The Intelligent Semantic Layer for Natural Language Data Analytics and SQL Automation
DeepQuery is a next-generation AI-driven data intelligence platform designed to bridge the gap between complex relational databases and non-technical business users. Built on a proprietary Semantic Knowledge Graph architecture, DeepQuery goes beyond basic Text-to-SQL translation by maintaining a persistent understanding of business logic, synonyms, and data relationships. In the 2026 market landscape, DeepQuery positions itself as a critical 'Agentic Data Layer,' enabling autonomous AI agents to query enterprise data warehouses with high precision and safety. The platform utilizes a sophisticated RAG (Retrieval-Augmented Generation) pipeline specifically tuned for schema metadata, ensuring that queries are not only syntactically correct but contextually accurate to the specific organization's naming conventions and KPI definitions. By abstracting the SQL layer, it allows for real-time data storytelling, automated anomaly detection, and the democratization of data-driven decision-making across departments. Its architecture supports hybrid cloud environments and focuses heavily on data privacy, ensuring that actual row-level data never leaves the client's secure environment while metadata-driven intelligence remains at the core of its operational utility.
Maps complex business relationships and synonyms to technical database schemas using graph embeddings.
Convert natural language prompts into high-performance SQL for the Atlassian Data Lake.
Conversational Business Intelligence for deep-dive data exploration and predictive forecasting.
Turn Complex Natural Language into Production-Ready SQL and Executive Insights Instantly
Turn complex data warehouses into conversational intelligence engines using agentic RAG and NLQ.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses a secondary model to validate SQL syntax and logical joins against the actual DB dialect before execution.
A policy-enforcement engine that monitors AI-generated queries for PII exposure and compute-heavy operations.
Capable of joining 10+ tables by understanding transitive relationships in normalized databases.
Queries are processed in-memory and result sets are streamed directly to the UI without being stored on DeepQuery servers.
Continuously scans query results for statistical outliers and proactively notifies users via webhooks.
Generates human-readable documentation for messy, undocumented legacy databases automatically.
Executives needing instant revenue breakdowns without waiting for a BI analyst to build a Tableau dashboard.
Registry Updated:2/7/2026
Identifying patterns in user behavior that lead to cancellations using natural language.
Managing stock levels across multiple warehouses with complex SKU structures.