Based on polymorphic technologies. A modular, extensible and adaptable Progressive Web App

A standard module can partially correspond to a business process. It is possible to adapt standard views or functionalities by developing tailor-made variants. Only the variant part concerned is rewritten according to customer requests

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Recover and process electrical signals, in order to allow the TALKME solution to reduce energy and consumable consumption, and increase productivity

Using current measurement sensors placed at different locations on the machine, we collect current curves. Using machine learning (AI), we can understand how it works and deduce normal operating cycles.

This non-intrusive approach allows us to equip all types of machines, whether motorized or electrically consuming, in order to draw up a behavioral profile based on this consumption.

By comparing these operating cycles with production orders (PO) in normal operation, we can monitor production and detect abnormal behavior. Thus, we are able to stop production of a part or trigger an alert in the event of abnormal behavior



The production order proposal in the schedule is today based on a few criteria (opening time and theoretical time of scope of the item)

The objective is to obtain a planning suggestion based on multiple known and/or unknown criteria (machine data, electrical signals, customer urgency, material supply, production history, etc.), and on a new relationship model between entities (machines, AI, algorithm)

The challenge of our research and development program is to provide real support and assistance to teams to relieve them and enable them to develop their skills by working on tasks with higher added value