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Signals are delivered through role-based dashboards designed for the maintenance and reliability team.

Operational roles such as engineers, planners and supervisors receive actionable signals they can directly follow up on. Each signal includes the relevant context from the ERP system, such as work order information, financial data and action logs.

Maintenance managers receive a higher-level view that shows how signals are being handled across the organization. They can see how many signals were generated, how many were acted upon, rejected or still open, providing visibility into how operational risks are being managed.

This allows operational teams to act quickly, while managers gain clear insight into the effectiveness of the response.

Not exactly.
Instead of predicting failures from sensor data, the AI analyzes operational maintenance data to detect blind spots in daily maintenance processes.

Implementation is lightweight and does not require a live connection to your ERP system.

The AI works with periodic snapshots of maintenance data from your ERP or CMMS, typically a few times per week. In many cases this can simply be done through exports of key tables.

Because the system analyzes existing operational data, it does not interfere with current workflows. Maintenance teams continue working in their ERP system as usual.

The AI agents are configured to prioritize quality over quantity of signals, avoiding notification overload. During the initial phase the system is carefully tuned, while the AI continues to learn and improve in the background.