You’re In! FrameworX 10.1 is Ready to Go
10.1.5c
- Full Designer environment
- All modules and features included
- 100+ connectors available
- Unlimited tags and screens
- 4-hour continuous runtime per sessions
- Unlimited restarts
.NET 10 is required to use the platform
To install the .NET 10, follow the link in the installer message, or you can find the .NET 10 Desktop Runtime installer from Microsoft here: https://dotnet.microsoft.com/en-us/download/dotnet/10.0. Be sure to pick the version that matches your CPU architecture, which is likely x64. (And don't pick the "SDK" or ".NET Runtime" or other versions - just the ".NET 10 Desktop Runtime")
Build Faster with a Tatsoft Engineer
Our Tatsoft Assistance Team team is here to help you:
- Solve a specific application challenge
- Set up your first tags, displays, and data sources
- Review the best way to deploy FrameworX in your environment
- Learn how to get the most out of scripting, WebAssembly graphics, or MQTT
AI Just Built a Complete SCADA System From One Sentence
This video demonstrates how FrameworX AI Designer builds a complete industrial solution inside the live Designer IDE — tags, alarms, historian, protocols — all from a single text prompt, in real time.
What AI Designer Does:
- Connects directly to the FrameworX Designer IDE
- Creates tags, displays, alarms, and device protocols
- Configures historian and runtime settings
- Builds the entire solution from one sentence
- Shows every change in real time as it happens
From MQTT Data to Machine Learning: Bottling Line Demo
This guide demonstrates how to create a complete IoT solution that collects data from the field, publishes it to an MQTT broker, historizes the data, applies machine learning models, and displays real-time values.
System Overview:
- Collects data from field devices (simulated)
- Publishes data to MQTT broker
- Historizes data to SQLite database
- Applies machine learning models to monitor anomalies
- Displays real-time values in customizable dashboards
Real-Time SCADA Meets AI: FrameworX 10.1 Solar Panels Demo
This guide demonstrates how to integrate MQTT Tools with Cloud AI by setting up a Built-in Broker, mapping MQTT data to the solution namespace, creating assets, and configuring MCP scripts for solar panel monitoring.
System Overview: Your solution now:
- Runs a Built-in MQTT Broker with SparkPlug simulation
- Maps MQTT data structure to the solution namespace
- Organizes data through the Europe asset structure
- Monitors solar panel data via MCP scripts
- Provides intelligent insights through Cloud AI integration
- Enables real-time queries and analysis of MQTT data