MCP server for industrial software that lets AI assistants connect machines, build screens and analyze production data
Managed Apache Iceberg tables and hosted agents for AI to work with data
Stage
Not given
Startup
Founded
Not given
2026
Based in
TR
ES
Pricing model
Not given
Freemium
Pricing
Not given
Storage €0.10 per GB-month (5 GB free monthly), Compute €0.12 per CPU-hour (10 CPU-hours free monthly), AI tokens €2.50 per million (5 million free monthly). No request, ingress or egress fees.
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
Managed Apache Iceberg tables with automatic maintenance and backups
Multiple data ingestion methods including files, APIs, webhooks, and scheduled URLs
Query with DuckDB, Spark, Trino, Polars, or PyIceberg
Public data catalogs served over a CDN
Automatic spatial clustering for geometry tables
FTP, FTPS and SFTP support for file uploads from cameras and devices
Corporate sign-in and granular access control for organizations
Audit trail and activity log for organizational use
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
Open table format: Apache Iceberg ensures your data remains portable and readable by any compatible engine
Agents keep what they observe, decide and do as persistent tables for learning across runs
Operated in Europe with data storage in Frankfurt and model inference in Paris
Pay only for what you use with no request, ingress or egress fees
Serverless platform with no servers or clusters to manage
PLCGateway AI MCP
LakehouseBox