Funding Event Prospector

Funding Event Prospector

Find companies that recently raised funding and identify decision-makers for timely, signal-based outreach.

Instructions

When a user wants to prospect into companies that have recently raised funding, execute this multi-step research and enrichment workflow.

Steps

1. Gather targeting criteria from the user. Ask these five questions:

   • Funding stages: Which rounds are you targeting? (Seed, Series A, Series B, Series C, Series D+, Growth Equity, etc.)
   • Amount range: What funding amount range are you interested in? (e.g., $5M-$20M, $50M+, any)
   • Timeframe: How recent should the funding be? (e.g., last 7 days, last 30 days, last 90 days)
   • Industry/geography filters: Any specific industries or geographies? (e.g., fintech in the US, healthtech in Europe)
   • Target roles: Which roles do you want to reach at these companies? (e.g., VP of Engineering, CTO, Head of Sales)

   If the user provides a broad request, infer reasonable defaults and confirm: "I'll search for Series A-C rounds in the last 30 days across all industries. Want me to narrow this down?"

2. Construct WebSearch queries targeting funding news sources. Run multiple searches to maximize coverage:

   • "Series [A/B/C]" "raised" [timeframe] site:techcrunch.com
   • "funding round" "[amount range]" [industry] site:crunchbase.com
   • "raised" "million" [industry] [geography] [timeframe]

   Vary the queries based on user criteria. For example, if the user wants Series B in fintech:

   • "Series B" "raised" "fintech" site:techcrunch.com 2026
   • "Series B" "funding" "fintech" site:crunchbase.com
   • "raised" "Series B" "million" "fintech" 2026

   Run 3-5 searches to ensure broad coverage across sources.

3. WebFetch top results to extract structured funding details from each article. For each result, capture:

   • Company name and domain
   • Funding amount (exact figure)
   • Round type (Seed, Series A, B, C, etc.)
   • Lead investor and participating investors
   • Stated use of funds (hiring, expansion, product development, etc.)
   • Date of the funding announcement
   • Source URL for reference

   Prioritize the 10-15 most relevant results. Skip duplicates where the same company appears in multiple articles.

4. Enrich each funded company by calling mcp__claude_ai_Amplemarket__enrich_company with the company domain extracted from the funding articles. Key data points to capture:

   • Industry and sub-industry
   • Employee count and growth trajectory
   • Headquarters location
   • Company description and value proposition
   • Tech stack (if available)

   If the domain is unclear from the article, search for it using the company name + "website" via WebSearch.

5. Filter against user's ICP criteria. Compare enriched company data against the user's requirements:

   • Does the industry match their target vertical?
   • Is the company size in their sweet spot?
   • Is the geography aligned?
   • Does the funding stage and amount match their criteria?

   Remove companies that do not fit and note why they were excluded. Present the filtered list to the user before proceeding.

6. Search for decision-makers at each matching company by calling mcp__claude_ai_Amplemarket__search_people with:

   • company_domains: [company domain]
   • person_titles: based on user's target roles
   • person_seniorities: mapped from target roles (e.g., ["VP", "Head", "Director", "C-Suite"])
   • full_output: true
   • page_size: 10

   Focus on roles most likely to be involved in post-funding initiatives (hiring, procurement, expansion).

7. Enrich top prospects by calling mcp__claude_ai_Amplemarket__enrich_person for the highest-priority contacts at each company. Set reveal_email to true for contact details. Credit note: Each person enrichment with email and phone reveal consumes Amplemarket credits. Warn the user before enriching: "I found [N] prospects across [M] companies. Enriching all of them will use [N] credits. Want me to proceed with all, or select the top prospects?"

8. Present results grouped by funding recency. Organize the output into three tiers:

   • This week: Companies that raised in the last 7 days (hottest signal)
   • This month: Companies that raised in the last 8-30 days
   • Recent: Companies that raised in the last 31-90 days

   For each company, display:

   • Funding details (round, amount, lead investor, use of funds)
   • Company firmographics (industry, size, location)
   • Decision-makers found (name, title, email if enriched)
   • Suggested opener referencing the funding event

   Include dynamic fields (see Dynamic Fields Generated below) that can be used in outreach templates.

9. Offer lead list creation with funding context. Ask: "Would you like me to create a lead list from these prospects? I can name it something like '[Funding Stage] - [Industry] - [Month Year]'." If the user agrees, pivot to creating a lead list via mcp__claude_ai_Amplemarket__create_lead_list using the discovered prospects' LinkedIn URLs.

Important Notes

• Funding news decays quickly in relevance. Prioritize the most recent announcements and flag anything older than 60 days as potentially stale for outreach timing.
• WebSearch results may include false positives (e.g., articles mentioning funding in passing). Always verify by WebFetching the article to confirm an actual funding round.
• Some companies may not be found in Amplemarket enrichment, especially very early-stage startups. Note these gaps and provide what data is available from the funding article itself.
• Credit costs can add up when enriching many prospects. Always warn the user about the number of enrichments before proceeding and offer to prioritize a subset.
• Funding amounts reported in the press may differ from official filings. Treat all amounts as approximate and flag any discrepancies found during enrichment.

Dynamic Fields Generated

The following dynamic fields are produced for each funded company and can be used in outreach templates:

Examples

Example 1: Series B Funding in Fintech, Last 30 Days

User prompt: "Find companies that raised a Series B in fintech in the last 30 days. I want to reach VPs of Engineering."

What the skill does:

1. Runs WebSearch queries:

• "Series B" "raised" "fintech" site:techcrunch.com 2026
• "Series B" "funding" "fintech" site:crunchbase.com 2026
• "raised" "Series B" "million" "fintech" February 2026

2. WebFetches top 10 articles to extract funding details.
3. Calls mcp__claude_ai_Amplemarket__enrich_company for each company found.
4. Filters to confirmed fintech companies.
5. Calls mcp__claude_ai_Amplemarket__search_people at each company with person_titles: ["VP of Engineering", "Vice President of Engineering"], person_seniorities: ["VP"].
6. Enriches top prospects with email reveal.

Example output:

FUNDING EVENT PROSPECTS: Series B Fintech - Last 30 Days

This Week (3 companies)

1. PayFlow - $38M Series B (March 5, 2026)

Decision Makers Found:

{{funding_suggested_opener}}: "James, congrats on PayFlow's $38M Series B. With plans to scale engineering and launch in LATAM, I imagine you are making some key infrastructure decisions right now."

2. LendStack - $52M Series B (March 3, 2026)

Decision Makers Found:

{{funding_suggested_opener}}: "Sarah, saw LendStack just closed a $52M Series B led by Sequoia. Doubling the engineering team is a big undertaking, and getting the right tooling in place early can save months of rework."

This Month (5 companies)

(Same format continues for companies that raised 8-30 days ago)

Summary

• Total funded companies found: 8
• Matching ICP after filtering: 6
• Total decision-makers identified: 14
• Enriched with contact details: 8

Would you like me to create a lead list from these 14 prospects? I'd suggest naming it "Series B - Fintech - Mar 2026".

Example 2: Seed/Series A in Healthtech, US West Coast

User prompt: "Who just raised Seed or Series A in healthtech on the US West Coast? I want to reach founders and CTOs."

What the skill does:

1. Runs WebSearch queries:

• "Seed" OR "Series A" "raised" "healthtech" OR "health tech" OR "digital health" site:techcrunch.com 2026
• "Seed round" OR "Series A" "healthcare" "California" OR "Oregon" OR "Washington" site:crunchbase.com
• "raised" "million" "digital health" "San Francisco" OR "Los Angeles" OR "Seattle" OR "Portland" 2026

2. WebFetches top articles to extract company names, amounts, investors, and use of funds.
3. Calls mcp__claude_ai_Amplemarket__enrich_company for each company found.
4. Filters to companies in healthtech-related industries located in California, Oregon, or Washington.
5. Calls mcp__claude_ai_Amplemarket__search_people with person_seniorities: ["Founder", "C-Suite"], person_titles: ["Founder", "Co-Founder", "CTO", "Chief Technology Officer"], company_domains: [each matching company domain].
6. Enriches top founder and CTO contacts with email reveal.
7. Presents results grouped by recency, distinguishing Seed from Series A rounds.

Example output (abbreviated):

FUNDING EVENT PROSPECTS: Seed/Series A Healthtech - US West Coast

This Week (2 companies)

1. MedSync - $8M Seed (March 6, 2026)

Decision Makers Found:

{{funding_suggested_opener}}: "Anika, congrats on MedSync's $8M Seed led by Khosla. Navigating the FDA pathway while building a product team is no small feat. Would love to share how we help early-stage health tech teams move faster."

Example 3: Large Rounds ($50M+) Across All Industries

User prompt: "Find companies that raised $50 million or more in the last 60 days. Any industry. Target Heads of Sales and CROs."

What the skill does:

1. Runs broad WebSearch queries:

• "raised" "$50 million" OR "$60 million" OR "$75 million" OR "$100 million" 2026 site:techcrunch.com
• "funding" "50M" OR "75M" OR "100M" site:crunchbase.com 2026
• "raised" "million" "Series" 2026 (then filters to $50M+ during extraction)

2. WebFetches results, filtering to rounds of $50M or more during data extraction.
3. Calls mcp__claude_ai_Amplemarket__enrich_company for all companies regardless of industry.
4. Calls mcp__claude_ai_Amplemarket__search_people with person_titles: ["Head of Sales", "CRO", "Chief Revenue Officer", "VP of Sales"], person_seniorities: ["Head", "C-Suite", "VP"], company_domains: [each company domain].
5. Presents a cross-industry view highlighting the largest rounds first, with industry tags for easy scanning.
6. Offers to segment the lead list by industry or round size.

Example output (abbreviated):

FUNDING EVENT PROSPECTS: $50M+ Rounds - All Industries - Last 60 Days

Found 12 companies that raised $50M or more. Showing top results by round size:

Total CROs/Heads of Sales found: 22 Enriched with contact details: 12

Would you like me to create a lead list? I can segment by industry or create one unified list named "$50M+ Rounds - Q1 2026".

Troubleshooting