find-vcs

name	find-vcs
description	Find venture capital firms that match a startup's fundraising brief. Searches VC databases and firm websites using WebSearch and Apify scraping. Triggers on: "find vcs", "find investors", "search for funds", "which vcs invest in", "step 2", "next step", "continue to find vcs".

Find VCs

Pipeline: clarify-thesis → find-vcs → map-investors → enrich-vc-contacts → score-vc-fit → draft-investor-outreach

You are step 2 of 6. Your job is to identify 15–30 candidate VC firms that plausibly match the startup's fundraising profile.

Tools

• Exa MCP (web_search_exa) — primary VC database discovery
• Exa MCP (company_research_exa) — deep research on a specific firm: portfolio, thesis, recent activity
• Exa MCP (crawling_exa) — fetch and extract content from firm/portfolio pages
• Apify MCP (apify/cheerio-scraper) — fallback for structured portfolio page extraction (JS-light pages)
• Apify MCP (apify/website-content-crawler) — fallback for JS-heavy firm websites
• WebSearch / WebFetch — fallback for anything Exa doesn't cover

Prefer Exa over Apify for portfolio and firm research — it handles rendering and returns cleaner structured content. Use Apify only when Exa returns incomplete results for a specific page.

Input

Read data/normalized/company_profile.json. If the file does not exist or required fields are null, stop and ask the founder to complete clarify-thesis first.

Search strategy

Step 0: Read the VC databases reference

Read references/vc-databases.md before searching. It lists recommended sources, search patterns, and Apify actors for each database.

Step 1: Discover VC firms using Exa

Use web_search_exa to find candidate firms. Run at least 5 queries from different angles. Tailor to the startup's sector, stage, and geography.

Source	Exa query
OpenVC	site:openvc.app [sector] [stage] investors
Crunchbase	site:crunchbase.com/organization [sector] VC fund [stage]
Signal by NFX	site:signal.nfx.com [sector] [stage]
Notion VC lists	"vc list" [sector] [stage] site:notion.so
Airtable VC lists	"vc list" [sector] [stage] site:airtable.com
General	[sector] seed fund OR venture capital investors [geography]
Portfolio adjacency	"[comparable_company]" investors OR "backed by" OR "portfolio"

Deduplicate by fund name across all queries.

Step 2: Research each firm's portfolio using Exa

For each candidate firm found in Step 1, use company_research_exa to pull structured information:

company_research_exa("[fund_name] venture capital")

This returns: investment thesis, sector focus, stage, portfolio companies, check size signals, and recent activity — without needing to scrape individual pages.

Then use crawling_exa to fetch the firm's portfolio page directly for the full portfolio list:

crawling_exa("[fund_website]/portfolio")

or

crawling_exa("[fund_website]/companies")

Extract:

• Portfolio company names (to check for comparable_companies matches)
• Sector signals from portfolio descriptions
• Stage signals from portfolio company maturities
• Any stated check size or investment criteria

Fallback to Apify if Exa returns incomplete portfolio data for a specific firm:

• apify/cheerio-scraper — for structured portfolio grids on JS-light pages
• apify/website-content-crawler — for JS-heavy firm websites

Step 3: Filter candidates

Exclude firms that are clearly out on:

• Stage (e.g. growth-stage only fund for a pre-seed company)
• Geography (e.g. LATAM-only fund for a US company)
• Sector (e.g. biotech-only fund for a SaaS company)
• Check size (e.g. $50M+ checks for a $500K raise)

Flag partial fits with a note in possible_concerns.

What counts as a match

Evaluate each firm on:

• sector fit
• stage fit
• geography fit
• check size fit
• lead/follow fit
• portfolio adjacency (invested in comparable_companies or direct competitors)
• recent investment activity (last 24 months)
• thematic alignment with thesis_keywords
• explicit investor preferences from the profile

Output

Write an array to data/raw/fund_candidates.json conforming to schemas/fund_candidates.schema.json.

Target 15–30 firms. Fewer is fine if quality is high. Do not pad with weak fits.

Assign each fund a fund_id as a lowercase hyphen-separated slug of the fund name (e.g. sequoia-capital). This is the canonical join key used in all later steps.

Review checkpoint

After writing data/raw/fund_candidates.json, present a summary table to the founder:

Found [N] candidate VC firms for [company_name]:

| # | Fund | Stage | Sector focus | Check size | Confidence |
|---|------|-------|-------------|-----------|-----------|
| 1 | ... | ... | ... | ... | high/medium/low |
...

[If any partial fits]:
  ⚠ Partial fits flagged: [list with concerns]

[If evidence was thin for some firms]:
  Note: Evidence confidence is low for: [list] — worth verifying manually.

Does this list look right? You can:
  - Confirm to proceed to step 3 (mapping people at each firm)
  - Remove specific firms: "remove [fund name]"
  - Add a firm manually: "add [fund name] at [website]"
  - Narrow the list: "only keep firms with high confidence"

Do not proceed to map-investors until the founder confirms or adjusts the list.