Bertrandt and Bosch Rexroth have agreed to combine industrial machine vision and artificial intelligence with factory automation, the two companies announced in a joint collaboration.

Under the partnership, evopro systems engineering AG — a subsidiary of Bertrandt AG — contributes its machine vision and AI expertise to Bosch Rexroth's open automation platform, ctrlX AUTOMATION. Both companies position the move as a step toward faster digitalization of industrial processes.

Who brings what to the partnership?

Bosch Rexroth supplies the automation side. Its ctrlX AUTOMATION platform is built as an open system: rather than locking users to one vendor's toolchain, it lets third-party developers add their own software modules, apps and hardware to the control environment.

Bertrandt supplies the perception side, through evopro systems engineering AG. The subsidiary has built its business on industrial machine vision — camera-based inspection, measurement and process monitoring — and on applying AI models to that image data on the factory floor.

The collaboration effectively pairs the two: vision-based intelligence delivered as a capability inside the same platform that runs motion control, PLC logic and other automation tasks.

Why does openness matter here?

The technical significance lies in where the vision and AI code runs. Traditional machine vision systems often sit in standalone inspection cells, with results passed downstream to the automation controller over fieldbus or Ethernet links. That separation adds latency, wiring and integration effort.

An open platform such as ctrlX AUTOMATION changes the integration model. Third-party modules from companies like evopro can run alongside — or in the same environment as — the control software itself, closing the gap between what a camera sees and what a machine does. For quality inspection gates, robotic guidance and adaptive process control, that architectural choice can decide whether a vision result arrives in time to act on it.

What applications does the pairing target?

The companies frame the collaboration around digitalization of industrial processes broadly. In practice, combining machine vision AI with an automation platform points at a familiar set of use cases:

• inline quality inspection integrated with production control
• AI-assisted image evaluation replacing manually programmed vision rules
• process monitoring in which camera data feeds automated decisions directly

Neither company has yet published performance figures, supported camera interfaces, or model deployment details for the combined offering. Buyers evaluating the partnership will want those specifics — accuracy on representative inspection tasks, inference latency within the control loop, and certification status of any AI models used for safety-relevant checks.

What remains open?

The announcement defines the scope of the cooperation but not its deliverables. No launch dates, product names, pricing or reference installations accompany the initial statement.

For metrology and test engineers, the question the partnership raises is one of verification: when AI-based vision moves from standalone inspection cells into the real-time automation stack, how will accuracy, repeatability and traceability of those AI decisions be documented and validated on the production line?

By Grace Kim