Loading...
What each of the 8 sources is best at, when the ICP router picks it, what it cannot do, and typical per-query yield numbers.
Lead Discovery dispatches up to 8 hunters per query. The ICP Router decides which subset to dispatch based on your prompt's geography + ICP shape. Here's what each source is good at and when the router picks it.
Best for: CTOs, engineering leads, principal engineers, technical founders, indie hackers.
How it works: Queries the public api.github.com/search/users endpoint with location + bio filters (e.g. location:bangalore "founder" in:bio). Then hydrates each match with the full profile to get name, bio, company, email (when public), Twitter handle, blog URL.
Yield: 30-100+ candidates per query for tech ICPs. Cap is 500 per Strategist query, but typical realistic yield is lower because we drop bare-name profiles.
Router picks it when: Your prompt mentions tech roles (CTO, Founder, Engineer), tech stacks (SaaS, fintech, AI), or implicit tech ICPs.
What it can't do: Find non-technical roles (sales, marketing, operations) — those people aren't on GitHub. Find leadership at large enterprises (they don't bother with GitHub bios).
Best for: Funding announcements, product launches, leadership changes, M&A, partnership news.
How it works: Pulls 22 globally-distributed RSS feeds (TechCrunch, VentureBeat, Sifted, Tech in Asia, KrAsia, TechCabal, Contxto, YourStory, Inc42, Entrackr, MoneyControl, ET-Startups, etc.). Filters articles by token-overlap with your prompt. Each matched article cascades through a Bedrock extractor that pulls company → website → leadership → emails.
Yield: 5-30 candidates per matched article × ~5-25 matched articles per query = 25-700 candidates worst-case. Usually capped at 25 articles to keep Bedrock cost predictable.
Router picks it when: Your prompt has any funding / launch / acquisition signal, or any prompt where current-month events matter.
What it can't do: Surface stable / mature companies (no news = no signal). Find people at companies whose websites don't have a Team page.
Best for: Founders self-identifying in build-in-public posts, indie hackers, side-project owners.
How it works: Searches r/IndianStartups, r/SaaS, r/startups, r/Entrepreneur, r/india, r/IndiaInvestments, r/developersIndia (subreddit list extends per geography). Each matched post runs through a Bedrock classifier that decides "is the author actually a founder/operator?" — comments, hiring posts, and theory-discussions are filtered out.
Yield: 5-30 candidates per query. Lower than other sources because Reddit founders are a smaller cohort.
Router picks it when: Your prompt is tech-founder / tech-engineer / SMB-build-in-public-flavored.
What it can't do: Find established executives (they don't post on Reddit about their day jobs). Find non-English speakers (most relevant subreddits are English-language).
Best for: SMB / local services with a physical address — dentists, gyms, restaurants, lawyers, schools, retail.
How it works: Queries OpenStreetMap's Overpass API for POIs matching the category + city in your prompt. Geocodes the city via Nominatim — works for any city worldwide. Pulls phone, website, email, opening hours, address, and accessibility tags.
Yield: 50-150 candidates per query in tier-1 metros (Berlin, NYC, Tokyo, Sydney). 20-60 in tier-2 cities. Lower in remote regions.
Router picks it when: Your prompt mentions a physical-business category + a city ("dentists in X", "law firms in Y", "coffee shops in Z").
What it can't do: Find online-only businesses. Find founders/owners by name (OSM tags businesses, not people). Coverage in rural areas is patchy.
Best for: SMB phone + email enrichment when OSM doesn't tag a category, or for non-Western markets where OSM coverage is thin.
How it works: Bedrock identifies the best public directory for your prompt's geography (Yelp for US/UK/CA, Pages Jaunes for France, 11880 for Germany, Sulekha/Justdial for India, Cylex/Kompass for cross-border, plus 10 more — and discovers new ones at runtime for countries we haven't pre-listed). Then runs SERP site:directory-domain category city queries through the scraper-pool and parses listings.
Yield: 10-30 candidates per query. Lower than OSM but complementary — we get phone numbers OSM didn't have.
Router picks it when: SMB or B2B-services ICP, especially when the geography is non-English-speaking.
What it can't do: Cover every country with a hand-tuned parser — generic SERP-title parsing is less precise than OSM's structured data.
Best for: Any role at any company with a public LinkedIn profile.
How it works: Issues site:linkedin.com/in/... queries through the scraper-pool's DuckDuckGo or Bing endpoints. Extracts name + title + company from SERP titles and snippets — never hits LinkedIn directly.
Yield: 30-50 candidates per query when SERPs are responsive. Hits anti-bot windows occasionally — when that happens, the rest of the source mix carries the run.
Router picks it when: Almost always (LinkedIn is universally relevant) — but rarely as the dominant source.
What it can't do: Get email or phone (LinkedIn hides those). The cascade resolver sometimes finds them via the company website, but not always.
Best for: Funded startups and their founders.
How it works: site:crunchbase.com/person/... SERP queries; same proxy pattern as LinkedIn.
Yield: 5-20 candidates per query. Crunchbase indexes funded companies, not the long tail.
Router picks it when: Funded-startup / Series-A-D ICPs.
Best for: Generic open-web search complement; finds blog posts, founder essays, conference speaker pages, podcast guests.
How it works: Generic SERP without a site: filter; cascade resolver extracts companies + people from the discovered pages.
Yield: 5-15 candidates per query.
Router picks it when: Almost always, as a low-signal complement.
Each hunter consumes a few seconds of wall-clock and Lambda spend. Running 8 hunters when only 3 are relevant slows the swarm without improving yield — and rate-limits some sources for the next user. The router optimizes for yield per minute across the source mix.
Tags
Lead Discovery: How it Works
How the agent swarm finds 100s-1000s of qualified leads from any country in 60-180 seconds. ICP Router, Source Discovery, and Cascade Resolver explained.
Writing Great Lead-Discovery Prompts
The 3-part formula for great prompts (Role + Industry + Geography), examples by ICP shape, common mistakes, and iteration tips.
Reading the Signals
What each signal type means, where it comes from, and how to quote signals in your outreach for warm-context first emails.