Send AI agents to securely query and process data from any database, legacy system, or edge location, even behind firewalls. Our Distributed Data Engine connects to sources across silos and locations, making data practical for AI without migration. You activate your information where it resides.
Run inference across many different compute types and sources on a single platform. Inference Mesh is a silicon-agnostic AI workload orchestrator that eliminates hardware vendor lock-in and scales AI workloads across any silicon (GPU or CPU). No manual tuning needed.
Overcome security and data sovereignty challenges with AI that complies with policies and regulations. Contextual authorization and Relationship-Based Access Controls (ReBAC) make it possible to understand not just who is accessing your data, but why, when, and how, with a comprehensive audit trail.
Ensure that your AI always acts on the source of truth right now, not outdated snapshots. As your data changes, new documents are uploaded, and policies are revised, our Context Manager produces a living ontology that automatically discovers and maps relationships to create a dynamic knowledge layer.
Relationship-based Access Control (ReBAC) with a unified graph engine and full auditability for contextual authorization
Comprehensive audit trail for user actions, system events, and AI agent activities with immutable, append-only logging
Hardened, zero-CVE (common vulnerabilities and exposures) container images to support a secure-by-default platform
Isolated multi-tenant execution spaces with compartmentalized access controls and ephemeral session management
Standardized markings to meet federal and defense classification requirements out of the box
Core architecture that connects to enterprise data sources and processes data where it lives, without migration
System that automatically discovers data and maps relationships across the enterprise to create a living ontology
Silicon-agnostic orchestration that routes AI workloads to data across distributed systems–cloud, on-prem, and edge
Text-to-vector conversion and vector database management for semantic search, integrated with existing infrastructure
Metadata management and DataHub integration for data discovery, governance, and lineage tracking
One unified service to handle data retrieval from any connected source, without the need to manage adapters
Semantic search, keyword matching, and knowledge graph traversal with automatic summarization
Library of production-ready AI applications that deploy to secure infrastructure instantly
Conversational AI agent that can take multi-step actions inside the enterprise firewall, acting as a secure orchestrator
Model repository with full lifecycle management, including discovery from Hugging Face and versioning
Catalog of secure connectors that allow AI agents to interact with databases, mainframes, APIs, and operational tools
Standardized connector architecture that supports Microsoft 365, S3, SharePoint, Outlook, Gmail, Slack, and more
Distributed multi-node orchestration that includes etcd coordination and resource management
Monitoring features to standardize observability and detect anomalies, investigate incidents, and demonstrate compliance
OpenAI-compatible APIs, Model Context Protocol (MCP) extensibility, and Python SDK
Integrated JupyterLab environment for data science experimentation, model prototyping, and interactive analysis
