Live OEE for one line
I run one line and I want a dashboard showing live availability, performance and quality for it, with every stop carrying a reason. Simulate the machine signals so it runs before I connect anything.
AI for Industrial Applications, Governed and Built-In
The platform is the control plane. AI is the accelerator.
Use FlowFuse Expert, or bring the agent your company already approved. Both reach only the teams you allow, and every action is logged.
01Sign in to FlowFuse
A new account puts you in a hosted instance with Expert already in the editor. Nothing to add, no connector, no token.
02Or start with the Device Agent
Already running Node-RED on your own hardware? Connect it as a remote instance and Expert works there in the same way.
03Ask for what you need
Build a flow, explain one you inherited, write the Function node, or ask what is running on the floor. Every write waits for you to approve, edit or reject.
Starter prompts
Andon calls with the evidence attached
When an operator flags a problem at a machine I want a record created with that machine's recent signals attached, an owner and a status on it, and a dashboard my supervisor can work through. Simulate the line and a handful of open problems.
A mid-shift board of orders and skills
I want a dashboard my supervisors can use mid-shift: what has to run on each station and who on shift is qualified to run it. Simulate the orders, the roster and the skills.
Call for help, routed and escalated
When a machine calls for help I want it routed to the person who can fix it, escalated if nobody acknowledges it, and the time from call to acknowledgement recorded. Simulate the line events.
A quality check at the machine
I want a check at the machine that measures each part against the tolerance for the product currently running, tells the operator on the dashboard the moment one is out of tolerance, and records every measurement rather than only the failures. Simulate the measurements.
One record per serial, both directions
Each assembled unit gets a serial number at the first station. I want one record per serial listing the stations it passed through, with the torque, temperature and operator captured at each, so I can look up one serial, or list every serial that passed a given station in a shift. Simulate the production steps.
Early warning on machine health
I want the few signals that tell me a machine is going bad, cycle time drift, current, temperature and fault counts, judged against what is normal for that machine, and a dashboard that flags it plus a message to maintenance before it stops. Simulate the machine signals.
Every protocol, one topic structure
I want every machine, whatever protocol it speaks, to publish to one MQTT topic structure of site, area, line and asset, with the same payload fields on every message: value, unit, timestamp and source. Simulate a few machines on different protocols.
Connect and build faster than ever
Connect in seconds and start building. Use natural-language to leverage your company-sanctioned AI in a secure and governed way.
AI Governance you can prove
The control plane every AI action runs through, the same one that governs your teams.
Only the teams you grant
AI reaches the teams you allow and nothing else, enforced by the platform on every call.
Role-based access
The same RBAC that governs your teams governs what AI can see and do.
Nothing gets deleted
No agent can remove an instance, an application, a snapshot or a team. There is no tool for it.
Audit on everything
Every action AI takes is logged and attributed, so you can show exactly what happened and when.
Every way AI shows up in your operations
From accelerating how you build, to acting inside live flows, to connecting the AI your company already trusts. Each one runs on the same governed platform.
01Build
Accelerate engineering
FlowFuse Expert turns intent into working industrial applications, right in the editor.
AI-assisted engineering
Generate and edit Node-RED flows, Function node JavaScript, SQL queries and dashboard UI from plain language, and ask Expert to explain any existing flow so any engineer can pick it up. It works inline in the editor your team already uses, so there is no separate tool to context-switch into. That turns unfamiliar or inherited flows into something the whole team can read and maintain.
Prompt-to-app
Describe the application you need and FlowFuse Expert agentically builds the starting flows and logic directly in your workspace. You begin from a working draft instead of a blank canvas, then refine it like any other flow. Everything it creates stays inside the platform, so the same permissions and review apply from the first node.
Build your own AI agents
Start from an agent blueprint, like the LLM chat agent or RAG chat agent, to stand up a task-specific AI agent grounded in your own data, tools and context. It gives you a proven structure to adapt rather than wiring an agent up from scratch. With MCP servers you can give the agent access to anything, including your RAG applications. Because it runs on the platform, the agent operates within the access you grant it.
Built-in product knowledge
A chat assistant with answers grounded in FlowFuse and Node-RED documentation, so guidance comes from the product, not stale wikis. Ask how a node works or how to approach a build and get an answer without leaving your workspace. It shortens the path from question to working flow for new and experienced users alike.
02Operate
Operate with AI, safely
AI that acts inside your flows and answers questions about live operations, always behind your controls.
Governed autonomous operations
Platform Automations let AI act on live systems, with every write behind an approval card, session-scoped and fully audited. A person approves, edits or rejects each proposed change before it reaches a machine. Nothing runs outside the permissions and RBAC that already govern your teams.
For external agents, approval cards do not apply. The access you granted is the control, enforced on every call.
Ask your plant anything
In Insights mode, ask questions in natural language and get answers grounded in live machine state, alarms and logs. Operators and engineers can check what is happening on the floor without building a report or querying a database by hand. Table and MQTT-broker reading are coming soon.
For external agents, whether they can reach the MCP servers you build in your own flows depends on the agent.
Automated visual inspection
Run ONNX vision models inside flows next to the machine, with camera ingest over RTSP, for inference that works offline and keeps data on your network. Detection results flow into the same logic as any other signal, so you can trigger alerts or actions from what the model sees. Running at the edge means no round trip to the cloud and no image data leaving the plant.
Use any model within flows
Certified LLM nodes bring OpenAI, Anthropic, Gemini or local models via Ollama into any flow with your own keys. Choose the provider that fits each task, or keep everything on local models when data cannot leave your network. Because you supply the keys, model access and spend stay under your control.
03Connect
Connect your own agent
The agent your company already approved, working your platform and building in Node-RED. Your agent, your model, on a boundary you set.
Bring your own AI agent
Point Microsoft Copilot, ChatGPT, Claude or a local model at FlowFuse, sign in, and it can query your teams and instances and build Node-RED applications for you. Where company policy only permits an approved AI agent, this is how that agent reaches your operations, instead of nobody getting AI on the platform at all.
You decide what it reaches
Signing in asks which teams the agent may act on and whether it may make changes at all. FlowFuse holds you to that on every call, so a read-only grant is refused whatever the agent tries. Nothing an agent reaches can delete an instance, an application, a snapshot or a team, and deploying stays yours.
04Expose
Expose your own tools
The other direction. Your flows become tools that an agent can call as part of its work.
AI that uses your tools
Build your own MCP servers and let Insights-mode agents call your tools and services as part of a workflow. Wrap an internal API or system as a tool once, then let agents use it wherever it fits. The agent stays inside the workflow you designed, calling only the tools you register.
Anyone can build them. For external agents, whether they can call them depends on the agent.
More on industrial AI
Worked examples and the thinking behind them, from the FlowFuse AI blog.
Processing RTSP Camera Feeds at the Edge
Learn how to use the FlowFuse RTSP Video Feed node to pull camera frames onto a dashboard and feed local AI models directly.
5 Places Smart Factories Are Already Using AI
Most manufacturers are still debating AI adoption. These five use cases are already cutting downtime, scrap, and energy costs.
The Last Mile Problem in Industrial AI
Your AI pilot passed every test and stalled before production. Here is why that keeps happening, and what it actually takes to stop it.
Get Started with FlowFuse
Your first operational application could be running this week. Request a demo to see how, or explore pricing to find the right fit.