FARio makes flour quality measurable and predictable — pairing artisan domain knowledge with explainable AI to give millers and bakers actionable intelligence, every single day.
Bogged down by slow, lab-bound tools with poor predictive power, industrial mills and bakeries struggle to determine which flours will actually perform. The result: costly workarounds, avoidable waste, and reactive firefighting instead of innovation.
Today's measurements don't anticipate how flour will behave in practice.
Current tools don't measure flour and dough properties under conditions that reflect real-world usage giving misleading results, while consuming significant time and resources.
While a lab baking trial remains the ultimate test, it's seldomly in line with results from production requiring further line testing at high cost and risk.
Today's tools used at production level just aren't performing or heavily rely on expert knowledge at scale.
Sparse, lab-bound sampling leaves blind spots when needed most while creating disputes with suppliers
Results are collected after the facts due to current techniques' long lead times, reliance on trained lab technicians, and the need for expensive equipment. Sometimes, even no insights are gathered at all.
As such, expensive data is not contributing to controlling processing and preventing issues or operations is running blindly.
And as analyses can't keep up with flour production (at flour mills) and use (at industrial bakeries), data gets sparse leading to conflicting results when deviations appear. This shifts discussions from finding solutions to protecting procedures.
By the time you found a working solution, flour quality may have changed leading to fast but costly workarounds
Flour's defining role for product quality requires optimizing recipes and processes, but under industry's time constraints, the vast number of variables to consider, and with the limitations of current techniques, finding optimal solutions is challenging. As a result, safe solutions in the form of expensive ingredients are added or quality standards are relaxed, leading to increased costs and missed business opportunites.
Starting from either compositional or spectral analysis, an explainable core model turns raw signal into decisions you can act on, balancing all trade-offs you face.
Lab composition data using proprietary methods.
Decentralized spectroscopy at the source.
ExploreLeverages the physics in the core model to tailor actionable intelligence to your company.
ExploreFinal product quality features & decision support.
ExploreGet insights into our journey, how we build intelligent flour solutions and the impact they have. Filter by topic to find what's relevant to you.
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