manufacturing data hub

the new standard for

manufacturing data

Rhize is a real-time, event-driven manufacturing data hub—a first-of-its-kind, headless platform that allows you to create an ISA-95 standardized, application-ready data model of your entire operation.
Better governance and delivery start here.

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STANDARDIZED

Contextualize all your plant data in ISA-95 schema.

CLOUD-NATIVE

Get high availability without putting data in the cloud.

event-driven

Orchestrate & innovate at the speed of operations.

Headless

Build applications your way without vendor lock-in.

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THE PLATFORM

A completely new way to USE MANUFACTURING DATA

Rhize allows the producers and consumers of your operations data to connect in a standardized, governed, and real-time manner. This gives IT and OT architects the data they need to rapidly innovate in parallel and close the gap between insight and action.

01

Standardize the model

ISA-95 provides a standard framework to define your equipment, process, events, and the relationships between them.

02

INTEGRATE YOUR DATA

Rhize ingests and stitches together data from all over your operation, from edge to ERP to quality systems.

03

CONTEXTUALIZE INTO EVENTS

Rhize uses your rules and custom business logic to contextualize your data into a real-time event-driven platform.

04

Build ONLY THE APPS YOU NEED

Connect your standardized, real-time data source to any front end, workflow, or AI/ML application via one API.

05

Orchestrate & innovate

Take your queries from minutes to milliseconds and drive real-time operational focus at any scale.

EXPLORE THE PLATFORM
COMPARISON CHART

Rhize vs. traditional
data management

Rhize offers a new way to integrate manufacturing data beyond simple equipment context. Unlike traditional models, Rhize is the first solution that can capture and store, in standardized schema, the event-based context of your data. So, now, instead of merely glimpsing a complex relationship between data points—such as material flow, operator behavior, and downtime—you can create a real-time feed for it and tap into it at will. 

Data Storage
Integrates data from various sources and stores in a standardized relational structure
Data Integration
Provides real-time, event-driven data integration
Data Schema
Standardized ISA-95 schema
Data Processing
Batch and real-time data processing
Business Process Management
Supports both short and long-running, user-defined business processes
Complex Event Processing
Cross-stream, real-time complex event processing which can trigger the execution of a user-defined business process
Data Query
GraphQL API supports user-defined queries and query subscriptions over a standardized schema
Data Security
Provides Role, Attribute, and Graph-based access controls and high-level encryption
Data Scalability
Highly scalable with horizontal scaling
Data Latency
Low latency for real-time data access and processing
Use Cases
Ideal for diverse data sources, real-time processing, real-time analytics, and application development
Cost
Cost-effective due to flexibility in data storage and processing
Suitable for Transactional Applications
Event Streams for AI/ML
Legacy-to-Cloud Migration
data lake
Data Storage
Stores raw, unstructured and structured data in its native format
Data Integration
Uses batch processing for data integration
Data Schema
Schema-on-read, providing schema flexibility
Data Processing
Primarily batch processing with limited real-time capabilities
Business Process Management
Not supported
Complex Event Processing
Handled outside the data lake
Data Query
Supports ad-hoc queries but may require schema-on-read transformation
Data Security
Provides access control but may require additional security layers
Data Scalability
Scalable but with some limitations on real-time processing
Data Latency
Higher latency for processing due to schema-on-read
Use Cases
Ideal for big data analytics and storage of raw data
Cost
Cost-effective for storing large volumes of data, may require optimization for query costs
Suitable for Transactional Applications
Event Streams for AI/ML
Legacy-to-Cloud Migration
Data Warehouse
Data Storage
Stores structured data in a structured format
Data Integration
Uses ETL (Extract, Transform, Load) to load data in batches
Data Schema
Schema-on-write with a rigid structure
Data Processing
Optimized for complex batch processing
Business Process Management
Not supported
Complex Event Processing
Not supported
Data Query
Optimized for structured, pre-defined queries
Data Security
Provides access control, encryption and auditing features
Data Scalability
Scalable with vertical scaling for performance improvement
Data Latency
Low latency for pre-defined data structures
Use Cases
Ideal for structured reporting, business intelligence and historical analysis
Cost
Can be expensive due to data transformation and high-performance infrastructure
Suitable for Transactional Applications
Event Streams for AI/ML
Legacy-to-Cloud Migration
Data Storage
Integrates data from various sources and stores in a standardized relational structure
Data Integration
Provides real-time, event-driven data integration
Data Schema
Standardized ISA-95 schema
Data Processing
Batch and real-time data processing
Business Process Management
Supports both short and long-running, user-defined business processes
Complex Event Processing
Cross-stream, real-time complex event processing which can trigger the execution of a user-defined business process
Data Query
GraphQL API supports user-defined queries and query subscriptions over a standardized schema
Data Security
Provides Role, Attribute, and Graph-based access controls and high-level encryption
Data Scalability
Highly scalable with horizontal scaling
Data Latency
Low latency for real-time data access and processing
Use Cases
Ideal for diverse data sources, real-time processing, real-time analytics, and application development
Cost
Cost-effective due to flexibility in data storage and processing
Suitable for Transactional Applications
Event Streams for AI/ML
Legacy-to-Cloud Migration
data lake
Data Storage
Stores raw, unstructured and structured data in its native format
Data Integration
Uses batch processing for data integration
Data Schema
Schema-on-read, providing schema flexibility
Data Processing
Primarily batch processing with limited real-time capabilities
Business Process Management
Not supported
Complex Event Processing
Handled outside the data lake
Data Query
Supports ad-hoc queries but may require schema-on-read transformation
Data Security
Provides access control but may require additional security layers
Data Scalability
Scalable but with some limitations on real-time processing
Data Latency
Higher latency for processing due to schema-on-read
Use Cases
Ideal for big data analytics and storage of raw data
Cost
Cost-effective for storing large volumes of data, may require optimization for query costs
Suitable for Transactional Applications
Event Streams for AI/ML
Legacy-to-Cloud Migration
Data Warehouse
Data Storage
Stores structured data in a structured format
Data Integration
Uses ETL (Extract, Transform, Load) to load data in batches
Data Schema
Schema-on-write with a rigid structure
Data Processing
Optimized for complex batch processing
Business Process Management
Not supported
Complex Event Processing
Not supported
Data Query
Optimized for structured, pre-defined queries
Data Security
Provides access control, encryption and auditing features
Data Scalability
Scalable with vertical scaling for performance improvement
Data Latency
Low latency for pre-defined data structures
Use Cases
Ideal for structured reporting, business intelligence and historical analysis
Cost
Can be expensive due to data transformation and high-performance infrastructure
Suitable for Transactional Applications
Event Streams for AI/ML
Legacy-to-Cloud Migration

ENTERPRISE-WIDE FLEXIBILITY

Most data solutions are rigid and difficult to customize. With Rhize, you can model data for a specific use case, customize it with your business logic, and develop purpose-built UIs in parallel.

ZERO DOWNTIME

Global manufacturers rarely get the time to upgrade a mission-critical system, especially across sites. With Rhize’s high availability, you can upgrade at will without ever going offline.

Open STANDARDS

Cooperation and collaboration require common language and ideals. Rhize combines time-tested standards and protocols (ISA-95 & MQTT) with next-generation ways to access them (GraphQL).

Scalable ON-DEMAND

As your operation grows, Rhize grows with you, leveraging Kubernetes clustering to process millions of events per second and add nodes as each new site comes online. 

Every other data platform is throwing tech at an unknown problem rather than starting with the problem and building the technology. Rhize starts with the problem. They know the language of manufacturing. And they save us loads of time and resources because we don’t have to build the solution ourselves.

Jordan Croteau

Sr. Director, Manufacturing & Facilities Systems
Use cases

Headless by Design. Endless by Application.

Rhize has virtually limitless application for global manufacturers. This is evidenced by our early adoption in the life sciences, serialized discrete manufacturing, steel, and food and beverage markets.

what can it do
who we work with

real-time batch records

Access a real-time, event-driven view of your batch manufacturing process—not just an end-of-batch record—making it easier to identify and decrease deviations in product quality in a far more timely manner.

INTEGRATED Track & Trace

Rhize allows you to trace product history through each material, machine and human interaction, but with all your supplier data integrated, giving you an up-to-the-minute, moving picture instead of merely a historical snapshot.

EVENT-DRIVEN WORKFLOWS

Monitor plant and operations activity in real-time and generate events if a change in status occurs, from equipment stoppage to operator action. Then trigger specific business process automation workflows to streamline your response.