B2B Search Layer

A GTM engineer builds a search layer that queries 20 ICP-matched searches daily on Google, checks Reddit for each discovered company, and applies negative filters (exclude consultancies, agencies, pre-seed startups). From 200 raw results, the layer surfaces 30 qualified leads per day. Hit rate after negative filtering: 62%, up from 28% without filtering.

B2B Search Layer is relevant to Google, Reddit. Scavio provides a unified API to access data from all of these platforms.

Traditional B2B prospecting relies on databases (Apollo, ZoomInfo, LinkedIn Sales Navigator) that charge per-contact and go stale between updates. A search layer supplements or replaces these by querying Google for company signals in real time: job postings (hiring signal), technology mentions (stack fit), funding announcements (budget signal), and customer complaints on Reddit (pain-point signal). The key insight from practitioners: negative filtering (excluding poor fits) improves hit rates more than expanding positive keywords. A search layer that filters out consulting firms, agencies, and companies below 10 employees from a 'fintech hiring data engineer' query can improve qualified lead rate from 30% to 60%+. Implementation: daily cron job queries Google for ICP-matched search terms, Reddit for company sentiment, then applies negative filters and scores results. Scavio covers both Google and Reddit under one API key at $0.005/query. Apollo charges $49-119/mo for similar company data but without the real-time search signal.