{"id":455,"date":"2026-01-02T20:16:46","date_gmt":"2026-01-02T20:16:46","guid":{"rendered":"https:\/\/buildconsole.com\/blog\/swiggy-launches-hermes-v3-boosting-text-to-sql-conversational-ai\/"},"modified":"2026-01-02T20:16:46","modified_gmt":"2026-01-02T20:16:46","slug":"swiggy-launches-hermes-v3-boosting-text-to-sql-conversational-ai","status":"publish","type":"post","link":"https:\/\/buildconsole.com\/blog\/swiggy-launches-hermes-v3-boosting-text-to-sql-conversational-ai\/","title":{"rendered":"Swiggy Launches Hermes V3: Boosting Text\u2011to\u2011SQL &amp; Conversational AI"},"content":{"rendered":"<p>Swiggy, the Indian food\u2011delivery platform, announced the release of Hermes V3 on Tuesday, a generative\u2011AI driven assistant that lets employees ask data questions in plain English and receive SQL queries in return. The system is integrated directly into Slack, the company\u2019s primary internal communication channel, and is designed to improve the accuracy of generated SQL and support multi\u2011turn analytical conversations.<\/p>\n<h2>Background of the Hermes Series<\/h2>\n<p>Hermes began as an internal project aimed at simplifying data access for Swiggy\u2019s workforce. The first version, Hermes V1, was a prototype that demonstrated the feasibility of converting natural\u2011language questions into SQL statements. In 2023, Swiggy rolled out Hermes V2, which added basic conversational memory and a limited set of data sources. The new iteration, Hermes V3, builds on that foundation by incorporating advanced generative\u2011AI capabilities and a more robust architecture that supports complex, multi\u2011step queries.<\/p>\n<h2>Technical Features of Hermes V3<\/h2>\n<h4>Vector Retrieval for Contextual Relevance<\/h4>\n<p>Hermes V3 uses vector retrieval to locate the most relevant data segments before generating a query. This approach allows the assistant to consider the semantic meaning of a user\u2019s question, rather than relying solely on keyword matching. By embedding data into high\u2011dimensional vectors, the system can quickly identify related tables and columns, improving the relevance of the generated SQL.<\/p>\n<h4>Conversational Memory for Multi\u2011Turn Interaction<\/h4>\n<p>The tool maintains a short\u2011term memory of the conversation, enabling it to handle follow\u2011up questions that refer back to earlier parts of the dialogue. This feature is essential for analytical tasks that require iterative refinement, such as narrowing a dataset by adding filters or aggregating results across multiple dimensions.<\/p>\n<h4>Agentic Orchestration for Complex Query Construction<\/h4>\n<p>Hermes V3 employs an agentic orchestration layer that decomposes a user\u2019s request into smaller sub\u2011tasks. Each sub\u2011task is handled by a specialized component that may involve data retrieval, transformation, or validation. The orchestrator then stitches the results together into a single, coherent SQL statement. This modular approach reduces the likelihood of errors and improves the overall reliability of the output.<\/p>\n<h4>Explainability for Transparency<\/h4>\n<p>To address concerns about black\u2011box AI, the assistant provides an explanation of the logic behind each generated query. Users can view the reasoning steps that led to the final SQL, which helps build trust and facilitates debugging when the results do not match expectations.<\/p>\n<h2>Implications for Swiggy\u2019s Workforce<\/h2>\n<p>By allowing employees to query data without writing SQL, Hermes V3 is expected to lower the barrier to data access across departments. Analysts, product managers, and marketing teams can retrieve insights more quickly, potentially reducing the time required for data\u2011driven decision making. The system\u2019s integration with Slack also means that users can perform these tasks within the context of their existing workflow, minimizing context switching.<\/p>\n<p>Swiggy\u2019s engineering team has indicated that the tool is currently in a pilot phase, available to a limited group of users. Feedback from this group will inform further refinements before a broader rollout. The company has not yet disclosed a public release date, but internal documentation suggests that a company\u2011wide deployment could occur within the next six months.<\/p>\n<h2>Future Outlook<\/h2>\n<p>Hermes V3 represents a step forward in Swiggy\u2019s broader AI strategy, which includes initiatives in recommendation engines, fraud detection, and supply\u2011chain optimization. The company\u2019s focus on generative AI for internal tooling aligns with industry trends that emphasize democratizing data access and accelerating analytics. While the tool is currently tailored to Swiggy\u2019s internal data ecosystem, the underlying architecture could be adapted for use by other enterprises seeking to streamline data queries.<\/p>\n<p>As the platform matures, Swiggy may expand Hermes V3\u2019s capabilities to support additional data sources, such as external market feeds or third\u2011party APIs. The company may also explore integrating the assistant with other collaboration tools beyond Slack, broadening its reach within the organization. For now, the immediate priority remains refining the user experience, ensuring query accuracy, and gathering user feedback to guide future enhancements.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Swiggy, the Indian food\u2011delivery platform, announced the release of Hermes V3 on Tuesday, a generative\u2011AI driven assistant that lets employees ask data questions in plain English and receive SQL queries in return. The system is integrated directly into Slack, the company\u2019s primary internal communication channel, and is designed to improve the accuracy of generated SQL [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":456,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[127],"tags":[224,355,358,356,357],"class_list":["post-455","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-dev-news","tag-ai","tag-swiggy","tag-conversationalai","tag-hermesv3","tag-texttosql"],"_links":{"self":[{"href":"https:\/\/buildconsole.com\/blog\/wp-json\/wp\/v2\/posts\/455","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/buildconsole.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/buildconsole.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/buildconsole.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/buildconsole.com\/blog\/wp-json\/wp\/v2\/comments?post=455"}],"version-history":[{"count":0,"href":"https:\/\/buildconsole.com\/blog\/wp-json\/wp\/v2\/posts\/455\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/buildconsole.com\/blog\/wp-json\/wp\/v2\/media\/456"}],"wp:attachment":[{"href":"https:\/\/buildconsole.com\/blog\/wp-json\/wp\/v2\/media?parent=455"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/buildconsole.com\/blog\/wp-json\/wp\/v2\/categories?post=455"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/buildconsole.com\/blog\/wp-json\/wp\/v2\/tags?post=455"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}