Funding Signal Outreach

Funding Signal Outreach

Detects recent funding events across a set of companies, qualifies them against your company's context, finds the right people to reach out to, and drafts personalized emails. The full chain from signal to outreach-ready.

When to Auto-Load

Load this composite when:

• User says "check if any of these companies raised funding", "funding signal outreach", "reach out to recently funded companies"
• User has a list of companies and wants to act on funding signals
• An upstream workflow (TAM Pulse, company monitoring) triggers a funding signal check

Architecture

This composite is tool-agnostic. Each step defines a data contract (what goes in, what comes out). The specific tools that fulfill each step are configured once per client/user, not asked every run.

┌─────────────────────────────────────────────────────────────────┐
│                  FUNDING SIGNAL OUTREACH                        │
│                                                                 │
│  ┌──────────┐   ┌──────────┐   ┌──────────┐   ┌──────────┐    │
│  │  DETECT  │──▶│ QUALIFY  │──▶│  FIND    │──▶│  DRAFT   │    │
│  │ Funding  │   │ & Rank   │   │  People  │   │  Emails  │    │
│  └──────────┘   └──────────┘   └──────────┘   └──────────┘    │
│       │              │              │              │            │
│  Input: companies  + your company  + buyer       + signal      │
│  Tool: web search    context        personas      context      │
│    or apollo         (LLM)         Tool: apollo   (LLM)       │
│    or crunchbase                     or linkedin              │
│    or any                            or clearbit              │
│                                      or any                   │
└─────────────────────────────────────────────────────────────────┘

Step 0: Configuration (One-Time Setup)

On first run for a client/user, collect and store these preferences. Skip on subsequent runs.

Company Source Config

Signal Detection Config

Contact Finding Config

Outreach Config

Your Company Context

Store config in: clients/<client-name>/config/signal-outreach.json or equivalent.

Step 1: Detect Funding Signals

Purpose: For each company in the input list, determine if they have raised funding within the lookback window.

Input Contract

companies: [
  {
    name: string          # Required
    domain: string        # Required
    industry?: string     # Optional, helps qualification
    size?: string         # Optional
    location?: string     # Optional
  }
]
lookback_days: integer    # From config (default: 30)

Process

For each company (or in batches):

1. Search for funding announcements using the configured signal_tool:

   • Web search: Query "{company_name}" AND ("raised" OR "funding" OR "Series") AND "2026" for each company
   • Apollo: Use company enrichment endpoint to pull funding data
   • Crunchbase: Query funding rounds API filtered by date
   • Any other tool: Must return the same output contract

2. Extract funding details from results:

   • Did they raise? (yes/no)
   • How much?
   • What stage? (Seed, A, B, C, D+)
   • When? (exact date or approximate)
   • Who led the round? (investors)
   • Source URL (for verification)

3. Filter: Drop companies with no funding signal detected.

Output Contract

funded_companies: [
  {
    name: string
    domain: string
    industry: string
    funding_amount: string        # e.g. "$15M"
    funding_stage: string         # e.g. "Series A"
    funding_date: string          # ISO date or "March 2026"
    lead_investors: string[]      # e.g. ["Sequoia", "a16z"]
    source_url: string            # Link to announcement
    confidence: "high" | "medium" # High = multiple sources or official PR
    original_company_data: object # Pass through all original fields
  }
]

Human Checkpoint

Present results as a table:

Found funding signals for X of Y companies:

| Company | Amount | Stage | Date | Investors | Confidence |
|---------|--------|-------|------|-----------|------------|
| Acme    | $15M   | Series A | 2026-02-15 | Sequoia | High |
| ...     | ...    | ...   | ...  | ...       | ...        |

Proceed with qualification? (Y/n)

Step 2: Qualify & Prioritize

Purpose: Given funded companies + your company context, rank them by outreach priority. This step is pure LLM reasoning — inherently tool-agnostic.

Input Contract

funded_companies: [...]           # From Step 1 output
your_company: {
  description: string             # From config
  pain_point: string              # From config
  buyer_personas: string[]        # From config
  proof_points: string[]          # From config
  price_tier: string              # From config
}

Process

For each funded company, evaluate:

Scoring

Assign each company a priority tier:

• Tier 1 (Act Today): Stage fit + industry relevance + funded within 14 days
• Tier 2 (Act This Week): Two of three criteria met, or funded 15-30 days ago with strong fit
• Tier 3 (Queue): Marginal fit or funding 30+ days old. Worth reaching out but not urgent.
• Drop: No relevance to your product/market. Remove from pipeline.

For each qualified company, generate:

• Relevance reasoning: 1-2 sentences on why this company would care about your product right now
• Outreach angle: The specific hook connecting their funding to your product's value
• Recommended approach: Direct pain-point, aspirational growth, or operational efficiency framing

Output Contract

qualified_companies: [
  {
    ...funded_company_fields,
    priority_tier: "tier_1" | "tier_2" | "tier_3"
    relevance_reasoning: string
    outreach_angle: string
    recommended_approach: string
    estimated_team_size: string    # Post-raise estimate
  }
]
dropped_companies: [
  {
    name: string
    drop_reason: string
  }
]

Human Checkpoint

Present qualified companies grouped by tier:

## Qualification Results

### Tier 1 — Act Today (X companies)
| Company | Stage | Amount | Angle | Why |
|---------|-------|--------|-------|-----|
| ...     | ...   | ...    | ...   | ... |

### Tier 2 — Act This Week (X companies)
| ... |

### Tier 3 — Queue (X companies)
| ... |

### Dropped (X companies)
| Company | Reason |
|---------|--------|
| ...     | ...    |

Approve this list before we find contacts? You can promote, demote, or drop any company.

Step 3: Find Relevant People

Purpose: For each qualified company, find the right people to contact based on your buyer personas.

Input Contract

qualified_companies: [...]        # From Step 2 output
buyer_personas: [                 # From config
  {
    title_patterns: string[]      # e.g. ["VP Sales", "Head of Revenue", "CRO"]
    department: string            # e.g. "Sales", "Engineering"
    seniority: string             # e.g. "VP+", "Director+", "Manager+"
    role_type: "buyer" | "champion" | "user"
  }
]
max_contacts_per_company: integer # Default: 3-5

Process

For each qualified company, use the configured contact_tool:

1. Search for people matching buyer personas:

   • Apollo: People search with company domain + title filters
   • LinkedIn Sales Nav: Company page → filter by title/seniority
   • Clearbit: Prospector API with role filters
   • Web search: site:linkedin.com/in "{company}" "{title}" queries
   • Any other tool: Must return the same output contract

2. For each person found, collect:

   • Full name
   • Current title
   • Email (if available from the tool)
   • LinkedIn URL
   • Role type classification (buyer / champion / user)

3. Prioritize contacts within each company:

   • Buyers first (decision-makers who control budget)
   • Champions second (mid-level who feel the pain daily)
   • Users third (end-users who can advocate bottom-up)

4. Cap at max_contacts_per_company — typically 3-5 people per company to avoid carpet-bombing.