MCP server for industrial software that lets AI assistants connect machines, build screens and analyze production data
Stage
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Founded
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Based in
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TR
Pricing model
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Pricing
Local: free with CLI, SDK, local proxy, and MCP wrapper. Founding Team: $499 per month after a 30-day pilot, including guided setup, hosted approvals, team dashboard, verified ledger sync, and direct support.
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Key features
Exact payload binding and hash verification
Policy engine with Cedar and Rego support
Local enforcement proxy and CLI
Hosted approvals dashboard and team coordination
Verifiable action ledger with audit trail
Model Context Protocol wrapper
Support for GitHub, Slack, Linear, Jira and cloud platforms
Runtime integration with Anthropic, OpenAI and Cursor
Credential-free playground and examples
ActionPass signed authorization artifact
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
What makes it different
Payload approval bound to exact hash, not summary—changes after approval are automatically rejected
Local enforcement with credential-free operation before team coordination layer
30-day pilot model with no long-term commitment required
Open-source ActionPass artifact and verifier for transparency
Adapter-side verification prevents tool execution after payload tampering
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
Axtary
PLCGateway AI MCP