Flexible subscription, term, and perpetual licensing models. Organizations switching from conventional architectures see 60-80% reduction in total infrastructure cost. CPU-based pricing with no incremental ETL costs.
Key features
Ingest timestamped text
Recall with ranked documents
Dated evidence and sources
Audience-based access control
Query past dates
Deletion and data removal
SOC 2 Type II certified
Self-hosting option
MCP server integration
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
What makes it different
Maintains history of fact changes with dates
Returns current facts not similarity-ranked text
Verifiable with source attribution
Works at scale with minimal accuracy loss
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
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