The Problem
Enterprise AI deployments fail not because models lack capability, but because they lack context. Data lives across dozens of systems — CRM, ERP, data warehouses, documents, chat platforms — each siloed and inaccessible to AI agents in real time.
Without unified context, AI agents hallucinate, return incomplete answers, and require constant human oversight. The promise of autonomous AI operations remains unfulfilled.
The mantle Approach
Mantle sits between your data infrastructure and your AI agents as a semantic context layer. It connects to enterprise data sources, builds a real-time knowledge graph with entity resolution, and serves quality-scored context via MCP.
What we're building
Unified Data Access
Zero-copy connectors designed to read data in place across enterprise systems — without ETL, data movement, or duplication.
Semantic Understanding
A knowledge graph that resolves identities and maps relationships across all connected sources in real time.
Quality-Scored Context
Every piece of context is scored for relevance, freshness, and reliability — so agents act on signal, not noise.