Bridge the gap between natural language and database insights with AI-driven query generation.
NL2Query is a sophisticated Natural Language to Query (NL2Q) engine designed to democratize data access across organizations. Utilizing a multi-model LLM architecture (incorporating GPT-4o, Claude 3.5 Sonnet, and proprietary fine-tuned Llama-3 variants), it translates complex human language into optimized SQL, NoSQL, and GraphQL queries. In the 2026 market landscape, NL2Query distinguishes itself through 'Semantic Fabric' technology, which understands organization-specific jargon and historical query patterns to improve accuracy over time. The platform prioritizes data privacy by utilizing a metadata-only schema ingestion process, ensuring that sensitive PII never leaves the client's infrastructure. It features a robust semantic layer that maps business logic to physical data schemas, allowing non-technical users to perform deep-dive analytics without writing a single line of code. Architecturally, it is designed for low-latency execution, often returning complex joins and aggregations in under 400ms. By integrating directly into existing BI tools and offering a headless API-first approach, NL2Query serves as the connective tissue between raw data lakes and actionable executive insights, reducing the burden on data engineering teams by up to 70%.
Maps human-readable business terms to complex database column names and joins automatically.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Analyzes table structures and relationships without ever reading the row-level data.
Supports PostgreSQL, MySQL, MongoDB, BigQuery, and Snowflake within the same interface.
Provides a step-by-step logic breakdown of why a specific SQL query was generated.
Uses graph theory to find the most efficient join path between disparate tables.
The system runs a 'dry-run' of the query and self-fixes syntax errors before delivery.
Allows enterprises to fine-tune the underlying model on their specific SQL history.
Executives need instant answers on regional sales performance without waiting for a data analyst.
Registry Updated:2/7/2026
Support agents need to find specific user transaction logs in NoSQL databases.
Developers need to create data endpoints quickly without manual SQL coding.