Nutrition Data Service addresses a hidden problem in AI workflows: similar food descriptions can refer to different releases, identifiers and contexts. When data identity is unstable, even a fluent analysis becomes difficult to reproduce.

The proposal uses description resolution, typed crosswalks and machine-readable interfaces to keep sources and versions explicit. In a person-level glycaemic-index analysis, pinned inputs produced identical outputs across models and runs, while open-web reconstruction was unstable.

The principle transfers to companies: ingredient catalogues, specifications, studies and internal data should be identifiable and versioned before an agent uses them. Retrieval and reasoning cannot compensate for an ambiguous document base.

Why it matters

For nutrition AI, result quality depends on source identity and repeatability, not model capability alone.

Evidence limit

A non-peer-reviewed preprint. Performance and generalisability require verification across different sources, countries and company workflows.

Editorial note. This content is intended for industry professionals. It does not constitute medical advice, therapeutic guidance or regulatory advice. Read our editorial method.

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