Building an Influencer Vetting Pipeline with Two x402 APIs (Python)

When I started building an influencer vetting tool, I realized there are two distinct problems: Discovery — finding accounts that match your criteria (niche, location, follower count) Research — pulling data on specific accounts you've already identified Most tools conflate these. I've been building a pipeline that uses two different x402-native APIs for each stage, and it works surprisingly well. Stage 1: Discovery (who exists?) Social Intel is purpose-built for discovery. You give it a niche and filters, it returns matching Instagram accounts. import httpx # Find yoga micro-influencers in the US discovery = httpx . get ( " https://api.socialintel.dev/v1/search " , params = { " query " : " yoga instructor " , " country " : " United States " , " gender " : " woman " , " followers_min " : 10000 , " followers_max " : 100000 , " limit " : 20 } ) candidates = discovery . json (). get ( " results " , []) # Returns: username, followers, category, city, email, is_verified # Price: $0.10 USDC