Our Scoring Methodology

Every Checkit score is powered by AI analysis of real ingredient data, cross-referenced against international food safety databases.

The Checkit Score (0-100)

Scoring Factor Breakdown

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AI Analysis Pipeline

  1. Ingredient extraction and normalization
  2. Database cross-reference (FDA, EFSA, Health Canada)
  3. AI safety scoring via GPT-4o-mini
  4. Weighted score computation
  5. Quality validation and anomaly detection

Data Sources

FAQs

How is the Checkit health score calculated?

The score (0-100) analyzes ingredients against six weighted factors: harmful additives (30%), seed oils (15%), sugar/HFCS (15%), processing level (15%), nutritional balance (15%), and allergen transparency (10%).

What data sources does Checkit use?

We use OpenFoodFacts, USDA FoodData Central, FDA recall databases, and international regulatory authorities including EU EFSA and Health Canada.

How often is scoring data updated?

Our database updates continuously through automated pipelines. FDA recalls are monitored in real-time.

Can I trust the score for allergy decisions?

The score flags all Big 9 allergens and cross-contamination warnings, but always read the full label for severe allergies.

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📋 Cite This Data
APACheckIt AI. (2026). "How Checkit AI Scores Food Products — Our Methodology". Climaverse PBC. Retrieved from https://getcheck.it/methodology
MLA"How Checkit AI Scores Food Products — Our Methodology." CheckIt AI, Climaverse PBC, 2026-04-17. https://getcheck.it/methodology.
HTML Embed<a href="https://getcheck.it/methodology">How Checkit AI Scores Food Products — Our Methodology — CheckIt AI</a>
BibTeX@misc{checkit2026methodology, title = {How Checkit AI Scores Food Products — Our Methodology}, author = {CheckIt AI}, year = {2026}, publisher = {Climaverse PBC}, url = {https://getcheck.it/methodology}, note = {Retrieved 2026-04-17} }