MCP server for industrial software that lets AI assistants connect machines, build screens and analyze production data
Human authority infrastructure for autonomous AI
Stage
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Founded
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Based in
TR
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Pricing model
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Freemium
Pricing
Not given
Free: $0 per month with 2 connections, 1 agent, 500 protected actions per month. Developer: $99/month with 25,000 billable executions included, then $0.001 per billable execution with $500 default overage cap. Business: Contact Sales for paid pilots and enterprise contracts.
Key features
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
Cryptographically signed receipts and verifiable evidence
Multi-party approval for organizational authority
Open-source SDK, MCP server, and framework adapters
Authority lifecycle management from grant to revocation
Supported integrations with payments and cloud accounts
Individual, family, business, and enterprise deployment options
Enforcement at tool execution boundaries
What makes it different
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
Write-once signed decisions verifiable by auditors without trusting the vendor
Controls sit at the point where AI systems take consequential actions
One company integrating security and compliance disciplines
Open-source cores and published research allow vendor review
PLCGateway AI MCP
Guardian