PLC data collection
Reading tags and registers from PLC units on the line, at the rate the process actually changes, without interfering with the control logic that runs it.
Industrial IoT development and PLC monitoring: reading live data from PLC units such as Siemens, moving it through a gateway into cloud dashboards, and wiring machines, sensors and readers into the business systems that record them. Production, downtime and efficiency become visible while the shift is still on.
This is the cluster that separates a systems architect from a web developer: one end of the integration is a physical machine that will not wait for a retry.
Reading tags and registers from PLC units on the line, at the rate the process actually changes, without interfering with the control logic that runs it.
A gateway between the plant network and the cloud, with local buffering so a dropped connection delays the data instead of losing it.
ESP32 and smart sensor firmware for equipment with no data port of its own — scales, readers, access control and counters wired into the software that records them.
Live output, downtime, cycle time and efficiency views built for the people on the floor and the people in the meeting — with alerts when a line stops.
Production counts feeding stock, planning and reporting, so what the machine made and what the system believes were made are the same number.
The machines already know all of this. The problem is that nothing above them does.
Counted by hand at the end of the shift, typed in the morning, and already too late to act on.
Downtime is recorded as a reason someone remembers, not as a timestamp the machine produced.
The data exists on the panel and stops there, because the plant network was never meant to leave the building.
What was made and what was recorded drift apart, and the correction is a stock count nobody wants to do.
Scales, readers and older machines that only show a number on a display nobody is logging.
Built on manually entered numbers, so it disagrees with the floor and gets ignored within a month.
Signals from the production line now travel through a gateway into the cloud, where they become the dashboards production and stock decisions are made from.
Read the case study →Machine-read input replacing manual entry, so the record is created where the physical event happens.
No. The reading side is kept strictly separate from the logic that runs the machine — data is read, never written back into the process. That boundary is what makes it safe to add monitoring to a line that is already producing.
The gateway buffers locally and forwards when the link returns, so an outage becomes a delay rather than a hole in the data. Dashboards show the gap explicitly instead of drawing a straight line through it.
It is the same work with a different endpoint. The value is rarely the dashboard on its own — it is production data reaching the stock, planning and reporting systems, which is an integration problem before it is an electronics one.
Tell me what you are trying to build, automate or scale. You get a straight answer on fit within a day — and a name to call if it is not me.