Tulip has just previewed code-based apps, where engineers build in their own IDE with an AI coding agent. Other platforms are following suit, and for good reason. The idea is right: let AI write the app and let the platform keep it under control.
The Fuuz® industrial operations platform was built that way. We think it is where enterprise-grade AI building leads.
AI Is The Easy Part. Running It Across A Hundred Plants Is Not.
Anyone can generate an app. What separates a demo from an operation is everything around it: how it is released, how it is reversed, how it is repeated at the next site, and who is allowed to see which data. Four things matter most.
Build with your own tools
Use your own Git process, or Fuuz's built-in version control. The Git integration (beta) works with VS Code, Cursor, any IDE you bring, or our native IDE, and your AI agent builds through the platform, against the same models your operation already runs on.
Release with a way back
Build, QA and production environments, blue-green deployment and native rollback, with full audit logging, for every customer. Blue-green deployment lets you swap your production app for your draft app without downtime on the plant floor, and it is made possible by our enterprise-grade multi-tenancy.
Build once, deploy to many
Package an app or a platform-level configuration once, deploy it to every plant, and let each site vary within limits you set. Full app trace, component registration and an application graph show where everything is used and how it performs, so apps keep scaling.
App trace gives you end-to-end traceability of everything that flows through an app: from the user's click to the data landing, with the validations, sequences and access checks along the way. Component registration joins every app component (form, table, dashboard, flow and script) to the application graph, so you can see where and how each one is used across your enterprise.
Isolated by design, connected by choice
Applications and their models are isolated until you decide otherwise, for governance and compliance. When you want a wider view, role-based access lets administrators choose which data, connectors, devices and users take part in enterprise events and explorers, across one enterprise-wide knowledge graph.
From A Plain-Language Request To A Working Board
Ask for a production scheduler in plain language. Claude reads the Fuuz server documents, then uses the Fuuz platform MCP to model work orders, operations and resources as Fuuz data models, run the scheduling logic in server-side data flows and compose the board screen. Jobs that are already running are locked, so only pending work can move.
Connected Operations, Not A Worker At A Station
An app that guides one operator is useful. Running an enterprise means the machine, the work order, the lot and the quality record are the same facts for everyone who needs them. In Fuuz every app is built on that shared model, so the next app reads what is already there instead of adding another silo.
Different Tools For Different Stages Of Scale
These platforms are strong choices for smaller manufacturers that want to put AI-built apps in operators' hands quickly, one team or one plant at a time. We think they do that well. Fuuz fits when scale becomes the problem: governance across many sites, release control and one connected view of the enterprise. In our opinion, that is where Fuuz is ahead of the curve.
See An AI-Built App On Your Own Data
Bring a real use case. We will build it with you and show you what it takes to put it on the floor. Book a working session.
Statements about other vendors are Fuuz's opinion, based on their public announcements. Other product names identify the companies that make them. Features and availability change.