Turn natural language into production-ready SQL and real-time data insights instantly.
NLQuery is a high-performance, LLM-agnostic semantic layer designed to bridge the gap between non-technical stakeholders and complex relational databases. Built on a proprietary Natural Language Processing (NLP) architecture that emphasizes schema security and semantic context, NLQuery enables users to interact with databases using conversational English. Unlike standard GPT-based SQL generators, NLQuery utilizes a sophisticated metadata-mapping engine that indexes table relationships, constraints, and column descriptions without ever accessing the raw row data, ensuring enterprise-grade privacy. In the 2026 market, it stands out by offering hybrid execution: users can generate SQL for manual review or allow the engine to execute queries and return visualized results directly. Its architecture supports multi-dialect conversion (PostgreSQL, MySQL, T-SQL, Snowflake, and BigQuery) while maintaining a feedback loop that allows the system to 'learn' company-specific business logic over time. This makes it an essential tool for reducing BI bottlenecks, empowering marketing and product teams to perform ad-hoc analysis without burdening data engineering teams.
Indexes table metadata into a vector database to provide high-accuracy context for LLM prompt construction.
Turn natural language into production-grade SQL and instant visual insights with RAG-enhanced schema awareness.
Autonomous data synthesis and predictive modeling for the modern AI-driven enterprise.
Turn your databases and spreadsheets into intelligent conversational AI agents.
The conversational AI analyst that transforms your database into an interactive dialogue.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Supports real-time conversion between PostgreSQL, T-SQL, Oracle, and NoSQL dialects using a unified semantic bridge.
Uses heuristic analysis to determine the best chart type (Bar, Line, Sankey) based on the SQL output structure.
Provides a step-by-step logic breakdown in plain English for how the SQL was constructed.
Automatically detects and redacts PII at the metadata level so the AI never 'sees' actual user data.
Saves successful natural language queries as reusable 'Data Assets' with version control.
Directly queries the database in-situ without requiring data ingestion into a separate warehouse.
Marketing managers cannot access SQL databases to see real-time campaign ROI.
Registry Updated:2/7/2026
Schedule a weekly automated Slack report.
Support agents need to look up complex user history across multiple tables without dev help.
Data analysts spend hours identifying null values and outliers in new datasets.