Control of Ingredient Detection to Reduce Food Waste

Objective
To minimise production errors and reduce food waste caused by incorrect ingredient combinations in processed food manufacturing.

How it works
The system analyses existing production records, recipe data and operational logs to identify patterns associated with defective outputs. Predictive and anomaly detection models flag potential inconsistencies, allowing corrective action before large volumes are affected.

Core digital services
We specialise in integrating production datasets from ERP or manufacturing systems and transforming them into structured analytical environments. Our services include training anomaly detection and predictive models, validating their performance, and embedding them into decision-support dashboards that can be used by production teams without technical expertise in AI.

How they apply to the agrifood sector
Food processors often generate significant operational data that remains underutilised. By analysing this information systematically, manufacturers can reduce raw material losses, improve production accuracy and enhance quality assurance processes without investing in additional hardware.

Type of agrifood partner sought
We are looking for food processing companies and industrial manufacturers that already collect digital production data and aim to improve efficiency, reduce waste and strengthen quality control through advanced analytics.