Kyligence
Augmented OLAP and Unified Metric Stores for High-Performance Cloud Analytics.
Turn Raw Data into Production-Ready Insights with Autonomous AI Engineering
DataSavant is a sophisticated AI-native data orchestration and analytics platform designed for the 2026 enterprise landscape. At its core, the architecture utilizes a proprietary 'Semantic Synthesis Engine' that combines Large Language Models (LLMs) with traditional ETL (Extract, Transform, Load) logic to automate complex data engineering tasks. Unlike legacy BI tools, DataSavant functions as an autonomous agentic layer sitting atop cloud data warehouses like Snowflake and BigQuery. It autonomously discovers schema relationships, generates optimized SQL queries through natural language, and performs recursive data cleaning without manual intervention. The platform's 2026 positioning focuses on 'Zero-Code Data Science,' allowing non-technical stakeholders to build predictive models and automated reporting pipelines. By integrating differential privacy protocols and advanced RAG (Retrieval-Augmented Generation) frameworks, DataSavant ensures that business insights are not only rapid but also contextually grounded in the organization's unique operational history, effectively eliminating the 'hallucination' risks associated with generic AI analytics.
Self-healing data pipelines that detect schema changes and automatically update transformation logic.
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.
Context-aware SQL generation that understands business-specific jargon and acronyms.
Monitors ML model performance and data distribution shifts in real-time.
Applies differential privacy and k-anonymity to datasets before analysis.
Generates statistically accurate synthetic versions of production data for testing.
Syncs metric definitions across PowerBI, Tableau, and Looker automatically.
Extracts structured data from unstructured sources like contract PDFs or meeting recordings.
Manual reconciliation of bank statements with internal ERP data takes weeks.
Registry Updated:2/7/2026
Overstocking and stockouts leading to capital inefficiency.
Identifying at-risk customers before they cancel subscriptions.