Data & AI
From siloed data and fragmented AI to a unified Knowledge Substrate
Traditional Approach
Enterprise data is trapped in silos. AI models are bolted on top of ETL pipelines that weren't built for inference. The result: models that hallucinate, pipelines that break, and data governance that's always behind.
- Fragmented ETL pipelines maintained by hand
- Siloed LLMs with no shared context
- Data governance as an afterthought
- Vector stores disconnected from graph data
- Months to onboard a new data source
x2machines Approach
The x2machines Knowledge Substrate integrates Graph (structural), Vector (semantic), and Skill Library (procedural) into one governed layer — connected to 148 pre-integrated tools via MCP. Data governance is automatic. Knowledge is always current.
- Unified Knowledge Substrate with automated ingestion
- 148 MCP-integrated tools with shared knowledge graph
- Governance baked into every knowledge boundary
- Graph + Vector + Skill Library unified in one substrate
- MCP connector deployed in days
148
Pre-integrated tools
3×
Faster data onboarding
0
Siloed knowledge stores
100%
Governance coverage