Generative Data Insights
Understand, Profile, and IntelligenceAutomatedintegration Converse with Your Data
Go beyond basic metadata. Build a unified semantic model of your data, automates data quality profiling, and provides conversational access for your entire organization.
The three Pillars of GenAI Data Intelligence
The integrated stack for data understanding and access.
Traditional data understanding is manual, slow, and doesn’t scale. Generative AI automates metadata enrichment, profiling, and access enabling true self-service data access for business users.
Generative Schema Semantic: Model Your Data for Clarity.
• Automated Metadata Augmentation: Enriching existing metadata with business context and relationships.
• Semantic Search: Enabling users to search for data using business terms rather than technical column names.
• Knowledge Graph Integration: Building a comprehensive knowledge graph of your data assets to improve discovery and understanding.
Automated Metadata Augmentation
Enriches technical metadata with business definitions—continuously updated as data evolves.
Semantic Search
Find data using business terms, not technical field names—supporting cross-system discovery.
Knowledge Graph Integration
Visualizes relationships between datasets, tables, and columns—understanding lineage and dependencies.
Generative Data Profiling
• Automated Documentation: AI-generated summaries of data fields, value distributions, and potential issues.
• Anomaly Detection: Proactive identification of outliers and drift in data patterns.
• Rule Suggestion: Automatically proposing Data Quality rules based on observed data characteristics.
Chat With Your Data: Conversational Access to All Information
• Database Chat (Structured Data): Translate natural language questions into precise, executable SQL queries.
•Document Chat (Unstructured Data): Ask questions about policy manuals, legal contracts, or research papers and get context-aware answers sourced directly from the content.
•Natural Language Querying: Supports complex, multi-part questions and follow-ups, allowing for a true conversational data exploration experience.
Insights and Updates
Generative Schema Semantic: Model Your Data for Clarity.
Automatically enrich metadata with business-friendly definitions and make data discoverable through semantic search and knowledge graph relationships.
Automated Metadata Augmentation: Enriching existing metadata with business context and relationships.
Semantic Search: Enabling users to search for data using business terms rather than technical column names.
Knowledge Graph Integration: Building a comprehensive knowledge graph of your data assets to improve discovery and understanding.
Insights and Updates
Generative Data Profiling builds trust in data quality.
Automatically document data, detect anomalies and drift, and generate AI-powered rule suggestions for continuous monitoring.
Automated Documentation: AI-generated summaries of data fields, value distributions, and potential issues.
Anomaly Detection: Proactive identification of outliers and drift in data patterns.
Rule Suggestion: Automatically proposing Data Quality rules based on observed data characteristics.
Insights and Updates
Chat With Your Data: Conversational Access to All Information
A unified conversational interface across databases and documents—no technical skills required, with instant answers and follow-up questions supported.
Database Chat (Structured Data): Translate natural language questions into precise, executable SQL queries.
Document Chat (Unstructured Data): Ask questions about policy manuals, legal contracts, or research papers and get context-aware answers sourced directly from the content.
Natural Language Querying: Supports complex, multi-part questions and follow-ups, allowing for a true conversational data exploration experience.
How It Works: The Architecture of Integration
Our SIX pillars are powered by a cohesive set of foundational capabilities. Generative Schema Semantic creates the understanding, Generative Data Profiling ensures trust, and Chat With Your Data provides the interface. Here’s the technology that makes it possible.
Automated Data Discovery
Find and understand data assets using business language, not technical field names.
Self-Maintaining Semantics
Semantic understanding is continuously updated as data evolves.
Continuous Quality Monitoring
Move beyond one-time checks with proactive anomaly detection and ongoing profiling.
Database Chat (NL → SQL)
Natural language queries translated to SQL for structured data sources.
Document Chat
Conversational AI over PDFs, documents, and knowledge bases with context-aware answers.
Technical Architecture Foundation
LLM layer for natural language understanding, semantic layer (knowledge graph/metadata), data quality layer
How Teams Use GenAI Data Intelligence
An end-to-end data intelligence pipeline to understand structure, assess quality, and provide natural language access across all your data.
Data Catalog Enrichment
Enrich technical metadata with business definitions to make data discoverable and understandable.
New Employee Onboarding
Help new team members find the right datasets fast with semantic discovery and clear documentation.
Regulatory Reporting
Identify where sensitive or regulated data exists by searching using business terms and context.
New Data Source Onboarding
Automatically profile new sources, generate documentation, and surface issues early.
Pre‑AI Project Data Assessment
Assess quality and anomalies before model training to reduce downstream risk.
Executive Dashboards
Enable fast, self-service questions and answers without waiting on data teams.
