Text2SQL.ai provides an LLM-powered SQL generator that converts natural language questions into optimized, ready-to-use SQL queries. The platform includes a schema-aware assistant, a public API for embedding SQL generation into other tools, and conversational follow-up capabilities. It has been adopted by companies like SimplyFleet to power in-app, self-serve reporting for end users.
Funding
Funding not disclosed
Founders
Product
Problem
Writing SQL queries requires technical expertise and significant time, especially for non-developers who need data insights. Teams often depend on data analysts or spend hours crafting and debugging complex queries, which delays decision-making and creates bottlenecks in accessing critical business information.
Solution
Text2SQL.ai offers an AI-powered SQL generator that translates plain English questions into accurate, optimized SQL queries within seconds. Users can add their database schema—including tables, columns, and relationships—so the AI produces queries that precisely match their data structure and are ready to use without manual edits. The platform supports follow-up questions, allowing users to refine results conversationally until satisfied. A public API enables developers to integrate SQL generation directly into their own applications, powering custom reporting tools and conversational interfaces.
Target Audience
Primary users are software developers, product teams, and non-technical business users who need to generate SQL queries quickly, as well as SaaS companies looking to embed conversational reporting capabilities into their platforms.
Features
- Natural language to SQL conversion with support for complex queries and follow-up refinement
- Schema integration that ingests database tables, columns, and relationships for context-aware query generation
- Public API for embedding SQL generation into third-party tools and custom applications
- Fast response times, delivering accurate SQL within seconds
- Conversational interface that allows iterative question-asking until the desired result is achieved
- Optimized query output designed to match the specific database design, reducing manual corrections