Snowflake has announced that Swiggy, the Indian food delivery platform, is now utilizing its AI Data Cloud as a unified data foundation. This integration allows Swiggy to democratize access to insights across its marketing and operations teams. By leveraging Snowflake’s capabilities, Swiggy can respond to customer needs in real time, thereby accelerating business execution on a larger scale.
This integrated foundation enhanced Swiggy’s slowest data workflows by 90 to 96%, the companies claim. Additionally, the company improved its heaviest queries from two hours to 15 minutes and accelerated data processing from six hours to near real-time.
With Snowflake, we are making trusted, governed insights easier to access while ensuring that every user and AI agent operates within the same permissions and audit framework,” said Swiggy CTO Madhusudhan Rao. As Swiggy rapidly scaled to serve millions of customers across food delivery, Instamart and Dineout, getting insights from fragmented data became a challenge. The company required a unified serving layer to support peak workloads smoothly. Swiggy also sought to empower its teams with easy-to-use, self-service access to insights to reduce dependency on central data teams. “At Swiggy, data is valuable only when it reaches the person who can act on it, whether that is a city sales manager, restaurant owner or delivery partner. By establishing a central analytical layer on Snowflake using Apache Iceberg, Swiggy has transformed its data ecosystem. “India’s fastest-moving companies can’t afford to route every data question through a central team. With Snowflake, Swiggy has built a centralised, governed data foundation, consistent controls, and an AI framework that holds agents to the same standards as people. This approach is helping Swiggy create a path toward more capable and accountable AI agents,” added Vijayant Rai, Managing Director- India, Snowflake. These performance gains have accelerated Swiggy’s daily execution across departments. Marketing teams now build and launch targeted campaigns directly within their own tools. Product engineering teams rely on standardised metric definitions to evaluate experimental features before release, using an in-house platform running directly on Snowflake. Operational teams monitor service quality metrics in real time, helping proactively improve delivery logistics. At the same time, finance teams gain workload-level visibility into technology expenses. Throughout the business, security features like role-based access, column masking, and row-level security enable both internal teams and external partners to interact with data safely using masked data. Snowflake’s governance framework further helps ensure that AI-powered agents and applications inherit the same permission boundaries, short-lived credentials, and audit trails required of human employees. Get the latest technology news and updates. Download the TOI App.