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DataPilot

Every signal the plant produces, in one place, live and historical.

The phone app: a technician signs in and lands on the plant's dashboards, then opens the forms and the alerts with their history and rules, all from the tab bar.

How it works

How a reading becomes an action A sensor reading and an operator's hand-entered reading both enter one operating record. From that record an alarm reaches a phone, a work order opens against the right asset, reports are written, and KoPilot adjusts setpoints. Process sensor every few seconds Operator's reading typed on a phone One operating record same equipment, same production batch, same clock Alarm on a phone Work order opens Report is written KoPilot adjusts setpoints both are the same shape to the platform
Your existing data logger never sees the operator's reading, and a maintenance system never sees the trend. Holding both makes everything to the right of the record possible.
The alert history filtered to one hammermill, showing three alerts across five days with their severity, status, responder and explanation.
One machine searched out of the alert history. Three rules fired across five days at two severities, and the muted row names who muted it and why, so a rule nobody trusts is visible rather than quietly ignored.

What it takes over from

  • The control-room panel
  • Screenshots sent over messaging apps
  • Paper clipboards and shift logbooks
  • Spreadsheets nobody maintains
  • A standalone data logger

Pays for itself by

Replacing the paper log, the messaging-app screenshot, and the week of trend history that used to vanish. Catching a drift before it becomes a trip.

The number a buyer checks: Unplanned downtime avoided, and hours a week not spent assembling reports.

What DataPilot includes

DataPilot is the plant's shared operational record. It brings together data from machines, people and production activities, organized around the same equipment and timeline.

Process and energy data
Data from your existing PLCs, sensors, meters and control systems, collected at the frequency they already provide it.
Operator-entered data
Shift logs, quality checks, moisture readings, downtime notes and other observations entered directly from a phone or workstation.
Photographs
Images of equipment condition, leaks, finished products or other events, attached to the relevant equipment and time.
Production batches
Runs, campaigns, crop cycles or other production periods that connect operational data to what was being produced.
Alarms and notifications
Rules that notify the right people when a value or condition needs attention.
A DataPilot chart of daily pellet production for one month, stacked by machine, eight machines in eight colours, with one day's figures per machine shown in a tooltip.
Daily pellet production for a month at one plant, stacked by machine. One of the charts a shift opens in DataPilot.

Why machine data and operator notes belong together

Most plants already collect sensor data. The harder part is connecting it with what operators observe and record during the day. DataPilot keeps both in one operational record, linked to the same equipment and timeline.

An alarm can fire on a number a person typed
Not only on a sensor. A moisture reading from the shift log can raise the same alert a meter would.
A data logger sees the machines. A maintenance system sees the assets.
DataPilot sees both, so it can show what the equipment was doing, what operators observed, and what happened next.
One of the two things AI Advisory needs
With Maintenance Management beside it, a developing trend can open a work order, and a weekly report can explain what changed using both sensor data and operator notes. What AI Advisory does with it.

What you can stop doing. Standing at a control panel just to read a value. Sending screenshots of charts through group chats. Maintaining separate spreadsheets because historical data is difficult to access. Asking several people to reconstruct what happened during a previous shift. DataPilot keeps that information in one place, tied to the equipment and time it belongs to.

Two alarm rules in DataPilot. A dryer's spark detector tripping three times in ten minutes, and a burner temperature dropping below 700 degrees while fuel is still being fed, each with its frequency, severity, and the explanation and recommendation a responder sees.
Two of a plant's alarm rules. The right-hand column is written for the person who gets the alert at two in the morning.

Where this sits

Two ways into one platform, a layer that needs both, and control on top. Each is a complete product with its own payback. How the pieces fit.

  1. Start here

    Maintenance Management

    Koidra Maintenance Management brings work orders, preventive maintenance, asset records and parts inventory together in one mobile-first system. It is also the first step toward predictive maintenance and an AI-driven autonomous factory.

  2. Start here

    DataPilot

    Every signal the plant produces, in one place, live and historical.

  3. Needs both products

    AI Advisory

    Reports on what changed, answers in plain language, what to change to move the KPIs you set, and predictive maintenance: failures seen before they arrive.

  4. Autonomous control

    KoPilot

    Closed-loop control of the process, minute by minute, with no human at the panel.

Add Maintenance Management and AI Advisory becomes available. It needs both, and it comes with both.

See Koidra on your own plant data

Bring a week of readings from one line. We will show you what the platform sees, what it would have flagged, and what it would have scheduled.

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