| Key features | - AI on Enterprise Data - bring AI directly to governed enterprise data at petabyte scale
- Security - built-in controls for access, governance, and compliance
- Deployment Options - cloud, on-premises, or hybrid deployment flexibility
- Data Ingest - continuously ingest and prepare high-volume data for AI-ready analytics
- Query Optimization - run complex analytics on petabytes of data with predictable performance
- Semi-Structured Data - give AI richer context with structured and semi-structured data in one platform
- Megalane Parallelism - sub-second queries at petabyte scale
- Zero Copy Reliability - no data duplication or redundant storage overhead
- Compute Adjacent Data Store - computation happens where data lives
- Smart Data Pipelines - continuously governed data pipelines replace manual ETL
| - Connects to 15+ data systems including Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Tableau, Looker, Power BI and Postgres
- Extracts structural metadata including databases, schemas, tables, views, columns and relationship lineage
- Provides Web-UI, MCP server and REST APIs for accessing semantic context
- Automatic incremental updates with configurable sync schedules from on-demand to daily or weekly
- Role-level access controls and data classifications including PII, PHI and SPII
- Only reads metadata without accessing or moving actual data from systems
- Integrates with quality monitoring tools including Monte Carlo, Anomalo and Bigeye
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| What makes it different | - Petabyte-scale performance without moving or duplicating enterprise data
- 60-80% reduction in infrastructure costs compared to conventional architectures
- Production-grade AI delivery with expert implementation and solutions delivery teams
- Flexible licensing models aligned to how enterprise AI programs actually scale
- Unified platform for ingest, governance, query execution and optimization
| - Eliminates confident AI mistakes by grounding agents in semantic context instead of having them guess from table names
- Reduces AI token costs and speeds up answers by resolving schema and joins once upstream instead of in each agent query
- Ensures consistent answers across all agents and teams using the same definitions and business logic
- Provides complete traceability and auditability of AI decisions with documented context and ownership
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Ocient
Kanlet