AI lead intelligence pipeline for an investment firm

The founding team spent hours each week scanning news articles, LinkedIn profiles, and foundation websites to find qualified prospects. Research happened ad hoc, with no systematic coverage of available sources and no way to ensure the same ground was not being covered twice.

Contact information gathered through manual searches arrived incomplete and unverified. CRM entries often lacked phone numbers, verified email addresses, or company details, which weakened outreach efforts and forced follow up research that duplicated earlier work.

Qualification relied on intuition and limited public data rather than structured scoring criteria. The team had no consistent way to evaluate prospect fit or prioritize outreach, which meant strong opportunities sometimes sat unworked while weaker leads received attention first.

Configured automated LinkedIn discovery through Clay with advanced search filters covering location, company revenue, job titles, and industry. Scheduled weekly table refreshes ensure the pipeline surfaces new prospects continuously without manual intervention. Clay's waterfall enrichment runs 100+ data providers to verify emails, phone numbers, company details, and social profiles.

Built a parallel news monitoring system using Perplexity Sonar API that tracks M&A activity, funding announcements, board appointments, and major philanthropic donations across regional business media. Names and companies extracted from articles feed back into Clay for full profile enrichment.

Designed an AI scoring engine in n8n that evaluates each enriched lead against the firm's ideal client profile. Leads scoring above the threshold route to a Slack based review queue where the operations team approves or declines each prospect with a single button click. Approved leads populate Pipedrive with full enrichment data, source attribution, and score breakdown.