Tech stack: Built on Next.js/React, pulling data from the Ahrefs API and Open Food Facts API. We used OpenAI's API for the heavy lifting - deduplicating, aggregating, and categorizing 800,000+ products to build comprehensive profiles across 609 food additives. The data work was surprisingly tricky. We had to refine our Ahrefs methodology to use "matching terms" rather than "related terms" - otherwise we got flooded with irrelevant keywords (MSG search volume was massively inflated by "Madison Square Garden" ). Filtered for 100+ monthly searches to eliminate spam while keeping meaningful queries. The result: real search demand data paired with actual product usage patterns, so you can see both what people are curious about AND where additives actually show up in products. Happy to answer any technical questions!

Hi HN, Co-creator here, built this after getting frustrated trying to figure out which food additives to actually worry about when my kid started kindergarten. Scraped 817K+ products to see which additives are actually common, then compared that against Google Trends and Ahrefs data. Turns out there's a massive gap - some additives banned in Europe are in tons of products here but almost nobody searches for them. Not making health claims, just showing the data. The awareness score is basically: how much people search vs how common it actually is.

Data normalization was a nightmare - manufacturers call the same thing 10 different ways (E102, Tartrazine, Yellow 5, etc). Also, MSG is not most known for being a food additive apparently, etc. Would appreciate feedback on the methodology or if you spot any issues.