Snowflake Unveils AI-Powered Data Intelligence Tools at Summit 2025

Snowflake's new AI agents make it easier for businesses to make sense of their data

Snowflake Intelligence spans structured and unstructured data from a variety of services. Snowflake kicked off its annual user conference, Snowflake Summit 2025, on Tuesday. The cloud-based data-storage company launched a slew of new features. The biggest highlight was agentic AI solutions that help organizations better make sense of their data: Snowflake Intelligence and Data Science Agent. With the rise of agentic AI, Snowflake is the latest company to embrace the burgeoning technology to optimize how companies sort, analyze, and understand their data. AI chatbots have risen in popularity because they make it easy to find what you are looking for using a simple, conversational text prompt. Snowflake is now bringing that capability to company data with Snowflake Intelligence, allowing business users to access insights using natural language queries in one unified platform. Also: Tech leaders are seemingly rushing to deploy agentic AI - here's why The experience, powered by OpenAI and Anthropic large language models and Cortex Agents, pulls from both structured data -- think data that has been carefully organized into tables or standardized formats -- and unstructured data, like documents, emails, etc. This eliminates a common technical challenge that companies face when adopting AI tools: their data isn't structured correctly. "Snowflake Intelligence breaks down these barriers by democratizing the ability to extract meaningful intelligence from an organization's entire enterprise data estate - structured and unstructured data alike," said Baris Gultekin, head of AI at Snowflake, in the blog post. "This isn't just about accessing data, it's about empowering every employee to make faster, smarter decisions with all of their business context at their fingertips." Snowflake Intelligence runs within the organization's existing Snowflake environment, keeping all of the existing security controls and governance policies in place. To make the data it pulls from as comprehensive as possible, it draws from different data sources, including Snowflake, Box, Google Drive, Workday, and data types, using Snowflake Openflow to bring together insights from spreadsheets, documents, images, and databases. Beyond generating insights, Snowflake Intelligence can also render visualizations of the data and access third-party knowledge through Cortex Knowledge Extensions, which will be generally available soon on Snowflake Marketplace, incorporating expert content from Stack Overflow, The Associated Press, USA TODAY, and more. Data scientists are in demand as ever, as they are responsible for building machine learning workflows. Using Anthropic's Claude, the Data Science Agent seeks to help ML teams by taking on some of the manual tasks, such as data analysis, data preparation, and training. Also: What are AI agents? How to access a team of personalized assistants Specifically, the agent provides "verified solutions in the form of fully functional ML pipelines that can be easily executed from a Snowflake Notebook," Snowflake said. The goal is for data scientists to be able to shift their priorities to more high-impact work, as well as reduce the amount of time an ML use case goes from idea to production. Get the morning's top stories in your inbox each day with our Tech Today newsletter.

How Snowflake's new tools turn business analysts into AI developers

Data warehousing giant Snowflake is holding its annual user and partner conference, Snowflake Summit 2025, this week. As with most infrastructure software vendors, the company emphasized the proliferation of artificial intelligence (AI) across its platform. Given Snowflake's focus on the enterprise, with almost 12,000 customers, the pitch of all the announcements had a singular message: Business analysts, the individuals who primarily work with the Snowflake database to get work done, can be the driving force behind both developing AI models and making predictions with those models. Also: Snowflake's new AI agents make it easier for businesses to make sense of their data Among the new features, ZDNET's Sabrina Ortiz relates that the chat mode lets one speak with the data, if you will, using natural language prompts. It is powered by OpenAI and Anthropic LLMs, along with Snowflake's own Cortex models. Data prep and analysis are made less burdensome by a new Data Science Agent feature that can automate some tasks. (Disclosure: Ziff Davis, ZDNET's parent company, filed an April 2025 lawsuit against OpenAI, alleging it infringed Ziff Davis copyrights in training and operating its AI systems.) As ZDNET's Webb Wright relates, a new service called Openflow is Snowflake's take on the classic data integration pipeline, known by the industry rubric "extract, transform, and load," or ETL. The Openflow functions will aid the production of AI agents, the company claims, by streamlining the complex process of cleaning up the data that has to be used by the agents. In addition to those two announcements, the company doubled down on its efforts to make its programs where Gen AI development happens. A function called Cortex AISQL allows business analysts to wrap the output of AI models inside standard SQL query language commands. For example, a "JOIN" command, one of the most basic ways of manipulating a relational database table, instead of being hard-coded to certain tables, can take a variable value based on what the AI model says about, say, a person's resume in relation to open job offerings in a company. Also: Snowflake launches Openflow to help businesses manage data in the age of AI The company claims that this makes it easier to create complex, "multi-step" queries on data with less coding. It also raises the profile of the business analyst, says Snowflake. "This unified approach transforms what would traditionally require data science expertise and weeks of development into straightforward SQL queries that business analysts can build and modify in minutes." And that, it says, "turns analysts into AI developers." The company says that by plugging SQL into the AI "pipeline," the tool "uplevels data analysts into AI superheroes who can work with all types of data." The traditional DevOps or DevSecOps domain of observability is being applied to large language models to let Snowflake customers continually evaluate how an AI model is performing relative to criteria of trustworthiness, etc. The company says the tool has "evaluation data sets" to measure the model's output and logging capabilities to facilitate debugging, prompt refinement, and governance. Also: Snowflake customers eke out early gains from Gen AI applications In a sense, Snowflake is making a statement with this tool, namely, that Gen AI training and maintenance is in some sense the province of the business analysts rather than the traditional IT folks who carry out DevOps or even AIOps. There are innovations as well regarding the engineering of AI models, innovations that Snowflake claims make the process of building the models more tightly integrated with its tools and also broaden what can be served in production. One is the ability to run machine learning (ML) code from a development environment or notebook context with what's called ML Jobs inside a Snowflake container service. That means the AI model tasks of training and the rest can be spun up within the rest of Snowflake's development work. ML Jobs is expected to be "generally available soon" on Amazon AWS and Microsoft Azure. Also: Nvidia teams up with Snowflake for large language model AI There's a new way to grab the best-performing AI models during the training process, called experiment tracking, that lets a developer share and reproduce that individual model. That function is currently in a private preview. For serving trained models, Snowflake has added to its Model Registry the ability to serve those developed and staged on Hugging Face. Says Snowflake, any model on Hugging Face can be brought into the Snowflake container service "in one click without downloading any client-side model ... by just pointing to the model handle and task for logging and serving in Snowflake."

Snowflake expands AI tools to streamline enterprise data workflows and speed machine learning - SiliconANGLE

Snowflake expands AI tools to streamline enterprise data workflows and speed machine learning Snowflake Inc. is leading off its Snowflake Summit 2025 user conference in San Francisco today by unveiling a series of new artificial intelligence capabilities intended to simplify the ways users interact with data. The announcements reflect the company's push to embed generative AI across its platform and make advanced analytics more broadly accessible. Snowflake is betting that the future of enterprise analytics lies in seamless, AI-enhanced collaboration across technical and nontechnical teams and wants its platform to be the default interface between people and data. The strategy continues its evolution beyond its data warehousing roots into a unified platform for intelligent data operations. "The goal is to bring the power of AI to analysts and personas that are typically comfortable with database technology but may not be fully versed in how AI works," said Christian Kleinerman, Snowflake's executive vice president of product. Snowflake said the overall aim of today's announcements is to reduce friction in data workflows and shorten the time between data ingestion and insight. The broader context is more strategic. With data volumes surging and demand for AI tools growing across industries, Snowflake's latest offerings position it as not just a data cloud provider but as an AI-native platform. Leading the new lineup is Snowflake Intelligence, a conversational data agent that allows users to query enterprise data using natural language. Targeted at business users with limited coding skills, the tool enables them to ask plain-language questions of both structured and unstructured sources. The functionality is made possible by intelligent data agents that operate within an organization's Snowflake environment, inheriting all existing security, data masking and governance controls, Snowflake said. Embedding directly within a company's trusted infrastructure avoids the compliance and security tradeoffs often accompanying external AI tools. The agents can parse and unify data from various platforms including Google Drive, Workday Inc. applications, Box Inc. storage and Zendesk Inc.'s customer service platform using Snowflake Openflow, an extensible, managed multimodal data integration service for moving data between sources and destinations. The company said users can analyze spreadsheets, images, PDFs and database entries side by side with no custom data engineering required. Universal Search for External Data enables users to discover data assets in sources like PostgreSQL or MySQL from within Snowflake. This is a small but strategic move that reflects the reality that most enterprise data is spread across a mix of platforms, and any tool that can reduce the friction of finding and using that data adds significant value. Snowflake Intelligence supports access to third-party sources through Cortex Knowledge Extensions, a new feature in the Snowflake Marketplace that integrates external content from sources such as Associated Press, Stack Overflow, CB Insights and USA Today. That allows users to contextualize findings with current events, market trends and technical resources within the Snowflake platform. "Our goal is to leverage AI to continue to shrink the effort and time that it takes to migrate data from a number of sources to Snowflake," Kleinerman said. On the more technical side, Snowflake is introducing Data Science Agent, a feature now in private preview that helps data scientists automate some of the more repetitive and time-consuming elements of machine learning workflows, such as data preparation, feature engineering and model training. These steps often stall machine learning projects between experimentation and production because of a lack of engineering resources or the complexity of debugging pipelines. Data Science Agent uses Anthropic PBC's Claude large language model to dissect machine learning projects into logical steps and deliver executable pipeline components that can be run inside Snowflake Notebooks. Users can iterate on the results by adjusting parameters or adding follow-up prompts, effectively turning the model into a copilot for ML development. Cortex AISQL, the newest member of Snowflake's Cortex AI suite, is designed to extend the reach of SQL to unstructured formats like images, audio or long-form text. It effectively turns a familiar SQL environment into a multi-modal data interface for more informed analysis. Now in public preview, Cortex AISQL lets analysts use standard SQL commands to query across diverse data types. This permits structured sales data, for example, to be merged with social media sentiment or customer service transcripts to be integrated with customer relationship management records. That means analysts can engage with a wider range of enterprise data without needing to learn new programming languages. The system is backed by large language models from OpenAI, Meta Platforms Inc., Mistral AI SAS and Anthropic, which are integrated directly into Snowflake's SQL engine. Snowflake said performance optimization features currently in private preview promise up to 70% performance improvements and 60% cost savings, depending on the workload. Snowflake is addressing the complexity of migrating legacy data systems into the Snowflake ecosystem with SnowConvert AI, a tool that simplifies data migration from older platforms into the Snowflake data warehouse and speeds the process up to threefold, according to the company. SnowConvert AI leverages agents to automate code conversion, extract/transform/load processes, reconfiguration and report migrations. It also validates results with reduced human intervention. SnowConvert AI isn't limited to databases; it also supports migrations of some business intelligence tools and ETL workflows, enabling organizations to standardize on Snowflake for a greater quantity of storage and analytics. In the same vein, a new Universal Search for External Data feature, enables users to discover data in sources like PostgreSQL or MySQL from within Snowflake.

Snowflake platform enhancements focus on performance, governance and interoperability - SiliconANGLE

Snowflake platform enhancements focus on performance, governance and interoperability Snowflake Inc. today is introducing a broad slate of platform updates aimed at improving performance, reducing operational overhead and expanding its range of interoperable tools. The flagship announcement is that the latest release of the company's core data warehouse, called Standard Warehouse - Generation 2, is now generally available. Comprising both hardware and software upgrades to Snowflake's core compute engine, the new release promises more than double the analytics performance without requiring changes to existing workloads or queries and scales computing resources in line with growing data volumes and increasingly complex workloads, Snowflake said. "For some specific workloads, anything that is write-heavy or updates-heavy it can be two to four times faster," said Christian Kleinerman (pictured), executive vice president of product. Complementing the Gen2 rollout is the debut of Snowflake Adaptive Compute, a new compute service in private preview that Snowflake said automates much of the warehouse management process. The new approach, dubbed "adaptive warehouses," lets users bypass manual configuration tasks like sizing warehouses, setting concurrency levels or dealing with multicluster configurations by dynamically adjusting resources behind the scenes to optimize for cost and performance. Snowflake's shift to adaptive infrastructure is consistent with broader cloud platform trends toward greater elasticity and automation. It also aligns with the company's stated vision of turning data infrastructure into an invisible layer that requires little engineering attention. Snowflake is also introducing several features aimed at making data more discoverable, secure and interoperable. The Snowflake Horizon Catalog has been enhanced with AI-powered data governance tools, including a new Copilot for Horizon Catalog that uses the Snowflake Cortex AI platform to answer security and governance questions via a chat interface. Horizon Catalog also now supports catalog-linked databases that allow users to synchronize with Apache Iceberg-based metadata catalogs like Apache Polaris, Snowflake Open Catalog and Amazon Web Services Inc.'s Glue. That makes for more seamless governance across hybrid and multicloud environments, particularly when using Iceberg tables, Snowflake said. In a nod to rising concerns about data resilience, Snowflake is announcing support for the point-in-time, immutable backups called snapshots that safeguard against data loss or ransomware attacks. Once created, snapshots can't be altered or deleted, which has value in meeting regulatory cyber resilience standards. Security is further bolstered with new Trust Center Extensions that allow customers to integrate third-party security scanners tailored to their compliance requirements. AI Observability Tools, now generally available, deliver real-time diagnostics and performance insights across an the entire Snowflake data environment. In a bid to make data integration easier, Snowflake is introducing Openflow, a managed, extensible service that the company said simplifies ingesting structured and unstructured data from virtually any source. Based on Apache NiFi, a widely used open-source tool for data flow automation, Openflow supports batch and streaming workloads, including Snowpipe Streaming, and includes hundreds of pre-built connectors to third-party services. Openflow addresses what Snowflake said is the disproportionate time data engineering teams spend wrangling ingest pipelines. It's intended to reduce that effort while maintaining governance and flexibility. It also moves Snowflake into the $15 billion data integration market, which has historically been served mainly by third-party ETL tools. "AI has materially changed the ability for organizations to get value out of their unstructured data," Kleinerman said. "Openflow takes data from SharePoint or Drive or Slack and makes it available for customers to combine with their data." Also for the data engineering community, dbt Projects can soon be built and run natively in Snowflake, offering direct integration with the dbt framework that many teams already use for SQL-based data transformation. It's part of Snowflake Workspaces, a new file-based development environment that features AI-assisted coding, Git integration and side-by-side code comparison. Snowflake is deepening its support for Apache Iceberg by allowing customers to manage Iceberg tables more efficiently, take advantage of semi-structured data types such as VARIANT and tune partitions and file sizes. Finally, Snowflake is enhancing streaming capabilities in Snowpipe Streaming to permit ingestion rates of up to 10 gigabytes per second, with data available for querying within 10 seconds after ingest. The capability is particularly relevant for operational analytics, real-time personalization and other use cases where timeliness is critical.

Snowflake Summit 2025: The Biggest News In AI, Agents

"These are projects that are going to define the future of the [customer's] company," Snowflake CEO Sridhar Ramaswamy said. Snowflake Intelligence for natural language interaction with data. New data science AI agents that can help build machine learning pipelines. And a private preview for an Adaptive Compute service for automatic resource sizing and sharing. These are some of the most exciting reveals to come out of the AI and data cloud vendor's annual Snowflake Summit 2025 conference, which runs this week through Thursday in San Francisco. Snowflake CEO Sridhar Ramaswamy told CRN that the world of IT services is undergoing "a major change" as AI takes off and top executives look to AI to create more efficient, productive companies and shorten product life cycles. "These are no longer IT projects," Ramaswamy said during a pre-Summit press conference in answer to a CRN reporter's question. "These are transformation projects. These are projects that are going to define the future of the company." [RELATED: Snowflake Stock Surges After $1 Billion Sales Report] Snowflake's 2025 channel goals include increasing the overall percentage of company revenue that comes through the channel and enabling partners to develop an AI strategy and sell AI solutions, according to CRN's 2025 Channel Chiefs. The vendor has more than 10,000 partners worldwide, up from 600 in 2022, according to Snowflake. Christian Kleinerman, Snowflake's executive vice president of product, added that opportunity in the AI era "is bigger than ever" and goes beyond traditional customer relationships with system integrators and partners. "With the advent of AI, virtually every area of a business now has the opportunity to get improved, to increase productivity with AI," he said. "We're in very close touch with many of our partners on how do we help organizations unlock that promise of better business outcomes, faster business outcomes and agility on AI." In response to a CRN reporter's question on whether Salesforce's pending acquisition of Informatica for $8 billion will impact Snowflake and its partners, Amy Kodl, the vendor's interim alliances and channel lead, said in a statement that the deal "only underscores the importance of data in powering enterprise innovation in the age of AI." "Since inception, Snowflake has served as a leader in the data and AI landscape and will continue to deliver a unified, open data platform designed for the agility and AI innovation businesses demand," Kodl said. She added that Salesforce is a strategic Snowflake partner and that the two companies will "continue to empower joint customers through ongoing collaboration." Snowflake's annual Summit comes on the heels of an upbeat fiscal 2026 first quarter earnings report. The vendor beat expectations on revenue during the quarter and increased the amount of expected product revenue growth for the full fiscal year-increasing the amount by $45 million and increasing percent growth year over year from 24 to 25. Still, the vendor showed some areas that need improvement. That 25 percent growth figure is a deceleration from 30 percent growth in fiscal 2025 and 38 percent growth in fiscal 2024, according to a report by investment firm Bernstein. The company has also seen 13 quarters of decelerating product revenue and its net revenue retention (NRR) fell to 124 percent compared to 126 percent the prior quarter. Here's more of what to know as Snowflake Summit 2025 kicks off.

Snowflake Unveils Comprehensive Product Innovations To Empower Enterprises To Ac...

Snowflake (NYSE: SNOW), the AI Data Cloud company, today announced several product innovations at its annual user conference, Snowflake Summit 2025, designed to revolutionise how enterprises manage, analyse, and activate their data in the AI era. These announcements span data engineering, compute performance, analytics, and agentic AI capabilities, all aimed at helping organisations break down data silos and bridge the gap between enterprise data and business action -- without sacrificing control, simplicity, or governance. "Today's announcements underscore the rapid pace of innovation at Snowflake in our drive to empower every enterprise to unlock its full potential through data and AI," said Theo Hourmouzis, Senior Vice President, ANZ and ASEAN, Snowflake. "Organisations across A/NZ are looking to take their AI projects to the next level - from testing, to production, to ultimately providing business value. Today's innovations are focused on providing them with the easiest, most connected, and most trusted data platform to do so." Snowflake Openflow Unlocks Full Data Interoperability, Accelerating Data Movement for AI Innovation Snowflake unveiled Snowflake Openflow, a multi-modal data ingestion service that allows users to connect to virtually any data source and drive value from any data architecture. Now generally available on AWS, Openflow eliminates fragmented data stacks and manual labor by unifying various types of data and formats, enabling customers to rapidly deploy AI-powered innovations. Snowflake Openflow embraces open standards, so organisations can bring data integrations into a single, unified platform without vendor lock-in and with full support for architecture interoperability. Powered by Apache NiFi™[1], an Apache Software Foundation project built to automate the flow of data between systems, Snowflake Openflow enables data engineers to build custom connectors in minutes and run them seamlessly on Snowflake's managed platform. With Snowflake Openflow, users can harness their data across the entire end-to-end data lifecycle, while adapting to evolving data standards and business demands. Hundreds of ready-to-use connectors and processors simplify and rapidly accelerate data integration from a broad range of data sources including Box, Google Ads, Microsoft Dataverse, Microsoft SharePoint, Oracle, Proofpoint, ServiceNow, Workday, Zendesk, and more, to a wide array of destinations including cloud object stores and messaging platforms, not just Snowflake. Snowflake Unveils Next Wave of Compute Innovations For Faster, More Efficient Warehouses and AI-Driven Data Governance Snowflake announced the next evolution of compute innovations that deliver faster performance, enhanced usability, and stronger price-performance value -- raising the bar for modern data infrastructure. This includes Standard Warehouse - Generation 2 (Gen2) (now generally available), an enhanced version of Snowflake's virtual Standard Warehouse with next-generation hardware and additional enhancements to deliver 2.1x[2] faster analytics performance and 1.9x faster analytics performance than Managed Spark. Snowflake also introduced Snowflake Adaptive Compute (now in private preview), a new compute service that lowers the burden of resource management by maximising efficiency through automatic resource sizing and sharing. Warehouses created using Adaptive Compute, known as Adaptive Warehouses, accelerate performance for users without driving up costs, ultimately redefining data management in the evolving AI landscape. Snowflake Intelligence and Data Science Agent Deliver The Next Frontier of Data Agents for Enterprise AI and ML Snowflake announced Snowflake Intelligence (public preview soon), which enables technical and non-technical users alike to ask natural language questions and instantly uncover actionable insights from both structured tables and unstructured documents. Snowflake Intelligence is powered by state-of-the-art large language models from Anthropic and OpenAI, running inside the secure Snowflake perimeter, and is powered by Cortex Agents (public preview) under the hood -- all delivered through an intuitive, no-code interface that helps provide transparency and explainability. Snowflake also unveiled Data Science Agent (private preview soon), an agentic companion that boosts data scientists' productivity by automating routine ML model development tasks. Data Science Agent uses Anthropic's Claude to break down problems associated with ML workflows into distinct steps, such as data analysis, data preparation, feature engineering, and training. Today, over 5,200[3] customers from companies like BlackRock, Luminate, and Penske Logistics are using Snowflake Cortex AI to transform their businesses. Snowflake Introduces Cortex AISQL and SnowConvert AI: Analytics Rebuilt for the AI Era Snowflake announced major innovations that expand on Snowflake Cortex AI, Snowflake's suite of enterprise-grade AI capabilities, empowering global organisations to modernise their data analytics for today's AI landscape. This includes SnowConvert AI, an agentic automation solution that accelerates migrations from legacy platforms to Snowflake. With SnowConvert AI, data professionals can modernise their data infrastructure faster, more cost-effectively, and with less manual effort. Once data lands in Snowflake, Cortex AISQL (now in public preview) then brings generative AI directly into customers' query engines, enabling teams to extract insights across multi-modal data and build flexible AI pipelines using SQL -- all while providing bestinclass performance and cost efficiency. Snowflake Marketplace Adds Agentic Products and AI-Ready Data from Leading News, Research, and Market Data Providers Snowflake announced new agentic products on Snowflake Marketplace that accelerate agentic AI adoption across the enterprise. This includes Cortex Knowledge Extensions (generally available soon) on Snowflake Marketplace, which enables enterprises to enrich their AI apps and agents with proprietary unstructured data from third-party providers -- all while allowing providers to protect their intellectual property and ensure proper attribution. Users can tap into a selection of business articles and content from The Associated Press, which will help users further enhance the usefulness of results in their AI systems. In addition, Snowflake unveiled sharing of Semantic Models (now in private preview), which allows users to easily integrate AI-ready structured data within their Snowflake Cortex AI apps and agents -- both from internal teams or third-party providers like CARTO, CB Insights, Cotality™ powered by Bobsled, Deutsche Börse, IPinfo, and truestar. Learn More: About Snowflake Snowflake is the platform for the AI era, making it easy for enterprises to innovate faster and get more value from data. More than 11,000 companies around the globe, including hundreds of the world's largest, use Snowflake's AI Data Cloud to build, use, and share data, apps and AI. With Snowflake, data and AI are transformative for everyone. Learn more at snowflake.com (NYSE: SNOW).

Snowflake Intelligence and Data Science Agent Deliver The Next Frontier of Data Agents for Enterprise AI and ML

Snowflake (NYSE: SNOW), the AI Data Cloud company, today announced at its annual user conference, Snowflake Summit 2025, new agentic AI innovations that bridge the gap between enterprise data and business action, making AI and ML workflows easy, connected, and trusted for technical and non-technical users alike. This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20250603071459/en/ Snowflake Intelligence (public preview soon) offers business users and data professionals a unified conversational experience -- powered by intelligent data agents -- to ask natural language questions and instantly uncover actionable insights from both structured tables and unstructured documents. Snowflake is also unveiling Data Science Agent (private preview soon), an agentic companion that boosts data scientists' productivity by automating routine ML model development tasks. These innovations enable users to simplify their AI and ML workflows, democratize access to data across their businesses, and eliminate the technical overhead that slows down business decision-making -- all through natural language interactions within Snowflake. "AI agents are a major leap from traditional automation or chatbots, but in order to deploy them at scale, businesses need an AI-ready information ecosystem. This means enterprises must be able to unite data silos, maintain enterprise-grade security and compliance, and have easy ways to adopt and build agents," said Baris Gultekin, Head of AI, Snowflake. "Snowflake Intelligence breaks down these barriers by democratizing the ability to extract meaningful intelligence from an organization's entire enterprise data estate -- structured and unstructured data alike. This isn't just about accessing data, it's about empowering every employee to make faster, smarter decisions with all of their business context at their fingertips." "At WHOOP, our mission is to unlock human performance and healthspan, and data is central to everything we do. Snowflake Intelligence marks a big step forward in our ability to be a data-first organization, ensuring that all employees can access insights without relying on analytics teams as the intermediary," said Matt Luizzi, Sr. Director of Business Analytics, WHOOP. "By eliminating the technical barriers to gleaning the insights we need for decision-making, our analytics teams can now shift from manual data retrieval tasks to more strategic, predictive, and value-generating work." Snowflake Intelligence Reimagines Business Intelligence, Without the Overhead Today, organizations are plagued by inefficient decision-making due to disjointed data governance, silos between data formats, and a shortage of technical data analysts who can code and synthesize information across the business. Snowflake Intelligence eliminates these operational challenges, allowing non-technical users and business teams to have conversations with their enterprise data in natural language -- all without writing a single line of code. Running directly inside organizations' existing Snowflake environment, Snowflake Intelligence inherits all security controls, data masking, and governance policies automatically. It unifies data across sources including Snowflake, Box, Google Drive, Salesforce Data Cloud via Zero Copy, Workday, Zendesk, and more, using the new Snowflake Openflow to bring together insights from spreadsheets, documents, images, and databases simultaneously. By leveraging natural language prompts, the data agents powering Snowflake Intelligence can generate visualizations and assist users in taking action on insights. From analyzing business metrics to looking up helpful internal knowledge, Snowflake Intelligence enables every employee to easily access and harness the full potential of their company's data. Snowflake Intelligence can also access third-party knowledge through Cortex Knowledge Extensions (generally available soon) on Snowflake Marketplace, and incorporate expert content from Packt, Stack Overflow, the USA TODAY Network, and more to further contextualize and enrich responses. Snowflake Intelligence is powered by state-of-the-art large language models from Anthropic and OpenAI, running inside the Snowflake perimeter, and is powered by Cortex Agents (generally available soon) under the hood -- all delivered through an intuitive, no-code interface that helps provide transparency and explainability. "By integrating Claude's reasoning capabilities directly into Snowflake's platform, we're further eliminating the traditional barriers between data and insights. Business users can now have natural conversations with their enterprise data, while data scientists can automate complex ML workflows -- all through simple natural language interactions," said Michael Gerstenhaber, VP, Product Management, Anthropic. "This demonstrates how Claude's advanced reasoning can democratize AI while maintaining the enterprise-grade security and governance that organizations require." Data Science Agent Automates Tedious ML Tasks, Saving Hours of Manual Work Data scientists spend lengthy cycles on developing and troubleshooting their ML workflows, leading to operational bottlenecks and fewer ML models making their way to production. Now, Snowflake is bringing agentic AI to ML workflows with Data Science Agent to boost productivity for ML teams by slashing hours of manual work. Data Science Agent uses Anthropic's Claude to break down problems associated with ML workflows into distinct steps, such as data analysis, data preparation, feature engineering, and training. Combining advanced techniques such as multi-step reasoning, contextual understanding, and action execution, Data Science Agent provides verified solutions in the form of fully functional ML pipelines that can be easily executed from a Snowflake Notebook. With suggested improvements, or with user provided follow-ups, Data Science Agent helps users easily iterate to the next-best version. By automating this tedious work, data science teams save hours of time that they would typically spend on experimentation or debugging -- and can instead focus on higher-impact initiatives. Snowflake Accelerates Enterprise AI Adoption for More Than 5,200 Customers Today, over 5,200¹ customers from companies like BlackRock, Luminate, and Penske Logistics are using Snowflake Cortex AI to transform their businesses. To further empower users to harness the power of AI, Snowflake is also announcing new innovations in AI building blocks for advanced conversational apps, unstructured data analytics, and ML. Teams can explore and analyze multi-modal data at scale with enhanced document processing, batch semantic search, and the new Cortex AISQL (now in public preview) to bridge the gap between data analysts and AI engineering skills. This press release contains express and implied forward-looking statements, including statements regarding (i) Snowflake's business strategy, (ii) Snowflake's products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, and (iv) the integration, interoperability, and availability of Snowflake's products with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under the heading "Risk Factors" and elsewhere in the Quarterly Reports on Form 10-Q and the Annual Reports on Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future events. © 2025 Snowflake Inc. All rights reserved. Snowflake, the Snowflake logo, and all other Snowflake product, feature and service names mentioned herein are registered trademarks or trademarks of Snowflake Inc. in the United States and other countries. All other brand names or logos mentioned or used herein are for identification purposes only and may be the trademarks of their respective holder(s). Snowflake may not be associated with, or be sponsored or endorsed by, any such holder(s). About Snowflake Snowflake is the platform for the AI era, making it easy for enterprises to innovate faster and get more value from data. More than 11,000 companies around the globe, including hundreds of the world's largest, use Snowflake's AI Data Cloud to build, use, and share data, apps and AI. With Snowflake, data and AI are transformative for everyone. Learn more at snowflake.com (NYSE: SNOW).

Snowflake Introduces Cortex AISQL and SnowConvert AI: Analytics Rebuilt for the AI Era

Snowflake (NYSE: SNOW), the AI Data Cloud company, today announced at its annual user conference, Snowflake Summit 2025, major innovations that expand on Snowflake Cortex AI, Snowflake's suite of enterprise-grade AI capabilities, empowering global organizations to modernize their data analytics for today's AI landscape. Snowflake is unveiling SnowConvert AI, an agentic automation solution that accelerates migrations from legacy platforms to Snowflake. With SnowConvert AI, data professionals can modernize their data infrastructure faster, more cost-effectively, and with less manual effort. Once data lands in Snowflake, Cortex AISQL (now in public preview) then brings generative AI directly into customers' queries, enabling teams to extract insights across multi-modal data and build flexible pipelines using SQL and AI -- all while providing best‑in‑class performance and cost efficiency. This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20250603908179/en/ "Every organization recognizes the potential of AI. But too often, harnessing AI means overcoming complex infrastructure, performance limitations, high costs, and a reliance on engineers to build custom pipelines," said Carl Perry, Head of Analytics, Snowflake. "We're removing those barriers, whether it's enabling anyone to analyze and act on all their data with Cortex AISQL or accelerating migrations off legacy systems through SnowConvert AI. By empowering teams to move faster, work smarter, and turn data into real impact, we're reimagining analytics for the AI era." "In capital markets, speed and precision are everything. For years, SQL has been the gold standard for transforming data -- and now, with Cortex AISQL, we're extending that power to unstructured text," said Thomas Bodenski, Chief Operating Officer, TS Imagine. "With AISQL, our teams can analyze documents, extract insights, and build intelligence directly in the language they already know -- all without complex engineering workflows. It's a game-changer for how fast we can respond to markets and deliver value to clients, while leveraging the Snowflake architecture for high performing SQL processing." Snowflake Provides Generative AI-Powered SQL Functions to Help Analysts Do More, Faster Snowflake helps customers leverage generative AI through familiar SQL queries, redefining how organizations analyze data. Snowflake Cortex AI has already enabled customers to build and deploy advanced AI models, apps, and agents within the security perimeter of the AI Data Cloud. Now, Cortex AISQL marks the next leap forward by leveraging generative AI to create powerful new query capabilities, effectively turning every data analyst into an AI engineer. Cortex AISQL is powered by leading models from Anthropic, Meta, Mistral, OpenAI, and others, coupled with core functionality and performance optimizations already built directly into Snowflake's SQL engine. With Cortex AISQL performance optimizations (now in private preview), enterprises now gain performance improvements ranging from 30-70% depending on datasets, with up to 60% cost savings when filtering or joining data across thousands of records, empowering them to scale their analytics capabilities for strategic decision-making. Traditionally, SQL has been limited to structured and semi-structured data, leaving analysts dependent on developers to access insights from unstructured sources. Cortex AISQL changes this by enabling teams to query all data types -- from traditional rows and columns of numbers to text, images, audio, and more -- all while using SQL, the universal language they already know and love for managing data. By harnessing SQL with AI-powered functions in Snowflake, analysts can now readily access and analyze multi-modal data at scale, eliminating data silos, consolidating tools, and combining traditional structured data with unstructured sources. This includes enriching customer tables with chat transcripts, correlating sensor readings with inspection photos, and merging sales figures with social media sentiment -- enabling analysts to classify images, extract insights from call transcripts, and detect anomalies with ease. The result is a fully integrated SQL experience across all data, unlocking deeper insights, faster decisions, and accelerated innovation without the need for specialized AI skills or external services. Cortex AISQL delivers unified intelligence across the entire organization. Snowflake Accelerates Time to Value with AI-Powered Migrations With SnowConvert AI, Snowflake is making one of the most tedious and time-consuming parts of digital transformation -- data warehouse migrations, business intelligence (BI) migrations, and ETL migrations -- faster and smarter, without introducing additional risk factors. With data ecosystem migration agents powered by Snowflake Cortex AI, SnowConvert AI enables organizations to quickly and easily move from legacy data warehouses like Oracle, Teradata, Google BigQuery, and other cloud data platforms to Snowflake. By automating the conversion of code, BI reports, ETL tools, and validating the converted code and migrated data efficiently, SnowConvert AI streamlines the migration process for data engineers. SnowConvert AI goes beyond just database migrations, allowing customers to migrate their entire data ecosystem, while staying seamlessly supported and without disrupting critical workflows -- ultimately reducing risk, costs, and complexity at every step. SnowConvert AI makes the code conversion and testing phases 2-3 times faster, significantly reducing time-to-delivery and speeding up modernization efforts. By accelerating the path to modernization, Snowflake shortens the time between intention and insight, so organizations can minimize migration timelines and start generating insights that drive results sooner. Global Enterprises Unlock Comprehensive Analytics Across All Data Snowflake's unified approach to analytics represents a fundamental shift in how organizations analyze their data. Snowflake is also unveiling innovations to Snowflake's unified platform that enables enterprises to process all their data, including open formats like Apache Iceberg™ tables, with remarkable efficiency. In addition, Snowflake announced Standard Warehouse - Generation 2 (now generally available) with next-generation hardware and software optimizations to deliver 2.1x faster analytics performance and 1.9x faster analytics performance than Managed Spark. Snowflake customers can now unlock faster AI-powered insights from all their data -- wherever it lives. Learn More: "Apache Iceberg" is a registered trademark or trademark of the Apache Software Foundation in the United States and/or other countries. Snowflake improvements based on performance of core analytics workloads measured as of May 3, 2024 using Standard Warehouse and May 2, 2025 using Gen2. Performance results based on core analytics workloads on 2XL Gen2 warehouse and comparable warehouse on Managed Spark as of May 2, 2025. Forward Looking Statements This press release contains express and implied forward-looking statements, including statements regarding (i) Snowflake's business strategy, (ii) Snowflake's products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, and (iv) the integration, interoperability, and availability of Snowflake's products with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under the heading "Risk Factors" and elsewhere in the Quarterly Reports on Form 10-Q and the Annual Reports on Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future events. © 2025 Snowflake Inc. All rights reserved. Snowflake, the Snowflake logo, and all other Snowflake product, feature and service names mentioned herein are registered trademarks or trademarks of Snowflake Inc. in the United States and other countries. All other brand names or logos mentioned or used herein are for identification purposes only and may be the trademarks of their respective holder(s). Snowflake may not be associated with, or be sponsored or endorsed by, any such holder(s). About Snowflake Snowflake is the platform for the AI era, making it easy for enterprises to innovate faster and get more value from data. More than 11,000 companies around the globe, including hundreds of the world's largest, use Snowflake's AI Data Cloud to build, use, and share data, apps and AI. With Snowflake, data and AI are transformative for everyone. Learn more at snowflake.com (NYSE: SNOW).

Snowflake's new AI agents aim to democratize data analytics: Here's how

Managers of data warehouses of big and small companies realise this sooner or later, that having vast tables of numbers and text means almost nothing if you can't turn them into something useful. If businesses can't use data to derive actionable insight, everything from efficiency to success is undermined in the long run. In the age of AI, Snowflake wants to fix that with new solutions that include a brand new AI agent. At Snowflake Summit 2025, the company unveiled a pair of AI agents designed to help close the gap between dumb data and intelligent insight, with the launch of Snowflake Intelligence (for business users) and the Data Science Agent (for data scientists). Also read: AI agents explained: Why OpenAI, Google and Microsoft are building smarter AI agents Rather than waiting weeks for an analyst to crank out a SQL query or an engineer to cobble together a pipeline, these agents let people simply ask questions in plain English (or prompt for modeling tasks) and get back not just insights, but actual workflows that run inside Snowflake. It's a departure from the usual "store-and-query" approach, and for once, the hype felt deserved. Whether you're a marketing manager, a salesperson, or even a finance lead, most of the time the only way to tap into the treasure trove of data locked away in your company's data silo was to politely beg an analyst or a BI engineer. However, Snowflake Intelligence claims to offer a chat-like interface (think a stripped-down ChatGPT chatbot interface) that lives natively inside your Snowflake account. You type something like: "Show me last quarter's top-performing products in the West region and tell me why product X outpaced product Y." Behind the scenes, here's what happens: In other words, you're not waiting on a SQL jockey. You're getting near-instant answers - and even suggestions for next steps ("Email the West-region team to investigate supply chain issues"). What's more, if you want to trigger a workflow - say, auto-create a Slack message or update a CRM record - the agent does that, too. Also read: Snowflake's Baris Gultekin believes AI agents are critical for future of work Christian Kleinerman, Snowflake's EVP of Product, summed it up neatly: "Intelligence is truly no code. It's a series of prompts, orchestration, and instructions. I do think that it'll expand to an audience way broader than just data teams. It'll go to lines of business and business users at large." Snowflake Intelligence taps into any data you already have in Snowflake (structured tables) or connected via Openflow (think Google Drive, Zendesk, Box, or 3rd-party datasets like AP News). Crucially, it all runs under your existing Snowflake security and governance rules, so nothing leaks out. You see an answer, you see the lineage - "Here's exactly which table, which column, which row" - and compliance teams can breathe easy. That's an example of data democracy with guardrails if I've ever seen one. If you're a data scientist, you've likely spent hours wrestling with Jupyter notebooks, writing SQL to extract features, gluing together Python code for preprocessing, then shoving it through a framework like scikit-learn or XGBoost. Data Science Agent aims to turn that tedious task into a simplified and convenient conversation. Imagine typing: "Build a churn-prediction model using the last 12 months of billing and usage data." Here's what happens under the hood: You don't have to juggle multiple tools. No more exporting CSVs to a local environment, no more cutting and pasting code scraps. Everything - data prep, feature store integration, training, and deployment - lives inside Snowflake's container runtime. Also read: Claude 4 Explained: Anthropic's Thoughtful AI, Opus and Sonnet And because Snowflake handles GPU orchestration, experiment tracking, and model serving (yes, even a Hugging Face endpoint if you like), your pipeline is reproducible and governed by the same security policies that protect your data warehouse. "I think of low code, no code as enabling a much, much broader base of people to be able to do pretty meaningful things," said Sridhar Ramaswamy, Snowflake's CEO, offering a useful analogy. "Like blogs and the Internet had on publishing: you still value amazing writers, but anyone can now post. Similarly, with Snowflake's AI agents, a business user doesn't have to write SQL or wait for a BI team. Their work becomes faster and more impactful, while the programmers - our equivalent of star authors - remain vital for deeper technical work." At its core, Snowflake's AI agents solve two key problems: Insights for anyone: Not every organization has a line of SQL wizards or a platoon of data engineers. Giving non-technical stakeholders (marketing managers, finance directors, operations leads) a conversational interface means they can explore their own data. No more tickets, no more backlog, no more "that query will be ready by next sprint." Accelerating ML workflows: Data scientists can't pump out predictive models if they're stuck on data munging seven days a week. By auto-generating production pipelines that run entirely within Snowflake, teams can shift focus to validating hypotheses, fine-tuning algorithms, and building higher-order features. Governance and lineage are baked in, eliminating security headaches. Snowflake's AI agents are signalling a genuine shift that's AI is bringing inside how businesses operate: transitioning from passive dashboards to active, conversational data workflows; from one-off model prototypes to repeatable, governed ML pipelines. For companies that have spent years wrangling data silos, this is the closest we've gotten to "data as code," where the barrier between human intent and machine execution becomes almost invisible. If you've ever stared at an Excel pivot table, wondered how to connect PDF reports to your sales tables, these agents could change that. It's not just about faster answers. Ultimately, it's all about unlocking the intelligence buried in enterprise data and actually acting on it. Which is what Snowflake is trying to do.