Your industrial data can finally be AI ready, no integration project needed
Your PLCs, SCADA, and historians already hold the data AI needs. Getting it flowing in real time shouldn’t take a custom integration project.
- No credit card
- No coding
- Live and running in minutes
TRUSTED BY INDUSTRIAL TEAMS WORLDWIDE
Global support — offices and support lines in the USA, UK, Spain, Costa Rica, France and Mexico.
THE PROBLEM
Your plant already has the data AI needs. Stop wasting time digging for it by hand.
From messy tags to dropped connections to hours spent digging through the historian by hand, vNode structures your real-time and historical data so your AI agents, not just your team, can actually use it.
- Broken tags. Tag names and formats vary plant to plant, and no model trains well on that kind of inconsistency.
- Gaps in the timeline. A dropped connection means missing data, and missing data means a model trained on gaps.
- Locked in the historian. The data exists. It just never reaches the platform that needs it.
- No agent to ask. Even with the data flowing, getting a diagnosis, a report, or a decision still means a person digging through dashboards by hand, one incident at a time.
THE SOLUTION
Four fixes. One AI ready data pipeline.
Each one closes a gap from above, so data gaps stop being the reason an AI project stalls.
One data model, every AI initiative
vNode's MCP Server exposes your contextualized data model to AI agents through the Model Context Protocol. Governed, secure access to real-time data, KPIs, and alarms, without re-engineering the model for every new AI project.
Real answers, not just data.
Once your data model is exposed through the MCP Server, AI agents can run natural-language diagnostics, generate reports and KPIs automatically, and support operational decisions with full OT context.
Every protocol, no custom drivers
vNode reads OPC UA, OPC DA, Modbus TCP/RTU, Siemens S7, DNP3, EtherNet/IP (including Allen-Bradley), BACnet, and Sparkplug B natively. No middleware, no dedicated integration project.
Store & Forward, standard
Every version buffers locally if the connection drops and resumes automatically once it's back. No data point lost, no gap in the timeline.
HOW IT WORKS
vNode turns raw OT signals into data your AI systems can actually use.
vNode sits between your PLCs and your AI or analytics stack. It reads native protocols on the plant floor, applies consistent structure and tagging, and streams it in real time to wherever your AI systems live. No exports, no manual cleanup, no separate integration project.
1
Connect
vNode reads your OT systems natively: OPC UA, OPC DA, Modbus TCP/RTU, Siemens S7, DNP3, EtherNet/IP (Allen-Bradley and others), BACnet, and Sparkplug B. No custom drivers.
2
Structure
Every tag arrives consistently named and formatted, ready to use, not a spreadsheet cleanup away.”
3
Deliver
Streamed in real time to AWS, Azure, Google Cloud, SQL, MongoDB, MQTT, REST API, or published to your SCADA via IEC 60870-5-104: wherever your AI stack lives.
CUSTOMER STORY
Source: vNode Success Story — “From the Cleanroom to SAP: How FAE Connected Its Production Machines with vNode” (FAE)
Stop waiting for AI ready data. Start putting it to work.
Start your vNode free trial and connect your plant’s real data, so your team and your AI systems can finally use it.
FAQ
What is the vNode MCP Server, exactly?
It’s a module that exposes vNode’s contextualized data model, real-time and historical data, KPIs, alarms and structures, to AI agents and systems through the Model Context Protocol (MCP), while keeping authentication, permissions and auditing intact. It supports built-in tools as well as user-defined tools, prompts and resources.
Do I need a new integration project for every AI initiative?
No. The MCP Server keeps governance, security and context consistent across initiatives, so the data model doesn’t need to be re-engineered each time a new AI agent or system connects.
Where does the historical data come from that AI agents can query?
From vNode’s Historian, rebuilt in v1.22 on TimescaleDB over PostgreSQL, built to handle millions of datapoints per second, with standard SQL access for BI, data lakes and AI pipelines, and disk-based Store & Forward so no data is lost if the destination is unavailable.
What can AI agents actually do with this data once it's exposed?
Documented use cases include natural-language diagnostics on assets and processes, automatic generation of reports and KPIs, and AI-assisted support for operations and decision-making with full OT context.
Which industrial protocols feed the data model that AI agents access?
OPC UA, OPC DA, Modbus TCP/RTU, DNP3, Siemens, Allen-Bradley/EtherNet/IP, Mitsubishi, Yokogawa, GE, Omron, Delta, Marchesini and Mettler Toledo, read natively without custom drivers.
Where do I find technical documentation?
You can access the vNode User Manual or download the latest software version here.