Kyligence
Augmented OLAP and Unified Metric Stores for High-Performance Cloud Analytics.
Transform complex natural language into optimized SQL and NoSQL queries with schema-aware precision.
AI Query Master is a specialized 2026-gen data orchestration layer designed to bridge the gap between technical database architectures and non-technical business requirements. Its architecture utilizes a proprietary LLM-router that selects between GPT-5, Claude 4, and specialized fine-tuned SQL-Coder models depending on the complexity of the relational schema provided. Unlike basic text-to-SQL wrappers, AI Query Master performs a deep 'Schema Embed' of the user's database metadata, allowing it to understand table relationships, foreign key constraints, and custom data types without the user ever sharing sensitive underlying data. By 2026, it has positioned itself as the industry standard for 'Contextual Querying,' offering a secure, VPC-deployable environment for enterprises. It supports over 20 dialects, including PostgreSQL, MySQL, BigQuery, Snowflake, and MongoDB. The platform's unique value proposition lies in its 'Query Optimizer' engine, which doesn't just produce functional code, but identifies performance bottlenecks, suggests missing indexes, and provides a step-by-step 'Execution Logic' explanation for every generated statement, making it both an automation tool and an educational resource for junior developers.
Uses RAG (Retrieval-Augmented Generation) to inject database DDL into the prompt context window without exposing actual row data.
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.
Predicts query cost and execution time before running against the production database.
Instantly converts legacy Oracle SQL to modern PostgreSQL or Snowflake syntax.
Automatically redacts potential personally identifiable information from query results and logs.
Generates Plotly or Chart.js code directly from the result set of a query.
Connects to dbt semantic layers to use predefined metrics in query generation.
The system learns from user corrections to improve future query accuracy for specific schemas.
Finance teams needing monthly reconciliation reports without waiting for the BI team.
Registry Updated:2/7/2026
Migrating a legacy system from Oracle to PostgreSQL where SQL syntax differs significantly.
Support managers checking ticket resolution times without SQL knowledge.