Skan AI vs PLCGateway AI MCP
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Skan AI compared with PLCGateway AI MCP
| Detail | 
Skan AI | 
PLCGateway AI MCP |
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| What it does | Not given
| MCP server for industrial software that lets AI assistants connect machines, build screens and analyze production data
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| Stage | Established
| Not given
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| Founded | 2020
| Not given
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| Based in | US
| TR
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| Pricing model | Contact for pricing
| Not given
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| Pricing | Not given
| Not given
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| Key features | - 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
| - Connect machines and create devices from plain language instructions
- Build process screens and report dashboards that engineers review before deployment
- Define production assets and calculate OEE for discrete, continuous or batch processes
- Query alarm statistics and production data in natural language
- Support MCP protocol over network HTTP or local stdio connections
- Works with any MCP-compatible AI client including desktop and self-hosted models
- Role-based access control matching viewer, operator and administrator permissions
- Audit logging of every write operation with user identification
- Acknowledge alarms and record batch results within user permissions
- Generate Python code examples for REST API integration
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| What makes it different | - 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
| - Built-in MCP server in every product, no separate AI add-on or cloud service needed
- Production data remains on-premise; nothing sent to cloud unless user's chosen AI model requires it
- Drafts protect production: AI-created screens and dashboards reviewed before going live
- Same access controls for AI as for people: roles, rights and logging
- Assists in user's own language and explains PLCGateway features through built-in help
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| Free plan | Not given
| Not given
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| Open source | Not given
| Not given
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| Platforms | Not given
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| Public API | Not given
| Not given
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 54 ● no change since last check | Not checked yet ● no change since last check |
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| Trust Flow (Majestic) | 14 | 0 |
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| Citation Flow (Majestic) | 38 | 0 |
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| Referring domains (Majestic) | 463 | 0 |
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3 details filled in for both companies.
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