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.
Not given
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
Context Graph of Work - continuously updated model of how work gets done across enterprises
Blueprint for AI roadmap planning with defensible ROI recommendations
Process Intelligence to identify bottlenecks and prioritize automation
Engineering Intelligence for unified view of developer work
Work-aware agents grounded in real operational context
Zero-integration capture across legacy and modern systems
In-environment processing with data staying in customer perimeter
Agentic Operating Procedures for governed agent execution
Audit logs and compliance tracking for every agent decision
Built on 25B+ events from 500+ observed processes
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
Observes actual work at scale across enterprise systems rather than relying on assumptions or documentation
Delivers AI agents that work in production at scale, not just pilots
Provides measurable ROI and audit trails for AI spending and decisions
Privacy-first architecture keeps sensitive data within customer environments
Ocient
Skan AI