Turn Every F&B Experiment Into a Reusable Asset
A practical guide for food and beverage R&D directors, formulators, and lab operations leads.
Food and beverage R&D teams are working harder but learning slower. The cause is not a lack of creativity. It is siloed data, disconnected systems, and manual processes that turn each experiment into a one-off event instead of a reusable asset.
This playbook shows how to move from manual data collection to a connected R&D platform: the practical workflows, illustrative ROI math, and a phased path to get there. It draws on teams like Ripple Foods, who centralized fragmented R&D data on Uncountable and cut time spent on data reconciliation by at least 30%, saving roughly half a day per scientist each week.
Structure your formulation, process, and quality data once, and everything downstream gets easier: faster iteration, fewer scale-up surprises, nutrition labels in minutes, and a foundation that makes AI in the lab actually work. Structure first, AI second.
What Will I Learn?
How to centralize formulation, process, and quality data so every experiment becomes a reusable asset, including the 5 signs you've outgrown your tools, an illustrative ROI calculator, and a phased implementation roadmap.
A platform that centralizes formulations and versions, ingredients and supplier data, process conditions, and sensory/stability/QA-QC results, structuring the data so it's searchable, comparable, and reusable across experiments. It includes the functionality of an ELN and LIMS while connecting R&D, QC, and product lifecycle data.
It includes ELN and LIMS functionality, but goes further: it's formulation-centric and connects R&D, QC, and product lifecycle data in one platform, which suits formulation industries where products change frequently and variants are common. By comparison, standalone LIMS systems can be overly rigid, while ELNs are primarily designed for lab workflows rather than end-to-end formulation development.
When formulation data is structured, nutrition labels are generated from the formulation record, part of the platform's PLM capabilities, turning a multi-day manual process into minutes, with sensory data linked to the same record.
Only once that data is structured. AI compounds learning across experiments, but it needs centralized, connected formulation and test data first. Structure data first, AI second.
