AI prospecting workflow
Build an AI prospecting pipeline from live public web data
Published: 2026-08-11
Static lead databases age quickly. A stronger prospecting workflow starts with a live reason to care—new hiring, a funding announcement, a target market, or a known account—then adds company context and traceable public business contact data before an AI model ranks or summarizes anything.
Use these Fetch Cat Actors as modular steps. Run only the lanes that match your market; they produce separate datasets that you join downstream by company URL, website domain, or company name after review.
1. Discover accounts from a buying signal
Hiring signal: LinkedIn Jobs Scraper exports public jobs by keyword, location, freshness, workplace type, and LinkedIn search URL. Look for roles that indicate an active initiative, then keep the job URL and description so an AI summary can cite the source.
Funding signal: Startup Funding Rounds Monitor turns recent public funding announcements into normalized events with company, amount, round type, publication date, source URL, evidence text, and confidence.
Local-market discovery: Google Maps Email Extractor & Lead Finder searches business categories by location and can return listing details, website, public phone, public website email, ratings, reviews, and lead score.
2. Add firmographic context
Feed exact LinkedIn company URLs or slugs into LinkedIn Public Company Profiles Scraper. It exports identity-checked public company records including website domain, industry, employee range, headquarters, specialties, followers, and provenance.
3. Find traceable public business contacts
Pass the resulting website domains to Website Contact Finder. It crawls bounded public pages and returns email, phone, and social-link records tied to source URLs, with confidence signals and optional MX checks.
4. Rank and summarize with AI
Join records on normalized website domain where possible. Ask the model to score accounts against an explicit ideal-customer profile and return:
- the triggering event or discovery reason;
- firmographic fit and disqualifiers;
- public contact channels and their source URLs;
- a short research summary with citations;
- a
needs_reviewflag when identity, recency, or contact evidence is weak.
Rank each account from 0–100 against our ICP. Use only supplied fields. Cite the funding article, job URL, company profile, and contact source URL behind each claim. Do not invent employee names, roles, email addresses, or buying intent. Mark missing or conflicting evidence for human review.