All case studies

AI agents Food supplements

A platform that analyses every label with AI and checks it against Spanish and EU regulations

A food-supplement company needed to know whether each product complies with Spanish and EU regulations before selling it. KANONIS reads the label with AI, a person reviews the reading and a rules engine checks every ingredient against the official registers.

  • 4 weeks from order to first version in production
  • +1,200 regulatory verdicts issued, each with its rule and its explanation
  • 12 automatic regulatory checks, each with an official source

The problem

Every food supplement must comply with a dozen rules: authorised ingredients, permitted chemical forms, maximum doses, mandatory warnings, allergens, additives, health claims. The sources are official registers written in legal language that does not match what a label prints.

Checking each product by hand takes an expert and hours per label, and a mistake means a recall or a fine.

The solution

AI reads the label and turns it into data, copying what is printed without interpreting it; a person reviews that reading; and a fixed rules engine checks every ingredient against the official registers (AESAN, Royal Decree 1487/2009 and the EU regulations). The result is a traffic light per ingredient and per market, with the rule and the explanation in every verdict. Anything absent from every register is flagged, not approved.

Twelve independent checks, an own traceable regulatory database, extendable to new markets.

How we worked

  1. 1

    Assessment

    What is checked today, with which sources and where a mistake hurts.

  2. 2

    Design

    A three-step flow (upload, review, evaluate) that keeps the person in charge.

  3. 3

    First delivery in production

    MVP in four weeks: regulatory database, search and label analysis.

  4. 4

    Extension

    New markets and new checks on the same base.

What we take away

AI adds value where it is good, reading a document, and steps aside where an error cannot be afforded. Separating the two is what makes the result reliable.

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