Recall Shadow

A product recall does not make the product disappear from the internet. Product-safety and compliance teams still need to find potentially recalled listings online and determine whether each one is actually the affected product. Recall Shadow turns an official recall into a structured product identity and uses Bright Data to investigate potential listings across the live web. Each candidate is examined individually with AI, separating supporting, contradictory, and missing evidence rather than relying on a similarity score alone. The key differentiator is adversarial self-challenge. Before presenting its recommendation, the AI actively searches for evidence that could contradict its own conclusion. This makes the reasoning inspectable instead of reducing the investigation to a confidence score. The AI recommendation is never the final decision. A human reviewer can classify the candidate, choose the appropriate follow-up action, add context, and record the decision as part of a traceable review history. Recall Shadow was built primarily with native.builder, with Bright Data for live web discovery, Gemini through AI/ML API for evidence reasoning and self-challenge, Supabase for persistence and authentication, and Vercel for public deployment. The product is designed for product-safety teams, manufacturers, marketplaces, and compliance teams. Its potential business model is enterprise SaaS with API integrations for larger workflows. The longer-term vision is to evolve from investigating individual recalls into continuous product-safety intelligence that can detect when recalled products reappear online.