| name | vc-finder |
| description | Takes a startup product URL or description, detects the industry and funding stage, identifies 5 comparable funded companies, searches who invested in those companies (Track A), finds VCs who publish investment theses about this space (Track B), and returns a ranked sourced list of relevant investors with deep-dives and outreach hooks. Use when asked to find investors for a startup, identify which VCs fund products like mine, research who backs companies in my space, build a VC target list, or find investor-market fit. |
| compatibility | ["claude-code","gemini-cli","github-copilot"] |
VC Finder
Take a product URL or description. Detect industry and stage. Find 5 comparable funded companies. Run two research tracks: who invested in those comparables (Track A), and which VCs publish theses about this space (Track B). Return a sourced, ranked investor list with outreach hooks.
Zero-hallucination policy: Every fact in the output must be traceable to a specific Tavily search result or the fetched product page. This applies to:
- Comparable company names: must appear in Tavily search results, not AI training knowledge
- VC fund names: must appear verbatim in Tavily search results
- Check sizes, stage focus, portfolio companies: must come from search snippets, not AI knowledge
- Fund overviews and thesis summaries: extracted from search snippets only. If a detail is not in the search data, write "not found in search data" -- do not fill from training knowledge.
Common Mistakes
| The agent will want to... | Why that's wrong |
|---|
| Add a16z or Sequoia because they are famous | A famous VC without evidence is noise. Only include VCs that appear in Tavily search results for this specific product. Name-dropping wastes the founder's time. |
| Generate comparable companies from training knowledge | Comparables must come from Tavily search results (Step 6). AI knowledge of companies is not evidence -- a company suggested from memory may have wrong funding status or may not be a true comparable. |
| Continue when all 5 Track A searches return 0 results | Zero Track A results means the comparables were wrong or too obscure. Stop, re-run Step 6 with broader search queries, and retry. |
| Include a Track B VC without citing the article or post | Thesis without a source is indistinguishable from hallucination. The founder cannot verify it and the list loses all credibility. |
| Fill in fund overview from training knowledge | Fund overviews must come from Tavily snippet text only. If the snippets don't describe the fund, write "not found in search data". |
| Detect stage from website aesthetics | Stage must come from the specific CTA signals detected in Step 4. |
| Write generic outreach hooks | Every outreach hook must name this specific product's differentiator and a specific VC portfolio signal or thesis quote from the search data. |
| Skip the URL fetch when the user also provides a description | Always fetch the URL. The live page often reveals stage signals that the user's description omits. |
Step 1: Setup Check
echo "TAVILY_API_KEY: ${TAVILY_API_KEY:+set}"
echo "FIRECRAWL_API_KEY: ${FIRECRAWL_API_KEY:-not set, Tavily extract will be used as fallback}"
If TAVILY_API_KEY is missing: Stop. Tell the user: "TAVILY_API_KEY is required to research VC investments and theses. There is no fallback for this. Get it at app.tavily.com -- free tier: 1000 credits/month (about 125 full runs). Add it to your .env file."
If only FIRECRAWL_API_KEY is missing: Continue silently. Tavily extract will be used for the URL fetch.
Step 2: Gather Input
You need:
- Product URL (required, unless user pastes a product description directly)
- Optional: target stage hint (pre-seed, seed, series-a, series-b) -- if provided, use it and skip stage detection
- Optional: geography preference (US, Europe, global) -- defaults to US if not specified
If the user provides only a pasted description (no URL): Skip Steps 3-4. Go directly to Step 5 with the pasted text as product_content. Set stage_source to user_description.
If neither URL nor description is provided: Ask: "What is the URL of your product or startup? Or paste a short description: what it does, who it is for, and what stage you are at (pre-seed, seed, Series A)."
Derive product slug from URL for the output filename:
PRODUCT_SLUG=$(python3 -c "
from urllib.parse import urlparse
url = 'URL_HERE'
host = urlparse(url).netloc.replace('www.', '')
print(host.split('.')[0])
")
Step 3: Fetch Product Page
Primary: Firecrawl (if FIRECRAWL_API_KEY is set)
curl -s -X POST https://api.firecrawl.dev/v1/scrape \
-H "Authorization: Bearer $FIRECRAWL_API_KEY" \
-H "Content-Type: application/json" \
-d '{"url": "URL_HERE", "formats": ["markdown"], "onlyMainContent": true}' \
| python3 -c "
import sys, json
d = json.load(sys.stdin)
content = d.get('data', {}).get('markdown', '') or d.get('markdown', '')
print(f'Fetched: {len(content)} characters')
open('/tmp/vc-product-raw.md', 'w').write(content)
"
Fallback: Tavily extract (if FIRECRAWL_API_KEY is not set)
curl -s -X POST https://api.tavily.com/extract \
-H "Content-Type: application/json" \
-d "{\"api_key\": \"$TAVILY_API_KEY\", \"urls\": [\"URL_HERE\"]}" \
| python3 -c "
import sys, json
d = json.load(sys.stdin)
content = d.get('results', [{}])[0].get('raw_content', '')
print(f'Fetched via Tavily extract: {len(content)} characters')
open('/tmp/vc-product-raw.md', 'w').write(content)
"
Step-level checkpoint:
python3 -c "
content = open('/tmp/vc-product-raw.md').read()
if len(content) < 200:
print('ERROR: Page returned fewer than 200 characters.')
else:
print(f'Content OK: {len(content)} characters')
"
If content < 200 characters: Stop fetching. Tell the user: "The product page returned no readable content. This usually means the site is JavaScript-rendered and requires a browser. Please paste your product description directly: what it does, who it is for, and what stage you are at."
Proceed to Step 5 using the pasted description as product_content.
Step 4: Detect Stage Signals Locally (No API)
Parse the fetched markdown with regex before the analysis step.
python3 << 'PYEOF'
import re, json
content = open('/tmp/vc-product-raw.md').read().lower()
stage_signals = []
if re.search(r'join\s+(the\s+)?waitlist|sign\s+up\s+for\s+beta|early\s+access|request\s+(an?\s+)?invite|get\s+notified', content):
stage_signals.append({'signal': 'waitlist or beta CTA', 'stage_hint': 'pre-seed'})
if re.search(r'start\s+(your\s+)?free\s+trial|try\s+(it\s+)?for\s+free|request\s+a?\s+demo|book\s+a?\s+demo|schedule\s+a?\s+demo', content):
stage_signals.append({'signal': 'free trial or demo CTA', 'stage_hint': 'seed'})
if re.search(r'contact\s+sales|talk\s+to\s+(our\s+)?sales|see\s+pricing|view\s+pricing|plans\s+and\s+pricing', content):
stage_signals.append({'signal': 'pricing or sales CTA', 'stage_hint': 'series-a'})
if re.search(r'case\s+stud(y|ies)|customer\s+stor(y|ies)|trusted\s+by\s+[\d,]+|used\s+by\s+[\d,]+', content):
stage_signals.append({'signal': 'case studies or customer count', 'stage_hint': 'series-a'})
if re.search(r'enterprise\s+(plan|pricing|tier)|we.?re\s+hiring|join\s+our\s+team|open\s+positions', content):
stage_signals.append({'signal': 'enterprise tier or job openings', 'stage_hint': 'series-a-or-b'})
funding_match = re.search(
r'raised\s+\$[\d,.]+\s*[mk]?|series\s+[abc]\s+round|seed\s+round|(\$[\d,.]+\s*[mk]?\s+(?:seed|series\s+[abc]))',
content
)
if funding_match:
stage_signals.append({'signal': f'funding text: {funding_match.group(0).strip()}', 'stage_hint': 'announced'})
if not stage_signals:
dominant = 'unknown'
elif any(s['stage_hint'] == 'announced' for s in stage_signals):
dominant = 'announced'
elif any(s['stage_hint'] == 'series-a-or-b' for s in stage_signals):
dominant = 'series-a'
elif any(s['stage_hint'] == 'series-a' for s in stage_signals):
dominant = 'series-a'
elif any(s['stage_hint'] == 'seed' for s in stage_signals):
dominant = 'seed'
else:
dominant = 'pre-seed'
confidence = 'high' if len(stage_signals) >= 2 else ('medium' if len(stage_signals) == 1 else 'low')
result = {'signals': stage_signals, 'dominant_stage': dominant, 'confidence': confidence}
json.dump(result, open('/tmp/vc-stage-signals.json', 'w'), indent=2)
print(f'Stage: {dominant} ({confidence} confidence) from {len(stage_signals)} signal(s)')
for s in stage_signals:
print(f' - {s["signal"]} -> {s["stage_hint"]}')
PYEOF
Step 5: Product Analysis (Taxonomy, Stage, ICP)
Print the product content and stage signals:
python3 -c "
import json
content = open('/tmp/vc-product-raw.md').read()[:6000]
signals = json.load(open('/tmp/vc-stage-signals.json'))
print('=== PRODUCT PAGE (first 6000 chars) ===')
print(content)
print()
print('=== DETECTED STAGE SIGNALS ===')
print(json.dumps(signals, indent=2))
"
AI instructions: Analyze the product page content above. Generate the taxonomy, ICP, and stage classification only -- do NOT generate comparable companies yet (that is done via live search in Step 6).
Write to /tmp/vc-product-analysis.json:
product_name: from the pageone_line_description: what it does, for whom, core value prop. Under 20 words. No marketing language.industry_taxonomy: l1 (top-level: fintech / healthtech / developer tools / consumer / etc.), l2 (sector: sales technology / logistics software / etc.), l3 (specific niche: outbound prospecting / last-mile routing / etc.). Vague labels like "technology" or "software" alone are not acceptable.icp: buyer_persona (job title), company_type, company_sizedetected_stage: pre-seed / seed / series-a / series-b / unknownstage_confidence: high / medium / lowstage_evidence: one sentence citing exactly which CTA or text on the page drove this. Write "no clear signals found" if unknown.geography_bias: US / Europe / global / unclearcomparable_companies: leave as empty array [] -- will be filled in Step 6
python3 << 'PYEOF'
import json
analysis = {
# FILL from your analysis above
"comparable_companies": []
}
json.dump(analysis, open('/tmp/vc-product-analysis.json', 'w'), indent=2)
print('Product analysis written.')
PYEOF
Verify:
python3 -c "
import json
a = json.load(open('/tmp/vc-product-analysis.json'))
print('Product:', a['product_name'])
print('Industry:', a['industry_taxonomy']['l1'], '>', a['industry_taxonomy']['l2'], '>', a['industry_taxonomy']['l3'])
print('Stage:', a['detected_stage'], '(' + a['stage_confidence'] + ' confidence)')
"
Step 5b: Curated Pre-Match Against Verified Fund Dataset
Run the product taxonomy against a curated dataset of 25 verified VC funds (sourced from fund websites). Produces zero-hallucination fund matches and seed comparables for Track A -- no Tavily credits consumed.
Print product analysis for tag mapping:
python3 -c "
import json
a = json.load(open('/tmp/vc-product-analysis.json'))
print('Taxonomy:', a['industry_taxonomy']['l1'], '>', a['industry_taxonomy']['l2'], '>', a['industry_taxonomy']['l3'])
print('Stage:', a['detected_stage'])
print('Geography:', a['geography_bias'])
"
AI instructions: Map the product taxonomy to the standard tags used in the fund dataset. Available tags:
DevTools, Infrastructure, Open Source, B2B SaaS, AI, Data, FinTech, HealthTech, Enterprise, Consumer, Marketplaces, E-commerce, Crypto, DeepTech, Cybersecurity, Generalist
Pick 2-4 tags that describe this product. Map detected_stage to: Pre-seed, Seed, Series A, or Growth. Map geography_bias to: US, Europe, India, or Global.
Write product context:
python3 << 'PYEOF'
import json
# FILL based on taxonomy analysis above
context = {
"extracted_tags": ["TagA", "TagB"], # 2-4 tags from the list above
"stage_hint": "Seed", # Pre-seed / Seed / Series A / Growth
"geography_hint": "US" # US / Europe / India / Global
}
json.dump(context, open('/tmp/vc-product-context.json', 'w'), indent=2)
print('Product context:', context)
PYEOF
Run scoring against the embedded curated dataset:
python3 << 'PYEOF'
import json
context = json.load(open('/tmp/vc-product-context.json'))
VC_FUNDS = [
{"fund_name":"Y Combinator","thesis":"We provide seed funding for startups. We invest in deeply technical teams building massive companies across all domains.","check_size":"$500k","stage_focus":["Pre-seed","Seed"],"industry_tags":["Generalist","B2B SaaS","DevTools","AI"],"geography_focus":["Global"],"notable_portfolio":["Stripe","Airbnb","GitLab"],"website":"https://www.ycombinator.com"},
{"fund_name":"boldstart ventures","thesis":"Day one partner for developer first, crypto, and SaaS founders. We love deeply technical founders solving hard infrastructure problems.","check_size":"$1M - $3M","stage_focus":["Pre-seed","Seed"],"industry_tags":["DevTools","Infrastructure","Crypto"],"geography_focus":["Global","US"],"notable_portfolio":["Snyk","Blockdaemon","Superhuman"],"website":"https://boldstart.vc"},
{"fund_name":"Heavybit","thesis":"The leading investor in developer-first startups. We help technical founders launch, gain traction, and build enterprise-ready companies.","check_size":"$1M - $5M","stage_focus":["Seed","Series A"],"industry_tags":["DevTools","Infrastructure","Open Source"],"geography_focus":["Global","US"],"notable_portfolio":["PagerDuty","Sanity","Netlify"],"website":"https://www.heavybit.com"},
{"fund_name":"Amplify Partners","thesis":"We invest in technical founders building the next generation of IT infrastructure, developer tools, and data platforms.","check_size":"$2M - $8M","stage_focus":["Seed","Series A"],"industry_tags":["DevTools","Infrastructure","AI","Data"],"geography_focus":["US"],"notable_portfolio":["Datadog","OCTO","dbt Labs"],"website":"https://www.amplifypartners.com"},
{"fund_name":"OSS Capital","thesis":"We exclusively back early-stage founders building Commercial Open Source Software (COSS) companies.","check_size":"$500k - $2M","stage_focus":["Pre-seed","Seed","Series A"],"industry_tags":["Open Source","DevTools"],"geography_focus":["Global"],"notable_portfolio":["Cal.com","Appsmith","Hoppscotch"],"website":"https://oss.capital"},
{"fund_name":"Sequoia Capital","thesis":"We help the daring build legendary companies, from idea to IPO and beyond. Sequoia is an early-stage and growth-stage investor.","check_size":"$1M - $10M+","stage_focus":["Seed","Series A","Growth"],"industry_tags":["Generalist","Enterprise","Consumer","AI"],"geography_focus":["Global"],"notable_portfolio":["Apple","Google","WhatsApp"],"website":"https://www.sequoiacap.com"},
{"fund_name":"Andreessen Horowitz (a16z)","thesis":"We invest in software eating the world. We back bold entrepreneurs building the future through technology.","check_size":"$1M - $50M+","stage_focus":["Seed","Series A","Growth"],"industry_tags":["Generalist","Crypto","Enterprise","Consumer","AI"],"geography_focus":["Global","US"],"notable_portfolio":["Facebook","Coinbase","Figma"],"website":"https://a16z.com"},
{"fund_name":"Point Nine Capital","thesis":"We are a seed-stage venture capital firm focused on B2B SaaS and B2B marketplaces globally.","check_size":"$1M - $3M","stage_focus":["Seed"],"industry_tags":["B2B SaaS","Marketplaces"],"geography_focus":["Europe","Global"],"notable_portfolio":["Zendesk","Typeform","Docplanner"],"website":"https://www.pointnine.com"},
{"fund_name":"Cherry Ventures","thesis":"We champion founders in Europe from their earliest days. We are generalist seed investors.","check_size":"$1M - $4M","stage_focus":["Pre-seed","Seed"],"industry_tags":["Generalist","Consumer","B2B SaaS"],"geography_focus":["Europe"],"notable_portfolio":["FlixBus","Auto1 Group","Forto"],"website":"https://www.cherry.vc"},
{"fund_name":"First Round Capital","thesis":"We are the seed-stage firm that builds the most supportive community for founders.","check_size":"$1M - $4M","stage_focus":["Pre-seed","Seed"],"industry_tags":["Generalist","B2B SaaS","Consumer"],"geography_focus":["US"],"notable_portfolio":["Uber","Notion","Roblox"],"website":"https://firstround.com"},
{"fund_name":"Bessemer Venture Partners","thesis":"BVP helps entrepreneurs lay strong foundations to build and forge long-standing companies.","check_size":"$1M - $20M+","stage_focus":["Seed","Series A","Growth"],"industry_tags":["Generalist","Enterprise","Consumer","FinTech"],"geography_focus":["Global"],"notable_portfolio":["LinkedIn","Twilio","Shopify"],"website":"https://www.bvp.com"},
{"fund_name":"Index Ventures","thesis":"We back the best and most ambitious entrepreneurs across all stages to build category-defining businesses.","check_size":"$1M - $20M+","stage_focus":["Seed","Series A","Growth"],"industry_tags":["Generalist","FinTech","Consumer","B2B SaaS"],"geography_focus":["Europe","US","Global"],"notable_portfolio":["Dropbox","Slack","Figma"],"website":"https://www.indexventures.com"},
{"fund_name":"Lightspeed Venture Partners","thesis":"We invest globally in enterprise, consumer, and health founders who are shaping the future.","check_size":"$1M - $25M+","stage_focus":["Seed","Series A","Growth"],"industry_tags":["Generalist","Enterprise","Consumer","FinTech"],"geography_focus":["Global"],"notable_portfolio":["Snap","Rippling","MuleSoft"],"website":"https://lsvp.com"},
{"fund_name":"Accel","thesis":"We partner with exceptional founders from inception through all phases of private company growth.","check_size":"$1M - $20M+","stage_focus":["Seed","Series A","Growth"],"industry_tags":["Generalist","B2B SaaS","Consumer","DevTools"],"geography_focus":["Global"],"notable_portfolio":["Facebook","Atlassian","Spotify"],"website":"https://www.accel.com"},
{"fund_name":"Bain Capital Ventures","thesis":"From seed to growth, we back founders building legendary infrastructure, fintech, application, and commerce companies.","check_size":"$1M - $50M+","stage_focus":["Seed","Series A","Growth"],"industry_tags":["Generalist","Infrastructure","FinTech","B2B SaaS"],"geography_focus":["US","Global"],"notable_portfolio":["DocuSign","SendGrid","Redis"],"website":"https://www.baincapitalventures.com"},
{"fund_name":"Greylock Partners","thesis":"We partner with early-stage founders to build enterprise and consumer software companies that define new categories.","check_size":"$1M - $10M","stage_focus":["Seed","Series A"],"industry_tags":["Enterprise","Consumer","Cybersecurity","AI"],"geography_focus":["US"],"notable_portfolio":["Workday","Palo Alto Networks","LinkedIn"],"website":"https://greylock.com"},
{"fund_name":"Unusual Ventures","thesis":"We provide a breakthrough level of support for early-stage founders building enterprise tech.","check_size":"$1M - $5M","stage_focus":["Pre-seed","Seed"],"industry_tags":["Enterprise","DevTools","B2B SaaS"],"geography_focus":["US"],"notable_portfolio":["Arctic Wolf","Harness","Vivun"],"website":"https://www.unusual.vc"},
{"fund_name":"Crane Venture Partners","thesis":"We back deep tech and enterprise founders in Europe solving hard problems with data and code.","check_size":"$1M - $4M","stage_focus":["Seed"],"industry_tags":["Enterprise","DeepTech","Data","AI"],"geography_focus":["Europe"],"notable_portfolio":["Onfido","Tessian","Forto"],"website":"https://crane.vc"},
{"fund_name":"Founder Collective","thesis":"We are a seed-stage venture capital fund, built by founders, for founders. We back weird, wonderful, and wild startups.","check_size":"$500k - $2M","stage_focus":["Seed"],"industry_tags":["Generalist","Consumer","B2B SaaS"],"geography_focus":["US","Global"],"notable_portfolio":["Uber","Airtable","BuzzFeed"],"website":"https://www.foundercollective.com"},
{"fund_name":"Benchmark","thesis":"We are a partnership of equal partners. We back mission-driven founders at the earliest stages and walk beside them for the long haul.","check_size":"$1M - $10M","stage_focus":["Seed","Series A"],"industry_tags":["Generalist","Marketplaces","Enterprise","Consumer"],"geography_focus":["US","Global"],"notable_portfolio":["Uber","Twitter","eBay","Snapchat"],"website":"https://www.benchmark.com"},
{"fund_name":"Accel India","thesis":"We partner with exceptional founders from inception through all phases of private company growth in the Indian ecosystem.","check_size":"$1M - $15M","stage_focus":["Seed","Series A","Growth"],"industry_tags":["Generalist","B2B SaaS","Consumer","FinTech","E-commerce"],"geography_focus":["India"],"notable_portfolio":["Flipkart","Swiggy","Freshworks"],"website":"https://www.accel.com/india"},
{"fund_name":"Blume Ventures","thesis":"We are a seed and pre-seed venture fund that backs startups with both funding and active mentoring.","check_size":"$500k - $3M","stage_focus":["Pre-seed","Seed"],"industry_tags":["Generalist","B2B SaaS","Consumer","DeepTech","HealthTech"],"geography_focus":["India"],"notable_portfolio":["Unacademy","Purplle","GreyOrange"],"website":"https://blume.vc"},
{"fund_name":"Elevation Capital","thesis":"We partner with visionary founders in India across early stages to help them build category-defining businesses.","check_size":"$1M - $10M","stage_focus":["Seed","Series A"],"industry_tags":["Generalist","Consumer","FinTech","B2B SaaS","HealthTech"],"geography_focus":["India"],"notable_portfolio":["Paytm","Swiggy","Meesho"],"website":"https://elevationcapital.com"},
{"fund_name":"Peak XV Partners","thesis":"Formerly Sequoia India & SEA, we partner with founders across early, growth, and public stages to build enduring companies.","check_size":"$1M - $20M+","stage_focus":["Seed","Series A","Growth"],"industry_tags":["Generalist","Consumer","FinTech","B2B SaaS","DevTools","AI"],"geography_focus":["India","South Asia"],"notable_portfolio":["Zomato","Pine Labs","Cred"],"website":"https://www.peakxv.com"},
{"fund_name":"Nexus Venture Partners","thesis":"We are a US-India venture capital firm backing extraordinary founders building product-first companies.","check_size":"$1M - $10M","stage_focus":["Seed","Series A"],"industry_tags":["B2B SaaS","Enterprise","DevTools","Consumer"],"geography_focus":["India","US"],"notable_portfolio":["Postman","Hasura","Zepto"],"website":"https://nexusvp.com"}
]
STAGE_ORDER = {"Pre-seed": 0, "Seed": 1, "Series A": 2, "Growth": 3}
def score_fund(fund, ctx):
score = 0
fund_tags = fund.get("industry_tags", [])
extracted_tags = ctx.get("extracted_tags", ["Generalist"])
tag_points = 0
matched_tags = []
for tag in extracted_tags:
if tag in fund_tags:
tag_points += 5 if tag == "Generalist" else 20
matched_tags.append(tag)
tag_points = min(tag_points, 60)
score += tag_points
stage_hint = ctx.get("stage_hint")
fund_stages = fund.get("stage_focus", [])
if not stage_hint:
score += 10
elif fund_stages:
if stage_hint in fund_stages:
score += 20
elif stage_hint in STAGE_ORDER:
hint_idx = STAGE_ORDER[stage_hint]
if any(f in STAGE_ORDER and abs(STAGE_ORDER[f] - hint_idx) == 1 for f in fund_stages):
score += 10
geo_hint = ctx.get("geography_hint")
fund_geo = fund.get("geography_focus", ["Global"])
if not geo_hint or geo_hint == "Global":
score += 10
elif fund_geo == ["India"] and geo_hint == "US":
pass
elif geo_hint in fund_geo:
score += 20
elif "Global" in fund_geo:
score += 15
if geo_hint == "US" and "India" in fund_geo and "US" not in fund_geo and "Global" not in fund_geo:
score = max(0, score - 30)
if fund_tags and extracted_tags and fund_tags[0] not in extracted_tags and tag_points <= 20:
score = max(0, score - 15)
return score, matched_tags
scored = []
for fund in VC_FUNDS:
score, matched_tags = score_fund(fund, context)
tier = "High" if score >= 70 else ("Medium" if score >= 40 else "Low")
scored.append({
"fund_name": fund["fund_name"],
"thesis": fund["thesis"],
"check_size": fund["check_size"],
"stage_focus": fund["stage_focus"],
"industry_tags": fund["industry_tags"],
"geography_focus": fund["geography_focus"],
"notable_portfolio": fund["notable_portfolio"],
"website": fund["website"],
"source": "verified (fund website)",
"score": score,
"confidence": tier,
"matched_tags": matched_tags
})
scored.sort(key=lambda x: (-x["score"], x["fund_name"]))
relevant = [m for m in scored if m["confidence"] in ("High", "Medium")]
curated_comparables = []
for m in relevant:
for company in m.get("notable_portfolio", []):
if company not in curated_comparables:
curated_comparables.append(company)
output = {
"high_medium_matches": relevant,
"curated_comparables": curated_comparables[:6]
}
json.dump(output, open('/tmp/vc-curated-matches.json', 'w'), indent=2)
print(f'Curated matches: {len(relevant)} High/Medium confidence funds')
for m in relevant[:8]:
print(f' {m["confidence"]:6} ({m["score"]:3}) {m["fund_name"]}')
print(f'Seed comparables from portfolio: {curated_comparables[:6]}')
PYEOF
Step 6: Discover Comparable Companies via Tavily
Load curated portfolio companies from Step 5b as seed comparables:
python3 -c "
import json
matches = json.load(open('/tmp/vc-curated-matches.json'))
curated = matches.get('curated_comparables', [])
print(f'Curated portfolio comparables ({len(curated)}): {curated}')
need = max(0, 5 - len(curated))
print(f'Tavily will supplement with up to {need} more')
"
Do not use AI training knowledge to generate comparable companies. Curated portfolio companies (above) are already zero-hallucination comparables from verified fund data. Tavily supplements with L3-niche-specific companies.
python3 << 'PYEOF'
import json, os, urllib.request
analysis = json.load(open('/tmp/vc-product-analysis.json'))
l2 = analysis['industry_taxonomy']['l2']
l3 = analysis['industry_taxonomy']['l3']
tavily_key = os.environ.get('TAVILY_API_KEY', '')
queries = [
f'"{l3}" startup raised funding venture capital seed series',
f'"{l2}" companies venture backed funded startup'
]
all_results = []
for query in queries:
payload = json.dumps({
"api_key": tavily_key,
"query": query,
"search_depth": "advanced",
"max_results": 8,
"include_answer": True
}).encode()
req = urllib.request.Request(
'https://api.tavily.com/search',
data=payload,
headers={'Content-Type': 'application/json'},
method='POST'
)
try:
with urllib.request.urlopen(req, timeout=30) as resp:
result = json.loads(resp.read())
all_results.append({
'query': query,
'answer': result.get('answer', ''),
'results': [
{'title': r.get('title',''), 'url': r.get('url',''), 'content': r.get('content','')[:500]}
for r in result.get('results', [])
]
})
print(f'Comparable search: {len(result.get("results", []))} results for "{query[:60]}"')
except Exception as e:
print(f'Comparable search FAILED: {e}')
all_results.append({'query': query, 'answer': '', 'results': [], 'error': str(e)})
json.dump(all_results, open('/tmp/vc-comparable-search.json', 'w'), indent=2)
PYEOF
Print results for AI selection:
python3 -c "
import json
results = json.load(open('/tmp/vc-comparable-search.json'))
for r in results:
print(f'Query: {r[\"query\"]}')
print(f'Answer: {r.get(\"answer\",\"\")[:400]}')
for item in r.get('results', []):
print(f' - {item[\"title\"]} | {item[\"url\"]}')
print(f' {item[\"content\"][:200]}')
print()
"
AI instructions: Combine the curated portfolio companies from /tmp/vc-curated-matches.json with the Tavily search results above. Pick exactly 5 comparable companies. Prioritize curated portfolio companies (already verified -- they are real portfolio companies of matched VC funds). Supplement with Tavily-discovered companies to reach 5 if needed.
For each comparable write:
name: company namesimilarity_reason: one sentence explaining the fit (for curated: reference the fund that backed them; for Tavily: cite the snippet)source_url: portfolio fund website for curated companies, Tavily result URL for discovered onesestimated_stage: from curated data or snippet text -- write "not in search data" if unknownsource_type: "curated_portfolio" or "tavily_discovered"
Update /tmp/vc-product-analysis.json with the comparable_companies array:
python3 << 'PYEOF'
import json
analysis = json.load(open('/tmp/vc-product-analysis.json'))
analysis['comparable_companies'] = [
# FILL 5 companies -- curated_portfolio first, then tavily_discovered
# Each: {"name": str, "similarity_reason": str, "source_url": str, "estimated_stage": str, "source_type": str}
]
json.dump(analysis, open('/tmp/vc-product-analysis.json', 'w'), indent=2)
print('Comparables written:', ', '.join(c['name'] for c in analysis['comparable_companies']))
PYEOF
If fewer than 3 comparable companies appear in the search results: Broaden the queries. Run a third search: "[l1] startup" funding round venture capital. If still thin, proceed with what is available and flag in data_quality_flags.
Step 7: Track A -- Who Invested in Comparable Companies
Run 5 Tavily searches, one per comparable.
python3 << 'PYEOF'
import json, os, urllib.request
analysis = json.load(open('/tmp/vc-product-analysis.json'))
comparables = analysis['comparable_companies']
tavily_key = os.environ.get('TAVILY_API_KEY', '')
all_track_a = []
for comp in comparables:
company = comp['name']
query = f'"{company}" investors funding venture capital backed seed series'
payload = json.dumps({
"api_key": tavily_key,
"query": query,
"search_depth": "advanced",
"max_results": 5,
"include_answer": True
}).encode()
req = urllib.request.Request(
'https://api.tavily.com/search',
data=payload,
headers={'Content-Type': 'application/json'},
method='POST'
)
try:
with urllib.request.urlopen(req, timeout=30) as resp:
result = json.loads(resp.read())
all_track_a.append({
'comparable_company': company,
'similarity_reason': comp['similarity_reason'],
'query': query,
'answer': result.get('answer', ''),
'results': result.get('results', [])
})
print(f'Track A - {company}: {len(result.get("results", []))} results')
except Exception as e:
print(f'Track A - {company}: FAILED ({e})')
all_track_a.append({
'comparable_company': company,
'similarity_reason': comp['similarity_reason'],
'query': query,
'answer': '',
'results': [],
'error': str(e)
})
json.dump(all_track_a, open('/tmp/vc-tracka-results.json', 'w'), indent=2)
print(f'Track A complete. Comparables with results: {sum(1 for r in all_track_a if r.get("results"))}')
PYEOF
If all 5 Track A searches return 0 results: Re-run Step 6 with broader queries. Retry with well-covered companies (those with significant press coverage). If still 0: proceed to Track B only and flag in data_quality_flags.
Step 8: Track B -- VCs With Investment Theses About This Space
Run 3 Tavily searches using L2 and L3 taxonomy from Step 5.
python3 << 'PYEOF'
import json, os, urllib.request
analysis = json.load(open('/tmp/vc-product-analysis.json'))
l2 = analysis['industry_taxonomy']['l2']
l3 = analysis['industry_taxonomy']['l3']
stage = analysis['detected_stage']
tavily_key = os.environ.get('TAVILY_API_KEY', '')
queries = [
{'name': 'thesis_l3', 'query': f'venture capital investment thesis "{l3}" investing 2023 OR 2024 OR 2025'},
{'name': 'thesis_l2', 'query': f'VC fund "{l2}" investment thesis portfolio companies'},
{'name': 'stage_space', 'query': f'{stage} investors "{l3}" startup venture capital fund'}
]
all_track_b = []
for q in queries:
payload = json.dumps({
"api_key": tavily_key,
"query": q['query'],
"search_depth": "advanced",
"max_results": 7,
"include_answer": True
}).encode()
req = urllib.request.Request(
'https://api.tavily.com/search',
data=payload,
headers={'Content-Type': 'application/json'},
method='POST'
)
try:
with urllib.request.urlopen(req, timeout=30) as resp:
result = json.loads(resp.read())
all_track_b.append({
'query_name': q['name'],
'query': q['query'],
'answer': result.get('answer', ''),
'results': result.get('results', [])
})
print(f"Track B - {q['name']}: {len(result.get('results', []))} results")
except Exception as e:
print(f"Track B - {q['name']}: FAILED ({e})")
all_track_b.append({'query_name': q['name'], 'query': q['query'], 'answer': '', 'results': [], 'error': str(e)})
json.dump(all_track_b, open('/tmp/vc-trackb-results.json', 'w'), indent=2)
PYEOF
If all 3 Track B searches return 0 results: Proceed with Track A results only. Note in data_quality_flags: "No thesis-led investors found via public search."
Step 9: Synthesize -- Rank and Score All VCs
Print the research data:
python3 -c "
import json
analysis = json.load(open('/tmp/vc-product-analysis.json'))
track_a = json.load(open('/tmp/vc-tracka-results.json'))
track_b = json.load(open('/tmp/vc-trackb-results.json'))
curated = json.load(open('/tmp/vc-curated-matches.json'))
track_a_summary = []
for item in track_a:
snippets = [{'title': r.get('title',''), 'url': r.get('url',''), 'content': r.get('content','')[:400]}
for r in item.get('results', [])[:3]]
track_a_summary.append({
'comparable_company': item['comparable_company'],
'similarity_reason': item['similarity_reason'],
'answer': item.get('answer', '')[:500],
'top_results': snippets
})
track_b_summary = []
for item in track_b:
snippets = [{'title': r.get('title',''), 'url': r.get('url',''), 'content': r.get('content','')[:400]}
for r in item.get('results', [])[:4]]
track_b_summary.append({
'query_name': item['query_name'],
'answer': item.get('answer', '')[:500],
'top_results': snippets
})
curated_summary = []
for m in curated.get('high_medium_matches', []):
curated_summary.append({
'fund_name': m['fund_name'],
'confidence': m['confidence'],
'score': m['score'],
'matched_tags': m['matched_tags'],
'thesis': m['thesis'],
'check_size': m['check_size'],
'stage_focus': m['stage_focus'],
'notable_portfolio': m['notable_portfolio'],
'website': m['website'],
'source': 'verified (fund website)'
})
print(json.dumps({
'product': {
'name': analysis['product_name'],
'description': analysis['one_line_description'],
'industry': analysis['industry_taxonomy'],
'icp': analysis['icp'],
'stage': analysis['detected_stage'],
'stage_confidence': analysis['stage_confidence'],
'geography': analysis['geography_bias']
},
'curated_matches': curated_summary,
'track_a_research': track_a_summary,
'track_b_research': track_b_summary
}, indent=2))
"
AI instructions -- zero-hallucination rules:
Every field in the output must be traceable to the printed data above. Rules:
- curated_vcs: Use the
curated_matches data directly. These are pre-verified -- no Tavily evidence required. fund_overview comes from the thesis field in the curated data. check_size and stage_focus come from the curated data fields. Do NOT fill from training knowledge even for these funds.
- VC names (Track A / B): Only include a fund if its name appears verbatim in the snippet text or title. No exceptions.
- evidence_company (Track A): The comparable company they backed -- must be stated in the snippet text, not inferred.
- thesis_source_title (Track B): The exact title of the article or post as it appears in the search results.
- fund_overview (Track A / B): Extract from snippet text only. Max 2 sentences. If the snippets do not describe the fund, write "not found in search data".
- thesis_summary: Close paraphrase of the snippet text. Do not add context from training knowledge.
- check_size (Track A / B): From snippet data only. Write "not in search data" if not mentioned.
- portfolio_in_space: Only companies that appear in the search snippets. Write "not found in search data" if none.
- stage_fit_score 1-10: Penalize 3 points if the VC's stated stage does not match the product's detected stage.
- space_fit_score 1-10: 9-10 only if the VC backed 2+ companies in the L3 niche per the snippets or curated data.
- approach_method: one of -- cold email / warm intro required / AngelList / application form / Twitter/X DM. Infer from snippets or fund website.
- outreach_hook: Must name a specific portfolio signal or thesis quote. Generic hooks like "highlight your traction" are not acceptable.
- No em dashes. No marketing language.
Write to /tmp/vc-final-list.json:
product_summary: name, one_line_description, industry_l1, industry_l2, industry_l3, detected_stage, comparable_companies_used (names only)curated_vcs: fund_name, confidence ("High"/"Medium"), matched_tags, fund_overview (from thesis field), check_size, stage_focus, website, source ("verified (fund website)"), stage_fit_score, space_fit_scoretrack_a_vcs: fund_name, evidence_company (REQUIRED), evidence_source_url, stage_focus, check_size, fund_overview, thesis_summary, stage_fit_score, space_fit_score, approach_methodtrack_b_vcs: fund_name, thesis_source_title (REQUIRED), thesis_source_url, stage_focus, check_size, fund_overview, thesis_summary, stage_fit_score, space_fit_score, approach_methodtop_5_deep_dives: fund_name, track ("Curated"/"A"/"B"), fund_overview, why_fit, portfolio_in_space, how_to_approach (min 30 chars), outreach_hookoutreach_hooks: 3 objects -- hook_type, hook_text (2-3 sentences), best_fordata_quality_flags: gaps, missing fields, low-confidence areas
python3 << 'PYEOF'
import json
result = {
# FILL from synthesis above
# Must include: product_summary, curated_vcs, track_a_vcs, track_b_vcs, top_5_deep_dives, outreach_hooks, data_quality_flags
}
json.dump(result, open('/tmp/vc-final-list.json', 'w'), indent=2)
print(f'Synthesis written. Curated: {len(result.get("curated_vcs", []))} VCs. Track A: {len(result.get("track_a_vcs", []))} VCs. Track B: {len(result.get("track_b_vcs", []))} VCs.')
PYEOF
Step 10: Self-QA
python3 << 'PYEOF'
import json
result = json.load(open('/tmp/vc-final-list.json'))
failures = []
# Remove Track A VCs missing evidence_company
original_a = len(result.get('track_a_vcs', []))
result['track_a_vcs'] = [v for v in result.get('track_a_vcs', []) if v.get('evidence_company')]
removed_a = original_a - len(result['track_a_vcs'])
if removed_a > 0:
failures.append(f'Removed {removed_a} Track A VC(s) missing evidence_company')
# Remove Track B VCs missing thesis_source_title
original_b = len(result.get('track_b_vcs', []))
result['track_b_vcs'] = [v for v in result.get('track_b_vcs', []) if v.get('thesis_source_title')]
removed_b = original_b - len(result['track_b_vcs'])
if removed_b > 0:
failures.append(f'Removed {removed_b} Track B VC(s) missing thesis_source_title')
# Remove deep dives for VCs that were stripped from all tracks
valid_funds = (
{v['fund_name'] for v in result.get('curated_vcs', [])} |
{v['fund_name'] for v in result.get('track_a_vcs', [])} |
{v['fund_name'] for v in result.get('track_b_vcs', [])}
)
original_dives = len(result.get('top_5_deep_dives', []))
result['top_5_deep_dives'] = [d for d in result.get('top_5_deep_dives', []) if d.get('fund_name') in valid_funds]
removed_dives = original_dives - len(result['top_5_deep_dives'])
if removed_dives > 0:
failures.append(f'Removed {removed_dives} deep dive(s) for funds stripped during QA')
# Check top 5 deep dives
dives = result.get('top_5_deep_dives', [])
if len(dives) < 5:
failures.append(f'Only {len(dives)} deep dives (expected 5) -- insufficient search data')
for dd in dives:
if not dd.get('how_to_approach') or len(dd.get('how_to_approach', '')) < 30:
dd['how_to_approach'] = 'Approach method not determinable from search data. Check the fund website directly for application instructions.'
failures.append(f"Fixed: '{dd.get('fund_name')}' had missing how_to_approach")
if not dd.get('fund_overview') or dd.get('fund_overview') == '':
dd['fund_overview'] = 'not found in search data'
# Check outreach hooks count
if len(result.get('outreach_hooks', [])) != 3:
failures.append(f"Expected 3 outreach hooks, got {len(result.get('outreach_hooks', []))}")
# Check for em dashes
full_text = json.dumps(result)
if '—' in full_text:
result = json.loads(full_text.replace('—', '-'))
failures.append('Fixed: em dash characters replaced with hyphens')
# Check for forbidden words
forbidden = ['powerful', 'robust', 'seamless', 'innovative', 'game-changing', 'streamline', 'leverage', 'transform']
full_text_lower = json.dumps(result).lower()
for word in forbidden:
if word in full_text_lower:
failures.append(f"Warning: forbidden word '{word}' found in output -- review before presenting")
# Flag any "not found in search data" entries so user knows coverage is incomplete
not_found_count = json.dumps(result).count('not found in search data')
if not_found_count > 0:
failures.append(f'INFO: {not_found_count} field(s) marked "not found in search data" -- verify directly before outreach')
if 'data_quality_flags' not in result:
result['data_quality_flags'] = []
result['data_quality_flags'].extend(failures)
json.dump(result, open('/tmp/vc-final-list.json', 'w'), indent=2)
print(f'QA complete. Issues addressed: {len(failures)}')
for f in failures:
print(f' - {f}')
if not failures:
print('All QA checks passed.')
PYEOF
Step 11: Save and Present Output
DATE=$(date +%Y-%m-%d)
OUTPUT_FILE="docs/vc-intel/${PRODUCT_SLUG}-${DATE}.md"
mkdir -p docs/vc-intel
Present the final output:
## VC Finder: [product_name]
Date: [today] | Stage: [detected_stage] ([stage_confidence] confidence) | Geography: [geography_bias]
---
### Product Analysis
What it does: [one_line_description]
Industry: [l1] > [l2] > [l3]
Buyer: [buyer_persona] at [company_type], [company_size]
Comparable companies used: [comma-separated list, noting source_type for each]
---
### Curated Matches (Verified)
*Funds matched from a verified dataset of 25 VC funds sourced from fund websites. Zero hallucination -- details come directly from the dataset.*
| Fund | Confidence | Stage Focus | Check Size | Matched Tags |
|---|---|---|---|---|
[one row per curated VC, sorted by confidence then score]
---
### Track A: VCs Who Backed Similar Companies
*These investors have already written a check in this space. Evidence from live Tavily search.*
| Fund | Backed Comparable | Stage Focus | Check Size | Fit Score | Approach |
|---|---|---|---|---|---|
[one row per Track A VC, sorted by space_fit_score descending]
---
### Track B: Thesis-Led Investors
*These investors are actively publishing about this space.*
| Fund | Thesis Source | Stage Focus | Check Size | Fit Score | Approach |
|---|---|---|---|---|---|
[one row per Track B VC, sorted by space_fit_score descending]
---
### Top 5 Deep Dives
#### [N]. [Fund Name] (Track [Curated/A/B])
Overview: [fund_overview -- from dataset or search data only]
Why it fits: [why_fit]
Portfolio in this space: [from dataset or search data, or "not found in search data"]
How to approach: [how_to_approach]
Outreach hook: "[outreach_hook]"
[repeat for all available deep dives]
---
### 3 Outreach Hooks for This Product Type
**1. [hook_type]**
[hook_text]
Best for: [best_for]
[repeat for all 3]
---
Data quality notes: [data_quality_flags, or "None"]
Saved to: docs/vc-intel/[PRODUCT_SLUG]-[DATE].md
Clean up temp files:
rm -f /tmp/vc-product-raw.md /tmp/vc-stage-signals.json /tmp/vc-product-analysis.json \
/tmp/vc-product-context.json /tmp/vc-curated-matches.json /tmp/vc-comparable-search.json \
/tmp/vc-tracka-results.json /tmp/vc-trackb-results.json /tmp/vc-final-list.json
