Hermes: Swiggy's in-house Text-to-SQL solution enables natural language data queries in Slack
Swiggy teams needing specific data had to either find an existing dashboard, write SQL themselves (requiring table knowledge, access, and SQL skills), or submit an analyst request — all taking minutes to days — causing important questions to go unasked or to be answered with proxy or incorrect information.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits question in Slack
Users type natural language prompts in Slack and receive the final SQL query and its output as a response.
Hundreds of users across Swiggy answer several thousand queries with an average turnaround time of less than 2 minutes, and the first-shot acceptance rate for generated SQL increased dramatically after the V2 launch.
What failed first
The V1 implementation using GPT 3.5 variants with a kitchen-sink approach — treating all business needs and data as the same — did not perform well against the complexity and volume of Swiggy's tables, columns, and business-specific context.