DeepQuery
Text2SQL that actually works on enterprise data. Every other solution breaks at 20+ tables — DeepQuery handles 1,000+. Self-service BI for every employee, no SQL expertise required.
Request a Demo →Why
Real-time insights from business applications for every employee. No complex BI tools, no experts, no ongoing development.
Understanding Your Data
- Builds deep semantic understanding by analyzing schema, data content, org documents, and business terminology — automatically
- Learns every valid value so users search using real data terms, not column names
- Discovers implicit table relationships — critical for legacy and government databases
- AI-generated descriptions for every table and column, enhanced with sample data and business context
Handling Natural Language
- Tolerates typos, abbreviations, and misspellings in queries
- Expands queries with synonyms, translations, and alternative terms automatically
- Full Hebrew support — bidirectional text, morphological awareness, bilingual handling
- Classifies query complexity and adapts processing depth accordingly
Generating Accurate SQL
- Quality-first — every architectural decision prioritizes accuracy over cost or speed
- Multiple independent SQL candidates using different strategies, then selects the best
- Verifies context sufficiency before generation — asks for clarification when ambiguous
- Validates every SQL for safety and runs post-execution sanity checks with self-correction
Built for Scale
- Handles 1,000+ table schemas by fitting full database context into a single AI call
- Comprehensive observability with query logging from day one
- Configurable per-deployment — swap AI providers, storage backends, and parameters without code changes
- Exposed as MCP server — integrates with any AI assistant or workflow
Under the Hood
The AI architecture behind DeepQuery's accuracy at enterprise scale
Triple-Storage Retrieval
Unified retrieval combining semantic search, knowledge graph traversal, and full-text search — fused via RRF, reranked, and verified for sufficiency before SQL generation.
Agentic Orchestration
Intelligent agent coordinates the pipeline — tiered LLM processing with fast models for analysis and strong models for SQL generation. Full observability via OpenTelemetry.
Multi-Path SQL Generation
Multiple independent SQL candidates via separate LLM sessions using different strategies. Self-consistency voting, sufficiency verification, and self-correction on failures.
Complexity-Scaled Processing
Automatically classifies query complexity and adapts processing depth — strategy count, exploration budget, and self-consistency level scale with difficulty.
Ready to Unlock Your
Empower every employee with self-service BI — no SQL expertise required. See DeepQuery in action.
Request a Demo →DeepQuery is built on DeepKnowledge's hybrid RAG engine core — triple-storage retrieval with unified knowledge graph.