Kanlet 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.
Kanlet compared with PLCGateway AI MCP
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Kanlet | 
PLCGateway AI MCP |
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| What it does | Semantic context layer that helps AI agents understand your business data accurately
| MCP server for industrial software that lets AI assistants connect machines, build screens and analyze production data
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| Stage | MVP
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| Key features | - Connects to 15+ data systems including Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Tableau, Looker, Power BI and Postgres
- Extracts structural metadata including databases, schemas, tables, views, columns and relationship lineage
- Provides Web-UI, MCP server and REST APIs for accessing semantic context
- Automatic incremental updates with configurable sync schedules from on-demand to daily or weekly
- Role-level access controls and data classifications including PII, PHI and SPII
- Only reads metadata without accessing or moving actual data from systems
- Integrates with quality monitoring tools including Monte Carlo, Anomalo and Bigeye
| - 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 | - Eliminates confident AI mistakes by grounding agents in semantic context instead of having them guess from table names
- Reduces AI token costs and speeds up answers by resolving schema and joins once upstream instead of in each agent query
- Ensures consistent answers across all agents and teams using the same definitions and business logic
- Provides complete traceability and auditability of AI decisions with documented context and ownership
| - 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
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| Open source | Not given
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| Platforms | Not given
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| Public API | Not given
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 9 ▲ +1 (up) since last check | Not checked yet ● no change since last check |
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| Trust Flow (Majestic) | 1 | 0 |
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| Citation Flow (Majestic) | 24 | 0 |
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| Referring domains (Majestic) | 29 | 0 |
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3 details filled in for both companies.
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