MCP server for industrial software that lets AI assistants connect machines, build screens and analyze production data
Stage
Not given
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Founded
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Based in
US
TR
Pricing model
Freemium
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Pricing
Welcome credit: $2 (personal) or $10 (Instinct). Prepaid packs: $14, $40, or $60 USD. Pay-per-call pricing varies by service: from <0.1¢ for LLM answers, ~1¢ for web search and scraping, 10-30¢ for deep research, from 5¢ for lead enrichment, 25¢ for company signals, from 33¢ for image and media gene
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Key features
Buy and hold tokenized stocks at 100% loan-to-value
Access hundreds of services via single balance
Per-call price disclosure before execution
MCP, TypeScript SDK, and Python SDK integration
Per-agent spending limits and controls
GitHub-based credit line with scoring
Access to LLMs, search, scraping, compute, storage, and media services
Holdings remain invested while credit is used
Weekly or on-demand billing via card on file
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
First credit line backed by tokenized stock holdings at 1:1 LTV
One unified balance for all agent services and payments
No subscription or monthly minimum; pure pay-per-call model
Price transparency with exact cost shown before each call
Holdings never automatically sold; credit is separate repayment obligation
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
Vaaya
PLCGateway AI MCP