Artificial intelligence helps plants in Mexico produce with fewer stoppages and less waste: it anticipates demand, predicts failures before they stop the line and catches quality defects as they happen. At Corsoft we connect these models to the data your operation already generates.
A stopped line costs by the minute. Predictive maintenance learns from sensors and failure history to warn you before equipment breaks down.
Without a good forecast, planning swings between idle inventory and missed orders. Demand models give you numbers to produce what will actually sell.
Defects caught at the end of the line have already cost material and time. Early detection from process data cuts scrap and rework.
Production, quality and maintenance live in separate sheets someone consolidates by hand. Automation brings it together so you can see the whole operation.
If OEE and KPIs arrive days later, it's too late to correct course. Dashboards connected to your operation tell you today what happened today.
Tell us about your operation's challenge and we'll come back with a clear plan, timeline and quote — usually within one business day.
No. Most projects start with what you already have: production records, maintenance logs and Excel sheets. If instrumenting the line makes sense later, we tell you exactly what to measure and why.
Yes. We connect to systems like SAP, Odoo or in-house software, as well as databases and flat files. The model feeds on your data wherever it lives today.
A demand forecast or an operations dashboard is usually running within weeks. Predictive maintenance depends on the failure history available, but typically shows value in the first quarter.
That's the norm, not the exception. Cleaning and structuring the data is part of the project — and that work stays with you as an asset, along with the model.
No: it takes the repetitive data-wrangling off their plate and gives them better numbers to decide with. The decisions stay with your team.