About FoodPack AI

Hybrid decision support

A knowledge-based rule engine with cited rules, physics models for gas exchange and moisture gain, a sourced material database and transparent weighted ranking. A machine-learning model predicts a polymer's O2 and CO2 permeability from its structure; it screens films that are not in the database and cross-checks sourced values, and never chooses the packaging.

Where is the AI in this?

The machine-learning model predicts a polymer's oxygen and carbon-dioxide permeability from its chemical structure. It was trained on published measurements for several hundred polymers and tested on polymers it never saw, where it clearly beats a baseline that ignores the chemistry. The product uses it to screen films that are not in the database and to cross-check sourced values, always labelled as a model prediction with its range. It never overrides a sourced value and never chooses the packaging.

Principles

1. 01No number without a source or a stated rule.
2. 02Missing data stays missing; it is never filled from memory.
3. 03Every value carries its epistemic class, from measured to illustrative.
4. 04Decision support for a person to check, not a certification.