Equinom | Managing Wheat & Flour Variability for Higher Profitability

Manna™ predicts how wheat grains and flour will behave in real production

Manna™ delivers fast, lot-by-lot functional insight by using a standard NIR device, data the miller and baker collect but often don't use, and AI and machine-learning models trained on real outcomes.

In seconds, users understand how a lot of wheat grains or flour will function before it enters production.

Wheat and flour variability are a given Uncertainty doesn't have to be

Wheat changes by type, region, season, and climate. Even flour that meets spec can behave unpredictably in real production - driving conservative blending, water adjustments, mixing drift, and yield instability.

Production continues. Margins shrink.

Specifications describe what wheat or flour is. They don't predict how it will perform.

How it works

Scan. Read. Act.

From NIR scan to operational decision in seconds. Built for quality managers and production teams — not data scientists.

Scan

Capture Full-Spectrum Data

Capture full-spectrum NIR data at intake, in the mill, or on the bakery floor. Millers use their existing NIR device. Bakeries without NIR can get started immediately using a compact handheld device from Equinom.

REad

AI Classifies Functional Behavior

AI models classify lots by expected performance behavior.
For millers, that means connecting spectral signals to wheat quality, blending behavior, and flour consistency. For bakers, it means predicting the adjustments needed - water levels, mixing time, and more - before production begins.

Act

Clear Operational Guidance

Clear operational guidance for the decisions that matter: wheat intake and silo allocation for mill operators; water and mixing adjustments for bakery teams. Built for quality managers, procurement leaders, and production staff - not data scientists.