The Problem: Data Without Context
Data without governance is a breeding ground for errors. Wrong inputs lead to wrong decisions, systems don’t talk to each other properly, and finding root causes is incredibly difficult without proper tracking. The solution is not more data, but rather the right context for the data already available.
Grounding AI with Rhize
Rhize uses ISA-95 to provide a structured contextual framework for manufacturing data. Because AI applications require this type of structured context to function effectively, Rhize’s foundation makes it a viable baseline for developing AI-driven applications within manufacturing systems.
The quality of that foundation determines everything. Feed AI messy or inconsistent data and you get unreliable results. Give it clean, well-structured, meaningful data and applications can be built faster and more reliably than traditional development, with far fewer errors along the way. Rhize provides that foundation.
See it in action. Check out these recent demonstrations:
- Building a GraphQL Schedule UI in Appsmith. (This video demonstrates how to quickly build a custom schedule user interface in Appsmith by querying GraphQL database data and using built-in AI assistance to generate and refine the layout and logic of the widget)
- Integration Microservice Generation with Restate (This video demonstrates how a Rhize ERP integration system uses Restate, Kafka, MQTT, and BPMN workflows to bi-directionally transform and sync ERP payloads into ISA-95 standard objects within a database)
- Data Insights with Apollo MCP & Apollo Router (This video demonstrates how the Apollo Model Context Protocol (MCP) connects with an Apollo router to autonomously query, interpret, and summarize highly nested GraphQL database data into user-requested formats like tables and totals)
- End to End ERP Integration Layers with AI & Restate (This video demonstrates how to use AI and the Restate SDK to rapidly generate and map an entire integration layer that converts SAP ERP payloads into ISA-95 standard objects within the Rhize stack)
- AI-Powered Defect Logging in BPMN Workflow (This video demonstrates how an AI integrated within a BPMN workflow can process either user text or audio recordings to automatically classify and log the cause of manufacturing equipment defects)
- Building a Factory Work Recorder in Appsmith (This video demonstrates using Appsmith and AI to design and configure a mobile-friendly custom widget for factory operators to log material usage and update production job statuses in real-time.)
Manufacturing is complex. The data that runs it doesn’t have to be. When your data is organized and properly managed, AI becomes less of a gamble and instead a reliable tool for building the applications you need. Rhize makes this possible.