Lepton AI
Build and deploy high-performance AI applications at scale with zero infrastructure management.
Turn natural language into production-ready SQL and real-time data visualizations via an API-first semantic layer.
NLQ Engine is an enterprise-grade middleware architecture designed to bridge the gap between non-technical stakeholders and complex relational databases. In the 2026 data landscape, it operates as a sophisticated semantic translation layer that converts high-intent natural language into optimized SQL across diverse dialects including PostgreSQL, Snowflake, and BigQuery. Unlike generic LLMs, NLQ Engine utilizes a RAG-based approach to ingest schema metadata, constraints, and business logic without exposing the underlying raw data to external servers, ensuring SOC2 compliance. Its technical architecture features a multi-step verification process where generated queries are validated for performance bottlenecks and syntax errors before execution. This makes it an essential tool for SaaS developers looking to embed 'Ask Your Data' features into their applications, providing a robust, scalable alternative to building custom NLU-to-SQL pipelines from scratch. By 2026, it has expanded to include multi-turn conversational memory, allowing users to refine complex data exploration through iterative dialogue.
Automatically infers relationships between tables using foreign key analysis and column name embedding without manual tagging.
Build and deploy high-performance AI applications at scale with zero infrastructure management.
The search foundation for multimodal AI and RAG applications.
Accelerating the journey from frontier AI research to hardware-optimized production scale.
The Enterprise-Grade RAG Pipeline for Seamless Unstructured Data Synchronization.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Generates dialect-specific code for over 15 database engines, including specific window functions for Snowflake and BigQuery.
Allows developers to inject runtime context (like user_id or geo_location) into the NLQ prompt to filter results automatically.
A feedback UI component that lets experts correct generated SQL, which then fine-tunes the engine's local model.
Provides a natural language explanation of how the SQL was constructed from the user's question.
Uses a heuristic engine to determine if the result set should be a bar chart, line graph, or pivot table.
Filters for destructive commands (DROP, DELETE, TRUNCATE) at the parser level before they reach the DB.
Providing customers with custom reporting without building hundreds of static dashboard templates.
Registry Updated:2/7/2026
Reducing the backlog of the data engineering team by allowing C-suite to query data directly.
Identifying anomalies in massive transaction logs using natural language queries.