Kili Technology
The data-centric AI platform for high-quality training data and model evaluation.
AI-Native Active Metadata Management and Observability for the Modern Data Stack.
DataWise Enterprise is a next-generation data intelligence platform engineered for the 2026 enterprise landscape, where decentralized data architectures and LLM integration are standard. It moves beyond static data cataloging into 'Active Metadata Management,' utilizing a proprietary AI graph engine to map relationships between structured, semi-structured, and unstructured data across multi-cloud environments (AWS, Azure, GCP). The technical architecture is centered on real-time data observability, providing automated PII discovery, semantic lineage mapping, and proactive data quality alerting. By 2026, DataWise has positioned itself as the critical 'context layer' for RAG (Retrieval-Augmented Generation) systems, ensuring that LLMs ingest only verified, high-quality, and compliant data. Its microservices-based deployment supports hybrid-cloud footprints, allowing for on-premises processing of sensitive telemetry while maintaining a centralized control plane. The platform bridges the gap between data engineering and business intelligence through its 'Semantic Bridge,' which translates complex schema relationships into natural language business terms automatically, significantly reducing the overhead of data discovery and governance.
Uses LLM-based parsing of SQL logs and ETL scripts to reconstruct data flows across disparate systems without manual intervention.
The data-centric AI platform for high-quality training data and model evaluation.
The semantic knowledge fabric for high-velocity enterprise intelligence.
Transform complex database schemas into actionable natural language insights with autonomous SQL synthesis.
The industry's first AI-powered, end-to-end data management platform for multi-cloud environments.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Automatically filters and pre-processes data for vector databases, ensuring high signal-to-noise ratios for AI applications.
Analyzes query patterns to predict cloud warehouse spend and suggests partitioning/indexing optimizations.
Validates incoming data against predefined schemas and business rules at the ingestion layer.
Applies security policies and masking directly at the compute layer without moving or duplicating raw data.
Converts technical column names (e.g., CR_USR_ID) into business concepts (Customer ID) using contextual training.
Visualizes data quality trends over time, highlighting 'hot spots' of recurring pipeline failures.
Manual identification of PII across thousands of tables is slow and prone to error.
Registry Updated:2/7/2026
LLMs accessing outdated or low-quality data produce useless outputs.
Migrating to Snowflake/BigQuery without knowing which data is redundant.