MyFoodFit helps people understand food choices against their dietary needs using a clear green / amber / red system.
Scan packaged foods to see how they align with dietary preferences. Each rating includes a short explanation and the rule used. The product is early-stage and focused on transparency over claims.
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Personal dietary scoring
Clear green / amber / red signals
Explainable rule-based logic
Built with transparency at its core.
Product scope (current stage)
- • Explainable food evaluation with rule-based logic
- • Preference-based scoring and green / amber / red signals
- • Structured data review and quality workflows
- • Contributor tools for rule refinement
- • Research datasets for academic use
- • Institutional APIs and integration pathways
Who MyFoodFit is for
People who want to check packaged foods against dietary needs without wading through complex labels.
Academic teams interested in transparent, rule-based approaches to nutrition information and label interpretation.
Universities, public-sector programs, and innovation groups seeking early-stage collaboration on food-label clarity.
Research & Collaboration
MyFoodFit uses transparent, rule-based logic rather than black-box recommendations, so reviewers can see how outputs are formed.
The product is designed to support understanding of food choices, not to diagnose, treat, or replace professional guidance.
The project is early-stage and open to academic, public-sector, and innovation partnerships.
What MyFoodFit does today
- Shows green / amber / red ratings based on stated preferences.
- Explains why a rating appears, with references to the underlying rule.
- Includes ingredients and nutrition facts with the rating for context.
What MyFoodFit does not do
- Does not diagnose, treat, or prevent any condition.
- Does not replace professional dietary advice.
- Does not adapt to medical history or clinical data in this beta.
Why we are building this
Food labels are dense and inconsistent. We are testing whether a transparent, rules-first approach can make label information easier to interpret without oversimplifying or promising outcomes.
Openness to collaboration
We welcome conversations with university innovation teams, public health groups, and research collaborators who want to evaluate the approach, review the rules, or co-design studies appropriate for an early-stage product.