Kyligence
Augmented OLAP and Unified Metric Stores for High-Performance Cloud Analytics.
Turn natural language into complex SQL queries with schema-aware AI precision.
AI SQL Bot is a sophisticated Natural Language to SQL (NL2SQL) translation engine designed for the 2026 data ecosystem, where rapid insight retrieval is prioritized over manual query construction. Built on a proprietary Retrieval-Augmented Generation (RAG) framework, it ingests database schemas (DDL) without requiring access to sensitive row-level data, ensuring enterprise-grade security and compliance. The tool bridges the gap between non-technical stakeholders and complex relational databases by allowing users to ask questions in plain English. For engineers, it serves as a high-velocity optimization layer, capable of refactoring legacy queries, explaining execution plans, and suggesting indexing strategies. By 2026, AI SQL Bot has positioned itself as an essential middle-ware for AI-driven BI, supporting multi-dialect environments including Snowflake, BigQuery, and traditional PostgreSQL. Its architecture minimizes hallucination by strictly grounding the LLM within the provided schema metadata, making it a reliable choice for production-level data operations and automated reporting workflows.
Uses RAG to inject database metadata into the prompt context for high-accuracy query generation.
Augmented OLAP and Unified Metric Stores for High-Performance Cloud Analytics.
AI-powered adaptive math learning that identifies and bridges learning gaps through granular skill modeling.
Transform raw data into real-time metrics with a powerful semantic layer and automated BI dashboards.
The AI-powered data scientist that automates complex analysis, visualization, and predictive modeling through sandboxed code execution.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Analyzes existing SQL strings to improve performance and readability based on dialect-specific best practices.
Converts SQL logic between different dialects (e.g., Oracle to Snowflake) while maintaining logic parity.
Allows users to map internal jargon (e.g., 'Revenue' to 'total_sales_after_tax') to schema columns.
A proxy layer that ensures actual data values never leave the local environment; only metadata is processed.
Generates a step-by-step logical breakdown of how the AI arrived at a specific SQL structure.
Analyzes query patterns to suggest missing indexes to the DBA.
Sales managers cannot write SQL and the data team is backlogged.
Registry Updated:2/7/2026
Company moving from Oracle to Snowflake; needs thousands of queries converted.
Developers spending too much time writing boilerplate CRUD SQL.