Snowflake expands its technical and mainstream AI platforms
Snowflake is expanding its Snowflake Intelligence and Cortex Code offerings in the hope of bringing users deploying and developing artificial intelligence inside the Snowflake portfolio. Snowflake Intelligence is framed as a tool for generalised business users, while Cortex Code is destined for developers and technical teams’ desks. A press release from the company lists additional […] The post Snowflake expands its technical and mainstream AI platforms appeared first on AI News .
Snowflake is expanding its Snowflake Intelligence and Cortex Code offerings in the hope of bringing users deploying and developing artificial intelligence inside the Snowflake portfolio. Snowflake Intelligence is framed as a tool for generalised business users, while Cortex Code is destined for developers and technical teams’ desks. A press release from the company lists additional features on both platforms, including an increase in the number of integrations they have with third-party software. It also details new automation features and simpler, web-based methods of building agentic AI workflows. White collar and beyond Snowflake Intelligence, aimed at non-technical staff, is among the platforms on the market today that advertise an ability to execute tasks inside existing business workflows. Users can describe to the LLM what they’d like to see happen in natural language, and it execute given tasks. Snowflake lists preparing presentations, running multi-step analyses, and sending follow-up messages as some of the uses it envisages. Data can be drawn from an organisation’s internal and linked digital assets, including structured and unstructured data, with external sourcese connected by various protocols and pre-built connectors. User queries and ensuing workflows will be carefully limited in terms of access permissions and organisational governance, helping to prevent incidents of data loss and non-compliance. New interfaces using MCP (Model Context Protocol) are available, and the company has named the Google business suite, Jira, and Salesforce (including Slack) as among the systems Snowflake Intelligence can now interface with. Also in the works is an iOS app for Snowflake Intelligence which will enter public preview “soon”. Snowflake says its Intelligence platform becomes more personalised over time, learning from user behaviour. Users will be able to save and share workflows so that work can be reused. Longer context windows – personalisation – mean that users addressing the large language model should not have to repeat long, contextualised prompts to get the results they want. The updates have come about as a result of feedback from Project SnowWork, a research project launched last month to showcase the platform and garner users’ preferences as to what features they’d like to see from an AI platform. Snowflake in the development toolkit Cortex Code is designed for software development teams in the enterprise, an area in which AI algorithms can prove successful at lower level tasks. Cortex Code is described in company press release as a coding and orchestration “layer” with new options for integration with external data sources, now supporting AWS Glue, Databricks, and Postgres. Cortex Code can also connect to other language models via MCP and ACP (agent communication protocol), the more commerce-driven protocol that emerged around the same time as the Anthropic-stewarded MCP. VS Code users will soon see Cortex Code as an extension (it’s currently in private preview), and a Snowflake plugin for Claude Code is currently under development. Snowflake’s Agent Software Development Kit for Python and TypeScript is available, so teams can embed Cortex Code functions in their own applications. Cloud Agents, also in private preview, are to appear in Snowsight, Snowflake’s browser-based interface. Plan Mode lets users preview and approve workflows before AI execution, and the company is working on a facility by which end-users can see detail of longer research processes the LLM undertakes to vet the veracity of its processes. Snowflake says more than 9,100 customers use its AI products weekly. Since its launch six months ago, Snowflake says more than half of its customers are using Snowflake Intelligence and Cortex Code. The company’s dual-pronged approach – mainstream business users and software development teams – doubles down on the company’s core technical market, but widens its the platform’s adoption among general business function users. The new software connectors, mobile app, and browser-based options will create a broader market of users, and the additional support for existing systems will widen its appeal among enterprises with embedded workflows and software platforms. Sameer Vuyyuru, chief AI and product officer at Capita, said: “Snowflake helps us deploy AI securely and with the right governance across highly regulated, citizen-facing services where performance, compliance and trust are critical.” (Image source: “The snow” by telafree is licensed under CC BY-NC 2.0.) Want to learn more about AI and big data from industry leaders? 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Key takeaways
- Democratizing access to AI could accelerate the adoption of technological solutions in Brazil.
- Integrations with popular systems facilitate the acceptance and use of AI tools in companies.
- Personalization and adaptation of platforms based on user behavior are growing trends.
Editorial analysis
The expansion of the Snowflake Intelligence and Cortex Code platforms represents a significant strategic move for Snowflake, especially in a Brazilian market that increasingly seeks to integrate artificial intelligence into its operations. The proposal to democratize access to AI, allowing non-technical users to utilize advanced tools through intuitive interfaces, could accelerate the adoption of AI solutions across various sectors. This is particularly relevant in a country where digital transformation still faces barriers, such as a lack of technical training in some areas.
Moreover, the integration with widely used systems like Google Workspace, Jira, and Salesforce indicates a concern for making the user experience more seamless and integrated into the existing work ecosystem within organizations. This approach could facilitate the acceptance and use of AI tools, as employees will not need to drastically change their workflows. For Brazil, where many companies are still adapting to the use of data and digital tools, this ease of use could be a game changer.
The continuous development of features, such as the personalization of Snowflake Intelligence based on user behavior, suggests a growing trend towards AI solutions that learn and adapt over time. This not only improves efficiency but can also enhance user satisfaction, a crucial aspect for the adoption of new technologies. The Project SnowWork initiative, which seeks direct feedback from users, also indicates that Snowflake is committed to aligning its offerings with the real needs of the market.
Finally, the introduction of an iOS app for Snowflake Intelligence could further broaden the platform's reach, allowing users to access its functionalities from anywhere. This is especially relevant in a remote and hybrid work scenario, which has become common in Brazil and worldwide. Monitoring how these innovations will be received and implemented in Brazilian companies will be crucial for understanding the real impact of Snowflake on the local technology and AI market.
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