. Canva, Every Cure, Harvey, Lovable, Recursion, Replit and many more. Every continent, every industry, companies both large and small, the very same AI platform. And all start with one common starting point. The prompt window. This has become the new interface for work. But the window is the easy part. The question is what sits behind it. Today, Gemini becomes an agent. You give it objectives, not just instructions. It plans the work, uses custom skills and tools, connects to your systems and brings back something finished.
Six things make it different from anything else in the market. First and most importantly, it is one agent. Gemini answers your questions, does your knowledge work, creates your images and media, writes and runs your code, all in one window. All with one agent. That very same agent also orchestrates processes that can run for hours. Give it any role you want, a personal agent, a teammate or a specialist carrying the skills your company needs. You can assign tasks directly or have it start on a schedule or based on an event.
You know, with other providers, chat and code live in separate tabs. They do not share a history. Gemini does it all in one window with one memory. Gemini is one agent doing all of it. Second, it's the very same agent everywhere, on your phone, the web, the desktop, the command line interface, in Google Workspace, in Microsoft 365, ServiceNow and Slack. That works because Gemini runs in the cloud, not on your laptop. One set of memories, one context, one personalization graph.
Start on the command line this morning, finish on the web tonight, you don't have to re-explain anything. Others run desktop agents, and a separate cloud agent that don't share memory. Third, Gemini can coordinate and create a roster of co-worker agents. It's not just a single agent working on its own. Because it runs in the cloud, the size of the job is not limited by the size of your desktop. When the job gets bigger and more complex than a single agent can perform, Gemini can dynamically create a roster of agents, assign each of them tasks, message them directly and coordinate them until the work is completed.
Fourth, it arrives knowing you and your business. Your tools, your skills, your data, who you work with and your own history of how you work. Gemini builds that picture from everywhere the work happens, in the Gemini app, in Google Workspace, Slack, Microsoft Office. It holds this information as a single personalization record. The longer you work with it, the better it gets to know you and how you work.
Fifth, it inherits the controls you already have. Who it is, what it can reach, what it can spend and what it did. Every Gemini agent has an identity of its own, separate from your identity. Your policies reach it the very same way they reach your people. Set the policies once, apply them everywhere rather than agent by agent. You set its budget before it starts, and that limit covers the whole cost of the work. And every action is recorded in its own name with its own identity so that you can always answer who did what and on whose behalf and when.
Finally, it's not tied to a single model. Gemini is the agent. The model underneath is a separate choice, and it is yours. From Argon at the frontier to Flash for speed, and to the latest Claude models. Other leading proprietary and open models are also coming soon. You can see that at Shopify, blending frontier models to drive sales insights across millions of merchants, at PayPal, routing 10 million multi-model requests every week. Now let me show you what Gemini can do for people inside your company. Please welcome Adam Fisher.
Thank you, Thomas. Great to be with you all today. Earlier this year, we made the leap from chatting with models to having agents do the work. Today, the Gemini agent brings that magic to everyone in the enterprise. You describe the task. The Gemini agent does the work in its own secure computer. I'm going to walk you through a few examples of how I'm using Gemini in hopes that it provides you some inspiration. This is the Gemini agent on my desktop. I can also work from web, mobile, CLI and other surfaces.
As you can see, there's one clean place to start: a single prompt box where I can ask a question, delegate a task or create code. I'll begin where many of us do. Let's plan for a launch review where I need to build the materials. Now, normally, this means hunting through files and folders, reading 20 chat spaces and then massaging pixels around a slide for hours. But not with Gemini. A lot of our customers work in Microsoft. So let's build there. In full disclosure, you're going to watch me paste my prompts today so you don't have to watch me type. Let's get into it.
I tell Gemini what I need: a PowerPoint, a revenue model and a product video. Gemini understands my goal, builds a plan and creates its own subagents to get started. While that's running, let me show you around. I'll go back to a new task. We already looked at the prompt box, but here I have the ability to pick which model I use. By default, auto mode picks the best model for each task to balance speed, quality and cost. I can also choose Gemini or third-party models. I can attach files or folders or even a project for specific workstreams.
A project is a combination of the right files, the right skills and even the right agents that helps Gemini do the job. For example, my Q4 product launch. I can equip Gemini with skills. These are simple instructions that can come from my company, a GitHub repository, or I can easily make these on my own. And Gemini is also directly connected to the systems and data that businesses use every day. Things like Google Workspace, Microsoft 365, Slack, Jira and more. And I can keep an eye on everything Gemini is doing right here in my tasks inbox.
That job we launched is still running so let's look at a previous version that I made. And before diving into the output, I really want to show you Gemini's thinking process. Here, we can see that it understood my goal, built a plan and then started delegating to subagents. It loaded skills like this corporate brand kit, and it even wrote code to build these materials. All right. Let's look at that PowerPoint. So, Gemini didn't just write bullets. It used my brand guidelines to create a beautiful PowerPoint. Let's see here.
It pulled the right numbers from that Excel model that we built, knew my timeline, and even used Omni to generate a product launch video. Let's take a listen. Not bad for a first task. This would have taken me half a day to do. The next thing that I love is Gemini is available where I already work with my team. For me, this is in Google Workspace. Let's head over.
In Workspace, Gemini can work like a co-worker. It has its own Workspace account, its own email address, its own context. And my team works with it the way they work with, well, anyone. Tag it, email it, share with it. But it only knows what we give it. To demonstrate, we've added an events agent right here in the group chat. I'll tag it in a comment now. I'm asking it to help with our regional launch. Just like that, the agent jumps in to give us a hand. It's using skills and connectors that we provided to pull the right context from our previous work.
Looks like it pulled together the workstream status, checklist and the risk assessment. Let's look at the document. All right. I already see that I love it because it's using a template that I chose. I have an exec summary. It calls out the action items and owners. And here, even that detailed checklist. But, I'm seeing it missed some of the recent meeting updates that I wanted included, so I'll drop a comment. Watch as the agent goes to work. Did you see that? It made both a suggested edit and commented a reply under its own name. I didn't even have to touch the keyboard.
So, this document is now ready for review. Let's ask Gemini to get the leads together. Gemini works like a well-informed assistant. I don't have to give it names or addresses. It knows who my team is, what timezone they're in, and whether I usually work with their calendar delegate. It also gets my approval before it contacts anyone. Let's see what it's drafted. Nice and short. I love it. I'll allow it. This is Workspace Intelligence in action.
Behind the scenes, Gemini emails the leads on my behalf, suggesting dates and times that work, and finds one that is right for the internal and external teams. Now, normally we'd have to wait a long time for all these replies to come in, but I've got a great team backstage helping us out so we can move this along more quickly. And just like that, the meeting is confirmed. Gemini handled all the scheduling back and forth, and now all I have to do is show up. And thank goodness, because I've got a lot going on today.
And this is why I'm also happy Gemini is proactive. Let's head over to the inbox and check it out. Sorting through my inbox and getting tasks done, it's always a ton of work. But Gemini identifies what matters and helps me action it more efficiently. Look at this top card. There is an important leadership meeting and our key stakeholder, Matt, oh, he's out of the office the day before. We need to make sure he has the right context going in. Now, I never told Gemini any of this. It knows my calendar, my team, my files, and I don't have anything to connect, and no automations to set up.
And it doesn't just surface this important information. If I click start with Gemini, it prepares the executive summary for Matt with the right context and highlights the risks and updates he needs to know. Now, this is going to take a few minutes to finish, so we're not going to wait around. But if I need to step away from my desk, I can pick this project up directly from my phone. Let me show you. We already looked at chat, so I'm going to show you this in Slack now. All right. I get a lot of important customer feedback, so I'm going to have Gemini see what it can find.
Gemini is on it. Oh, here we go. 35 comments across three threads. This is a lot. We need to make sure this is included. Add this to the executive summary we worked on earlier. Great! Gemini has got it from here! Now, we've seen a few examples of how Gemini works with existing tools, but sometimes the one that I need, it doesn't exist. Let's go back to the desktop.
All right. Every week, I spend hours pulling together updates across different sheets and Jira blockers and other data sources to make a status report. How can I help save some time on this? Let's see what Gemini thinks. It's got three suggestions for me. But the one it recommends is a live microsite. Sounds good. Let's try it. I can choose a few different hosting options, but I want this to be fast. So let's just go with the HTML mock-up. Without writing or even reading a single line of code, I can quickly prototype a tool that gives me insight that was previously available only across multiple other data sources.
Here we go. I like the layout. It's got some great filters here. Yeah. I think this is definitely going to be easier for the team to manage. While Gemini can write all the code and deploy this microsite to production from here, I want to show you how this would look for an engineer doing the same thing from the terminal. Same Gemini, same work, just in the CLI. Gemini will spin up a dynamic team of subagents. It will use the skills I have to read the sheets, connect the data and write the code.
Once it finishes, Gemini can also test and debug the system. It can keep the data up to date, and add the readiness scores and more. The bottom line here is you pick the surface. The agent stays the same. And that surface matters, because over time, Gemini learns how and where I work. It uses a Dream Mode to turn our conversations into memories and preferences. Without telling it any of these things, Gemini learned who I am and where I fit into my organization. It took notes on how I communicate and how that communication varies by stakeholder.
And it learned my preferences. For me, this is, never use an em dash, obviously. Or, executive summaries always have the bottom line up front. And this is where I think things get really powerful. Let me show you an example of how I'm using this with a scheduled job. Every morning, Gemini reads my new emails and drafts a reply. As part of a wake-up routine. If I review these emails and edit one over coffee and then send it, Gemini reviews the differences and improves the next day.
As Gemini learns my style, it needs fewer nudges and can do more on my behalf in the background while I focus on other priorities. This is a compounding benefit for your business. But if this room has any CIOs in it, I'm sure there are at least a few breaking out into a cold sweat, which is why we need to talk about one final component: governance. While Gemini is personalized to me as a builder, admins have complete visibility and control across the entire company. In the console, admins govern everything users and agents do.
Each agent has a unique identity, so admins can distinguish the actions I take versus the actions my agents take. For example, here's a trace that shows exactly what an agent did on a previous run. You can see here the agents that were invoked, the LLM calls, a policy, tool calls, all of it. Very powerful. And speaking of policies, admins can also set policies across the organization, like mandating that any external email has a human in the loop review, or like automatically blocking the sharing of sensitive data.
Finally, admins control project and user spend, so tasks and background routines never overrun the budget. For example, here, I'm getting pretty close. You can see what portion of the budget I've spent, what my fixed caps are, and any alerts that pop up from the spend. To sum it up, every action is tracked, every policy enforced, every budget is kept in check. That's how you unlock the full power of Gemini. Gemini has completely changed the way I work.
The Gemini agent is available across different surfaces, learns my preferences and runs in the background. And it's not just for me. Everyone on my team can use Gemini with the skills, tools and preferences they need. And whatever the role, it starts the same way. You describe the task. The Gemini agent does the work. Back to you, Thomas.
Thank you so much, Adam. One agent. Five surfaces. Desktop, Web, CLI, Mobile, Browser. One agent. Three types of work: chat, tasks and code. Over 50 companies are running it today. Adopting an agent-led strategy, On successfully transitioned 24 core enterprise services to Google Cloud, cutting migration time from three months to two weeks per service. In addition, they're testing the Gemini agent in early access to boost process optimization, selecting the best model for each use case.
This agentic-first approach has teams reporting meaningful productivity gains, keeping their teams focused on innovating the sportswear industry with a customer-centric approach. Now, speed is just one kind of number. The one I get asked most about is revenue. Ulta Beauty used Gemini Enterprise to build Ulta AI to guide guests through more than 30,000 different products. Customers can ask questions as they shop. They receive personalized guidance, and tailored product recommendations. As a result, this has driven a three-times-higher conversion rate than traditional browsing on their site.
Now, the prompt window is becoming the interface. Not one of several. But the interface for getting work done. Everything we build from here assumes it. You just watched the Gemini agent in action, already knowing your business, your data and the answers you wrote months ago. Nobody manually loaded any of it. The Gemini agent comes with three important capabilities to connect to your enterprise systems, understand the information in them and let it run a process end to end on your behalf.
First, skills. The Gemini agent comes with a broad set of skills, which are reusable sets of instructions, knowledge or workflows, stored as markdown files or modular prompts that teach it how to perform specific multi-step tasks. Gemini ships with a broad skills library, and each person or team can build their own skills and publish them to this shared registry, where any other user or agent can find them. Each time Gemini uses a skill, it learns from it, improving its consistency, and following the same standard procedure every time.
It also improves efficiency by using the right skills, saving you tokens and improving accuracy. Second, tools. Tools are how the Gemini agent reaches the software your company already runs. Workspace, Microsoft Office, Teams and Slack for collaboration, Jira, Confluence and Git for building, Salesforce and ServiceNow for running the business. It can also connect securely to any MCP server. Inside your company or on the internet. And as with skills, your teams can build their own tools and publish them to the registry.
Third, context. Gemini learns from each question you ask it, each tool it uses, each objective you assign it, and uses your personal context and your organization's context to personalize and improve the quality of its responses. Nowhere is that clearer than in Google Workspace. Gemini works directly inside Gmail, Drive, Docs, Slides, Sheets, Chat and Calendar, carrying the same memory, skills and controls it has everywhere else. Because it already knows your calendar, your team and your project.
It is briefed before you start, and it puts that context to work, suggesting what you can hand off and surfacing the email and chat messages that matter most to you. As Gemini works, it maintains four kinds of memory: session memory for the task directly in front of it; semantic memory, which is a structured knowledge base it builds as it works; procedural memory for how a job gets done; and episodic memory of everything that it has done before.
In addition to providing you a powerful Gemini agent, we are also specializing each of these capabilities for specific domains, starting with data analytics. To help everyone in your organization get reliable operational reports to monitor your business, users can ask Gemini their questions in plain language. The Gemini agent uses specific skills with BigQuery and our Knowledge Catalog to generate and save the query. Once done, you can run the report without incurring the cost of tokens and get a reliable answer every single time. We're also extending the Gemini
agent with machine learning skills and tools to back every data engineer with a team of agents. Data engineers describe the outcome they want. Gemini generates PySpark for the machine learning model, provides local notebooks to edit the code, trains the model and automatically fixes issues. To process data accurately from your many enterprise databases and applications, we've integrated our Knowledge Catalog with Gemini. The Knowledge Catalog maps your business data definitions, with no re-indexing. So when the Gemini agent reads "net margin," it knows exactly what your company means by that metric. This helps improve accuracy by knowing your schemas and business rules, making responses more reliable. You define those metrics just once, and all Gemini agents use them. By the way, if that metric lives in Databricks, in DBT and LookML or in SAP, Gemini reads those metrics wherever they're defined. Let's show you these data capabilities in action. Please welcome Gabe Weiss.
Thanks, Thomas. So let's say I'm a data scientist at a retail bank, and I need to build an ML model to help marketing identify the customers most likely to switch their high-rate credit card to new lower-rate personal loans. So I can just start by asking Gemini, what data do we have on customer accounts, card spending and past loan offers? Visualize my data landscape. Gemini makes this easy with a set of skills, including pre-built data Cloud skills to work across my data estate. Now, Knowledge Catalog has already registered my Google Cloud and cross-cloud data sets, including rich metadata, schemas and glossaries. And not only that, our developers can easily create, register and describe new data sets and business logic in Knowledge Catalog, making both those data sets and their descriptions available to humans and Gemini agents. So now, let's put this data to work.
Find me customers who could save money by switching their high-rate card balances to our new lower-rate personal loan. Now, we're going to watch Gemini as it generates PySpark code that queries our customer and card spend data sets from the Lakehouse, and pushes zero-copy joins to Lightning Engine using those pre-built data cloud skills. Now, here we are. None of this is in a black box. In our next-generation python notebook, this environment is live. I can do stuff like edit the PySpark code live line by line right in Gemini, or if I want, I can change this drop-down and all of the charts below it recompute instantly. With this, we've already identified over 50,000 customers that could save $3,400 on average. That's great. But I want to know who would actually say yes. So for that, I need a model. So I can prompt: Build a machine learning model to identify which of these customers have the highest likelihood of switching to our offering. Gemini queries the Knowledge Catalog campaign history from the past three years stored in BigQuery. Using those pre-built data cloud skills, Gemini is going to pick the best algorithm, XGBoost, and chooses the best service for training, Management Spark. Now, we get all of this really high-quality code on the right. So just as an example, I can see here, it knows the columns from the data that I need to reconstruct the features for training. Gemini evaluates this model on historical test data, and in this case, I can see we've achieved 90% model accuracy with an 85% precision rate. And in addition, it's all explainable. Here, Gemini is actually telling me which signals drove it, confirming that high-interest credit card rates and steady monthly payroll deposits are the strongest propensity drivers. Now, I love the quality of this model. I'm going to go ahead and publish it here to Knowledge Catalog. What this does is this will allow my business users and my agents to use it. So knowing that, let me switch gears for a minute. Let's say now I'm that marketing manager and I want to use that machine learning model. So I can ask it: Identify the customers in Illinois most likely to take this offer and create me an E-mail campaign, including some digital marketing assets. Gemini finds the model in Knowledge Catalog, and invokes it. Because this is all built on the same Gemini agent that uses the same tools, skills and memories to draft compliant E-mail copy, make me some creative assets, generate a CSV for the target segment and stage the whole campaign in Google Marketing Platform. This is great. Let's fast forward a week. My campaign's been running and to track the business impact, I can ask it: Show me how our personal loan campaign is performing by the top five counties. And in just seconds, we get a dashboard on live data. Gemini also makes it really easy to build on our data story. Let's go ahead and add an additional KPI just so I can really understand the total impact. Add a KPI for the median time for how fast the campaign is turning into loans. This is going to easily add that metric to my dashboard, which supports interactive parameter controls that I can modify without the LLM. So let me show you what that means. First, I can see here, great, it added me the tile with my new KPI. And when I click into the different counties, behind the scenes, that dashboard is leveraging dynamic query templates that are stored in Knowledge Catalog. These context templates allow me to run the chart queries with zero LLM tokens and no compromise on accuracy. Business intelligence dashboards also stay fresh and secure, grounded in your enterprise data. And from here, I can go ahead and share the live results with the rest of my team. What used to take months of manual work just happened in minutes with an enterprise-grade, governed, transparent workflow. We created an ML model, empowered our marketers to visualize data in real-time, leveraging it and did so with less token spend and high accuracy, all grounded in Knowledge Catalog. Thanks! Back to you, Thomas!
Thank you, Gabe. 90% of enterprise data is unstructured. Images, PDFs, recordings, scans. For most companies, that information is dark. You store it. You pay for it. No system ever reads it. Smart storage changes that. It uses Gemini to enrich those objects in place, writing context back on to the object itself, so the intelligence stays where the bytes are and inherits the security postures you already have. Snap connected its prism agent straight to its storage archives and cut diagnostic trouble shadowing from 30 minutes to 30 seconds. And once Gemini can read an image, it can check and validate work that used to depend on someone remembering. Hitachi, for example, with the help of Gemini Enterprise, is transforming global power and utilities by putting agents in the hands of their frontline workers using HMAX, their portfolio of AI-powered solutions. Technicians have built custom AI agents for over 200 use cases. For example, instantly verifying photos taken of every tool and wire before and after a job. This helps to keep their team safer while boosting productivity by up to 30%. You know, your data lives across clouds. In SaaS applications, and even on premises. We're not asking you to move your data. Gemini uses our borderless Lakehouse to query Amazon S3 and Azure Data Lake with no variable egress fees. It reads Salesforce, SAP, ServiceNow and Workday, without copying anything. And it federates open Iceberg across Databricks Unity, Snowflake Polaris and AWS Glue. Deutsch Telekom has made data collection, collation and analysis three to four times faster. Etsy unified three petabytes of eCommerce data on open Iceberg to run its pipeline queries 60% faster. Bloomberg grounded its data agents in the Knowledge Catalog and lifted query accuracy by 63%. Here's how a leading industrial automation company is protecting mission-critical systems by predicting maintenance problems before they happen using Gemini.
