Warm Intro & Referral Connection Finder (Laya & JEV AI) avatar

Warm Intro & Referral Connection Finder (Laya & JEV AI)

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Pay per usage

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Warm Intro & Referral Connection Finder (Laya & JEV AI)

Warm Intro & Referral Connection Finder (Laya & JEV AI)

Map warm introduction paths to target accounts via mutual investors, alumni networks, and board members. Turn cold outreach into trusted referrals.

Pricing

Pay per usage

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Developer

Jona

Jona

Maintained by Community

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0

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2

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1

Monthly active users

7 days ago

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Referral Intelligence — Map Warm Introduction Paths

Warm introductions convert 4x higher than cold outreach. Discover who can introduce you.

Referral Intelligence uncovers warm introduction paths, mutual connections, alumni overlaps, shared investors, and board ties into target accounts. Powered by Laya System-1 Decision Intelligence, it scores referral strength and crafts customized introduction request templates.


Core Question Answered:

Who can introduce me to key decision-makers at this target company?


Key Capabilities

  • Automated Network Mapping:
    • Uncovers investor networks (shared VC/accelerators like Y Combinator, Sequoia, a16z).
    • Identifies university alumni and past company overlap (e.g. ex-Google, ex-Stripe, ex-Meta).
    • Maps board members, advisors, and corporate press affiliations.
  • Referral Strength Scoring (0–100):
    • Categorizes paths into Tier 1 (Strong/Direct), Tier 2 (Moderate/Indirect), and Tier 3 (Weak/Affiliation).
    • Weights closeness of connection (co-investor vs. shared university).
  • Ready-to-Send Intro Requests:
    • Automatically drafts the exact forwardable blurbs and introduction request emails to send to your mutual connector.
  • Actor Chaining:
    • Direct upstream ingestion from Business Opportunity Finder or AI Prospect Intelligence.

Example Input (JSON)

{
"yourCompanyName": "Eternal Labs",
"yourCompanyDomain": "eternallabs.ai",
"connectionSources": [
"investors",
"alumni",
"board",
"advisors"
],
"minReferralStrength": 30,
"maxCompanies": 5,
"decisionEngine": "laya",
"targetCompanies": [
{
"name": "Stripe",
"website": "https://stripe.com",
"industry": "Fintech",
"location": "San Francisco, CA"
},
{
"name": "Datadog",
"website": "https://datadoghq.com",
"industry": "Cloud Monitoring",
"location": "New York, NY"
},
{
"name": "Figma",
"website": "https://figma.com",
"industry": "Design Software",
"location": "San Francisco, CA"
}
]
}

Example Output (JSON)

{
"target_company": "Stripe",
"target_website": "https://stripe.com",
"target_industry": "Fintech",
"referral_strength_score": 88,
"referral_tier": "strong",
"paths_count": 3,
"best_path": {
"connector_name": "Sequoia Capital",
"connector_role": "Shared Investor",
"path_type": "investor",
"strength_score": 92,
"rationale": "Both companies share early-stage backing from Sequoia Capital."
},
"intro_request_blurb": "Hey [Connector], noticed you're connected to the leadership team at Stripe. We've built an AI lead discovery engine that solves their infrastructure scaling bottleneck — could you introduce me to their engineering leadership?",
"all_paths": [
{
"connector_name": "Sequoia Capital",
"connector_role": "Shared Investor",
"path_type": "investor",
"strength_score": 92
},
{
"connector_name": "Stanford University Alumni",
"connector_role": "Founding Team Overlap",
"path_type": "alumni",
"strength_score": 75
}
]
}