Methodology & Trust

How Aurascan decides what to tell you.

No black box. Every verdict starts from trusted product data, is researched ingredient by ingredient against authoritative sources, and carries the citations behind it. Here is exactly how it works - and where we draw the line on what we’ll claim.

Universal coverageVerifiable citationsDeterministic resultsNo sponsors

From barcode to verdict

Five stages turn a number on a package into something you can act on. Each one is auditable.

  1. 01

    Scan the barcode

    You point your camera at any packaged-food barcode. That number - a GTIN - is the only thing we need to start.

  2. 02

    Resolve the product

    We look the product up across Open Food Facts, USDA FoodData Central, and a barcode database, and merge what each one knows into a single set of facts.

  3. 03

    Research each ingredient

    Every ingredient is researched individually against authoritative sources. Each claim we surface carries the citations behind it.

  4. 04

    Verify the citations

    A verifier checks each cited authority against the sources actually retrieved. Anything it can't tie to a real source gets rewritten in neutral terms or removed - never left implying false authority.

  5. 05

    Deliver a transparent verdict

    You get plain-English findings scoped to how the ingredient actually reaches your body, tiered by how often you eat it - never a black-box grade.

Universal coverage

No dead-ends. No “untested.”

Some tools stop cold when a product isn’t in their hand-curated list. Aurascan researches every ingredient on demand, so a scan returns a real answer instead of a shrug. When the evidence is thin or missing, we say so plainly - which is different from having nothing to show you.

Where the product data comes from

We combine four sources and keep the best field from each. Every scan credits only the sources that actually contributed to it - not a generic list.

  • Open Food Facts

    ODbL

    A collaborative, open database of packaged foods. We use it for standardized additive, allergen, and processing tags, plus product identity.

  • USDA FoodData Central

    Public domain (CC0)

    The U.S. government's authoritative branded-food dataset. When it has a product, its label ingredients and nutrition numbers take precedence.

  • Go-UPC

    Licensed

    A barcode lookup that fills the long tail - product name, category, and image for items the open datasets haven't catalogued yet.

  • Community submissions

    User contributed

    When a shopper submits a label we don't have on file, that contribution is credited too - and clearly marked as community-sourced.

How we research each ingredient

AI does the research - in the open

Each ingredient is researched by a large language model (Google’s Gemini) with live web grounding. We’re deliberately transparent about that: other tools either bury the fact that AI is involved or hide their methodology entirely. We’d rather show you the machinery and the sources, so the research is something you can check - not something you have to take on faith.

Every citation is checked - and false authority is stripped out

A claim without a source is just an opinion. When the research says “according to the FDA,” a verifier checks that an FDA source was actually retrieved to back it. If it can’t tie the authority to a real source, it rewrites the phrase to a neutral one (“according to research”) or drops the sentence entirely - never leaving language that implies an authority we can’t point to.

The allowlist is tiered, with regulators and health agencies ranked above journals: FDA, EFSA, the EU scientific committees, CIR, EWG, IARC, JECFA (WHO/FAO), NIH/PubMed, EMA, MHRA, and Health Canada, plus peer-reviewed hosts like Nature, The Lancet, and the BMJ.

When there’s no good evidence, we say so

The most important thing we do is refuse to make things up. When an ingredient has no authoritative sources, or the research can’t be grounded, we return an unresolvedresult with the reason - not a confident-sounding guess. A blank we’re honest about beats a verdict we can’t stand behind.

Not all evidence is equal

A claim backed by a randomized human trial is not the same as one backed by a mechanistic hypothesis. We tag research by its evidence tier so you see not just what we know, but how well we know it.

  • Tier 1

    Human clinical trials

    Highest confidence

    Randomized controlled trials in humans - a direct measurement of cause and effect in real people. When available, this is what we cite first.

  • Tier 2

    Human observational studies

    Strong but imperfect

    Prospective cohorts and case-control studies. Real people, real outcomes, but correlational - we can measure what happens, not always prove why.

  • Tier 3

    Animal studies

    Suggestive

    Controlled studies in animal models. Directional evidence - useful for mechanism, but not conclusive for humans.

  • Tier 4

    Mechanistic hypotheses

    Exploratory

    In-vitro experiments or plausibility-based mechanisms without in-vivo data. Interesting signals, but we flag these so you know the evidence is thin.

Two independent signals

A verdict draws on two channels that don’t depend on each other, so a gap in one doesn’t silence the other.

Deterministic regulatory data

A hand-curated regulatory seed - FDA-prohibited substances and EFSA/IARC-cited additive flags. The same inputs produce the same signal every time, with no model in the loop.

Includes genotoxicity classifications from EFSA OpenFoodTox 3.0, licensed CC-BY-4.0.

Grounded AI analysis

The cited, evidence-tiered research above - covering the long tail of ingredients no fixed table could ever fully enumerate.

Benefits and concerns are independent

An ingredient can carry a benefit and a concern at the same time. We don’t collapse them into one number that cancels the nuance out.

Scoped to how you’re exposed

A concern about inhaling something doesn’t apply to eating it. For food, we scope signals to the way it actually reaches your body.

Dose matters

“Should you care?” is tiered by how often you eat it - an occasional treat and a daily habit are not the same question.

Deterministic by design

The same product, the same answer.

AI is famously non-repeatable - ask twice, get two answers. We engineer that out. Every analysis is cached against the product’s normalized barcode, the methodology version it was produced under, and your profile. Scan the same product again and you get the identical result, byte for byte, until we explicitly publish a new methodology version and regenerate. When the science changes, we bump the version on purpose - results never drift on their own.

1:1

same product + profile, same result

GTIN-14

normalized barcode in the key

Versioned

every result is stamped

Independence pledge

The only thing that shapes what we tell you is the evidence. Nothing else is for sale.

  • No paid placements

    No brand can pay to change, soften, or hide what we say about its product. There is no rate card for a better result.

  • No ads, no affiliate cut

    We don't run ads and we don't take an affiliate commission that could bias which products we favor.

  • Industry funding counts against a source

    When research is funded by the maker of the thing it studies, that lowers our confidence in it - it never raises it. Sponsorship is a penalty, not a boost.

  • Funded by subscriptions

    Our revenue is subscriptions through the App Store, Play Store, and Stripe - not the brands we analyze. Our incentive is to be right for you.

Where we draw the line

  • AI can be wrong. Research is a starting point, not the final word. We show sources and flag weak evidence so you can judge for yourself.
  • This is not medical advice. Aurascan is educational and is not intended to diagnose, treat, or replace guidance from licensed clinicians or nutrition professionals. If an ingredient interacts with a medication you take, talk to a doctor - not a phone app.
  • Found a mistake? Tell us. Every ingredient has a report-a-problem path, and every result is stamped with the methodology version behind it - so a correction is traceable, not lost.

Frequently asked

  • Does AI write the analysis?

    Yes, and we're upfront about it. Each ingredient is researched by a large language model (Google's Gemini) with live web grounding, then every claim is checked against an allowlist of trusted sources. We show you the citations so you can verify the research yourself, rather than asking you to trust a hidden model.

  • What happens when there's no good evidence?

    We tell you. If an ingredient has no authoritative sources, or the research can't be grounded, we return an unresolved result and say why - we never invent a verdict to fill the gap.

  • Will the same product give the same result every time?

    Yes. Results are cached against the product's normalized barcode, the methodology version they were produced under, and your profile, so a given product returns the same analysis for the same profile until we explicitly publish a new methodology version.

  • Do you give foods a single score or grade?

    No. A single letter or number hides the reasoning and treats one bite the same as a daily habit. We show what each ingredient does, scoped to how it reaches your body and how often you eat it, and let you decide.

  • Can the analysis be wrong?

    It can. Science evolves, databases have gaps, and AI can make mistakes. That's why we show our sources, flag weak evidence, stamp every result with a methodology version, and give you a way to report a problem.