How to Build an Investor List With Claude That Investors Actually Answer

1. Key Themes

Investor lists decay rapidly and most fundraising failure is a data problem, not a pitch problem

• "Ask a founder six weeks into a raise why replies are at 2%, and they'll show you the deck. They'll rarely show you the list, and the list is usually the problem." The article emphasizes that "Investor lists rot faster than any other asset in fundraising" and that "Run any bought list or $29 template through real verification and something like half of it evaporates."

Effective targeting requires multi-dimensional scoring, not single filters

• The article proposes scoring each prospect on three dimensions — Fit, Recency, and Reach — each 1-5, and warns against oversimplified filtering: "One weak number caps the row, which is exactly what a single pass/fail filter or one blended score hides." It further explains: "Fit is what their last ten checks say, never what their site says" and "Recency belongs to the person, not the firm."

Cost-efficient list-building follows a strict layering sequence: curate first, scrape to fill gaps, enrich and verify last

• "The economics only work in this order. Every layer runs on fewer rows than the one before, so the expensive steps (enrichment credits, Claude tokens) never touch rows that a cheaper filter would have killed." And: "Sourcing wide is nearly free; enriching wide is how a $60 run becomes a $600 one."

Precision in defining your raise thesis multiplies downstream efficiency

• "Narrow beats broad everywhere downstream: a spec for 'seed AI infra in Europe, partners only' produces a cheaper, cleaner run than 'tech investors' ever can, and every layer below filters against it."

2. Contrarian Perspectives

• Curated/existing lists beat scraping as a starting point, contrary to the "scrape everything" mentality in growth-hacking circles. "Here is where this system disagrees with the scrape-everything school: the cheapest high-quality rows are ones someone already curated." The author argues scraping should only be used "at the edges: the niche the curated lists don't cover, the region nobody mapped, the angels who exist only as LinkedIn profiles."

• SEC EDGAR Form D filings are an underused but superior signal for identifying active investors, better than social/website signals most founders rely on. "SEC EDGAR Form D filings for anything US-facing: free, public, searchable through EDGAR's full-text search, and the best dry-powder signal there is, because a fund that closed capital recently is a fund that has to deploy. Almost nobody uses it, which is part of why it works."

3. Companies Identified

• Claude / Claude Code (Anthropic) — AI coding/agent tool used to build the list-scoring and verification pipeline.

  • Why mentioned: Central to the entire system described; used for verification, deduplication, and scoring stages.
  • Quote: "What sits behind the paywall is the machine that does it for you: a Claude Code project that takes your thesis and hands back a scored, deduplicated, verified list."

• Apify — Web scraping platform.

  • Why mentioned: Recommended as a tool for LinkedIn profile scraping in the sourcing layer.
  • Quote: "A LinkedIn-profile scraper (Apify hosts several) filtered hard by region and investor-only titles."

• Crunchbase (referenced generically as "Crunchbase-style APIs")

  • Why mentioned: Used for enrichment — pulling deal history to detect stale or inaccurate investor listings.
  • Quote: "A deal-history enricher (Crunchbase-style APIs) pulling each fund's last deals and roster. This is what catches the person who left the fund three years ago with the LinkedIn title intact, which is the single biggest source of list rot."

• SEC EDGAR — US government filing database.

  • Why mentioned: Highlighted as a free, underused data source for identifying investors with fresh capital to deploy.
  • Quote: "the best dry-powder signal there is, because a fund that closed capital recently is a fund that has to deploy."

4. People Identified

• Ruben Dominguez — Author of the article, writer at The VC Corner.
  • Why mentioned: Byline author; presumably the newsletter's founder given the "ruben@thevccorner.com" footer.
  • Quote: No direct substantive quote attributed beyond authorship; article credited to "Ruben Dominguez."

5. Operating Insights

• Build a written thesis spec (thesis.md) before touching any tooling — defining company, stage, region, investor type, roles, required fields, and disqualifiers up front prevents wasted enrichment spend downstream: "Every run starts from a spec, and the spec does more work than any scraper."

• Segment outreach intensity by composite score — Tier 1 (12+ combined score) gets individualized outreach, 9-11 gets a sequence, below 9 goes to a passive quarterly update list: "Score each 1 to 5. Twelve or more combined is your Tier 1, written to individually. Nine to eleven gets the sequence. Below nine goes on the quarterly update list."

• Structure the pipeline as one agent per stage in a single project folder for debuggability: "One folder, one agent per job, so when row 2,847 comes out wrong you know exactly which stage broke."

6. Overlooked Insights

• Scraping LinkedIn carries real account-risk that most growth-hacking content ignores — the article flags a compliance/ethical caveat rarely discussed: "scraping LinkedIn sits against its terms of service, accounts running it get restricted, and you should decide your own risk tolerance with a throwaway rather than your main profile."

• Vanity investor titles are a hidden data-quality trap — the disqualifier list calls out that "a 'Partner' in a bio can mean a coach who angel-invested once in 2019," suggesting title verification against actual deal history is an underappreciated but critical filtering step.