Glossary

B2B Search Layer

A B2B search layer is the search infrastructure component of a sales pipeline that programmatically discovers target companies from SERP data, enriches them with web and community signals, and filters out poor fits before human review.

Definition

A B2B search layer is the search infrastructure component of a sales pipeline that programmatically discovers target companies from SERP data, enriches them with web and community signals, and filters out poor fits before human review.

In Depth

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.

Example Usage

Real-World Example

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.

Platforms

B2B Search Layer is relevant across the following platforms, all accessible through Scavio's unified API:

  • Google
  • Reddit

Related Terms

Frequently Asked Questions

A B2B search layer is the search infrastructure component of a sales pipeline that programmatically discovers target companies from SERP data, enriches them with web and community signals, and filters out poor fits before human review.

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.

B2B Search Layer

Start using Scavio to work with b2b search layer across Google, Amazon, YouTube, Walmart, and Reddit.