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Uh Oh!
The internet is usually chaotic. Occasionally, it is also early. Before a recall becomes official, someone is already leaving clues: a person writing “my boyfriend broke out in hives after our wedding day,” a runner wondering why their supplement made their heart race on an empty street, a review mentioning a weird smell, a broken seal, or a “nut-free” snack that suspiciously tastes like almond. To most teams, that is noise. To Uh Oh!, it is the beginning of a signal. Uh Oh! is an early-warning product safety radar for food and supplement teams. We use Bright Data to scan live public web signals across reviews, forums, marketplaces, brand pages, and product listings, then correlate them with FDA/openFDA enforcement data and CAERS adverse-event reports. AI extraction turns the messy internet into structured product identity, issue clusters, severity cues, source summaries, and recommended next actions using AI/ML API. The output is a Product Safety Case File: a source-backed packet for quality, legal, marketplace trust, brand risk, and compliance teams. Each file includes official recall correlation, CAERS signal summaries, a live web evidence wall, since-last-scan deltas through Cognee memory, and a transparent 4-factor risk score across regulatory, adverse-event, live-web, and credibility signals. When the evidence reaches “Review Needed,” Triggerware opens a quality review case so human teams can investigate faster. Uh Oh! is not a public frenzy enterprise. It does not decide whether a product is unsafe, and it does not pretend that reviews, forums, or CAERS reports prove causality. It is a responsible triage layer for the moment before the obvious, when the internet is saying, “something might be wrong.” Our demo shows the most valuable moment: a product that is not officially recalled yet, but whose live web signals are starting to look like risk. We’re doomscrolling the internet so product safety teams don’t have to.
31 May 2026
