Natual Language Querying for Catalog Data

Designed and implemented AI-powered natural language querying capabilities for the enterprise catalog system, enabling business users and developers to interact with catalog data using conversational language.

The solution leverages OpenAI, Claude, and Snowflake language models to interpret user queries and translate them into structured database queries. This allows users to ask complex questions about catalog data without needing to understand the underlying data schema or write SQL or structure API calls. The integration has significantly improved accessibility and usability of catalog data, empowering teams to make data-driven decisions more efficiently and limit support request to the development team to generate more complex queries.