Use case
Operational Data Pipelines
Every system on your plant floor speaks a different language, and every integration you build by hand becomes something nobody dares to touch.
01 · Customer pain
Point-to-point integrations do not scale.
Machine data lives in PLCs, historians, SCADA and MES; business context lives in ERP and quality systems. Getting one to talk to the other means custom scripts, vendor gateways, and undocumented middleware.
It works, once, on one line. Then the estate grows to dozens of machines across multiple sites, and the integration layer becomes the riskiest, least visible part of the operation.
Protocol sprawl
OPC UA here, Modbus there, S7 on the old line, CSV exports from the lab. Every connection is a one-off project with its own failure modes.
Unmanaged edge scripts
Data movers live on plant PCs nobody owns, with no version control, no deployment process, and no answer to 'what runs where?'
Vendor-locked connectivity
Proprietary connectivity platforms meter every tag and every target, and take the integration knowledge with them when the contract ends.
Data that AI cannot use
Raw tag streams without context, units, or structure. Every analytics and AI initiative starts with the same expensive clean-up.
02 · Outcome first
One governed pipeline layer, from edge to everywhere.
FlowFuse turns data movement into managed, visual flows: built in Node-RED, versioned, deployed to fleets of edge instances, and observable from one place. The pipeline becomes an asset you own instead of a risk you inherit.
Connect anything to anything, visibly
OPC UA, MQTT, Modbus, S7, REST, SQL and historians wired in visual flows anyone on the team can read and change.
Deploy to hundreds of edge instances
DevOps pipelines, snapshots and remote deployment roll the same governed flow out across lines and sites, with rollback.
AI-ready data by construction
Contextualized, structured data landing in your Unified Namespace, Tables and historians, ready for analytics and AI agents on your terms.
03 · How it works
How FlowFuse builds operational data pipelines.
A pipeline is a protocol on one end, a shared payload shape in the middle, and consumers on the other. This is how it breaks down and which FlowFuse pattern keeps it from becoming untouchable.
Edge building blocks
Each protocol becomes a versioned subflow published as a package, not a bespoke integration. Sites assemble their own pipeline from blocks the platform team maintains, and a fix to a block propagates by version bump.
Read the pattern in the docsThe individual pieces
One reusable block per protocol
OPC-UA, Modbus, S7 and serial each become a maintained subflow package rather than a hand-built integration nobody dares touch.
DocsNormalize to one payload shape
Every source produces the same envelope: identity, value, unit, quality and timestamp. Consumers stop caring what spoke first.
Model the topic structure
A namespace that reflects site, area, line and asset, decided once so producers and consumers agree without coordinating.
Publish to a broker
The Team Broker means adopting a namespace does not start with procuring and hardening infrastructure.
DocsRoute to consumers
Historian, database, MES, ERP and cloud each subscribe to what they need instead of being wired point to point.
Version the blocks, not the copies
Shared library and packages mean an improvement lands everywhere by upgrade rather than by hunting down duplicates.
Docs04 · Why this is important
The integration layer decides how fast everything else moves.
Every initiative that matters, from OEE to AI, arrives at the same bottleneck: can the data get there, with context, reliably?
Time-to-value is the buying criterion
Connectivity programs are judged in weeks, not quarters. A pipeline you can stand up in days changes what the business is willing to attempt.
Unmanaged glue code is operational debt
Every undocumented script is a future outage with a name nobody remembers. Governance is not bureaucracy here; it is uptime.
AI readiness is a data property
Agents and analytics are only as good as the contextualized data they can reach. The pipeline layer is where readiness is actually built.
05 · Why off-the-shelf doesn't work
Why the usual approaches stall.
Teams typically arrive here from one of three directions, each with the same ceiling.
Per-tag pricing, closed tooling
Licensed connectivity suites meter growth and lock integration logic into a vendor's black box.
Scripts that become legacy on day one
Python on a plant PC moves data until its author changes roles. Then it is archaeology.
Built for SaaS APIs, not the plant floor
Generic integration platforms have no OT protocol depth and no answer for edge, offline, or on-prem constraints.
06 · With / without FlowFuse
Without FlowFuse
Every integration is a project
New machine, new script, new risk. Knowledge lives in individuals.
No view of what runs where
Data movers scattered across plant PCs with no inventory, versioning, or rollback.
Data arrives raw
Consumers each re-clean the same streams; AI initiatives stall at the data step.
With FlowFuse
Integration is a managed capability
Visual flows, shared patterns, one library of connections the whole team can maintain.
Fleet-scale deployment with rollback
Pipelines, snapshots and device groups push governed changes to every edge instance.
A data foundation you own
Structured, contextualized data in your UNS and Tables, feeding dashboards, MES, and AI.
07 · Build it with AI
From described to deployed, with the FlowFuse Expert
The FlowFuse Expert works on this use case with you: describe the pipeline you need and get a working starting flow, then refine it with AI assistance and keep full ownership of the result.
Describe it, get a starting flow
Tell the Expert what should move from where to where. It assembles tabs, nodes and wiring for a working pipeline starting point. Currently in open beta on FlowFuse Cloud.
Refine with in-editor assistance
Function Builder and inline code completions help with payload transforms and SQL; the flow explainer documents what any part does.
Make it AI-ready
Expose pipeline data as MCP tools with the MCP nodes, and use the certified LLM nodes (OpenAI, Anthropic, Gemini, or local models via Ollama) inside flows where language models add value.
AI capabilities noted as beta are in open beta on FlowFuse Cloud at time of writing. Placeholder template copy for internal review.
Automotive, Food & Beverage, Life Sciences, Aviation & Aerospace, Aerospace Components, Renewables, Semiconductors, Electronics & Appliances · all industries
See your data estate in one governed layer
Talk to an expert about replacing point-to-point integrations with managed pipelines, or start building today.
