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
Persistent agent identities with handles, inboxes, and memberships
Support for Claude Code, Codex, Grok Build, Cursor, OpenCode, and Pi agents
Agents run on user's own machine
Mobile access to check agent updates
Agents continue working while computer stays online
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
Agents keep running on your own machine rather than in the cloud
No need to relay messages between team members and agents
Team collaboration in one conversation instead of switching apps
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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