News Signal Outreach

News Signal Outreach

The catch-all signal composite. Every other composite handles a specific signal type (funding, hiring, leadership change, champion move). This one handles everything else — any piece of news or public event that could create a reason to reach out.

A regulation change. A product recall. A competitor acquisition. A market expansion. A layoff. An earnings miss. A new partnership. An industry report. A conference keynote. A viral LinkedIn post. Any external event that shifts a company's priorities, creates urgency, or opens a window for your product.

Why this composite exists: The world generates an infinite stream of potential outreach triggers. The four structured signal composites handle the most common patterns. This composite handles the long tail — the unpredictable, opportunistic moments that often produce the best outreach because nobody else is sending a templated sequence about them.

When to Auto-Load

Load this composite when ANY of these are true:

• User shares ANY URL (LinkedIn post, article, tweet, blog, news) and asks about a company or person mentioned in it
• User says "came across", "saw this post", "found this article", "check this out", "is this relevant", "is this company a fit", "should we reach out"
• User mentions a company or person they discovered from an external source (social media, news, conference, podcast, newsletter) and asks about relevance or fit
• User asks "can we reach out to anyone based on this?"
• User says "check if this news is relevant to our prospects", "news-based outreach", "trigger-based outreach"
• User has a list of companies and wants to check recent news for outreach angles
• The news doesn't fit neatly into funding, hiring, leadership change, or champion move categories
• An upstream workflow surfaces a news item that needs evaluation

Key principle: If the user shares an external signal (URL, post, article, mention) and asks ANY question about the companies/people in it — load this composite. Don't wait for the word "outreach." The composite handles both evaluation-only (Steps 1-3) and full outreach (Steps 1-6).

Input Flexibility

This composite accepts three input modes:

Step 0: Configuration (One-Time Setup)

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

ICP Definition

Your Company Context

Examples of relevant_news_categories:

# For a cybersecurity product:
relevant_news_categories: ["data breach", "compliance regulation", "security incident",
  "digital transformation", "cloud migration", "IPO/going public"]

# For a sales AI product:
relevant_news_categories: ["sales team scaling", "market expansion", "new product launch",
  "competitor acquisition", "cost cutting", "revenue miss"]

# For an HR tech product:
relevant_news_categories: ["layoffs", "rapid hiring", "remote work policy",
  "DEI initiative", "union activity", "culture crisis"]

Signal Detection Config

Contact Finding & Outreach Config

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

Step 1: Parse & Extract

Purpose: Take the raw news input — whatever form it arrives in — and extract structured entities (companies, people) and the core event.

Input Contract

Three modes:

Mode A: News → Companies/People

news_input: {
  mode: "news_to_targets"
  items: [
    {
      type: "url" | "text" | "structured"
      content: string               # URL to article, raw text, or structured summary
      source: string | null          # "TechCrunch", "LinkedIn post", "user provided", etc.
    }
  ]
}

Mode B: Companies → News

news_input: {
  mode: "targets_to_news"
  companies: [
    {
      name: string
      domain: string
      industry?: string
    }
  ]
  lookback_days: integer
}

Mode C: People → News

news_input: {
  mode: "people_to_news"
  people: [
    {
      full_name: string
      company: string
      linkedin_url?: string
    }
  ]
  lookback_days: integer
}

Process

Mode A: News → Companies/People

1. Fetch and parse the news content:

   • If URL → fetch the page, extract article text
   • If raw text → use as-is
   • If structured → use as-is

2. Extract entities:

   • Companies mentioned (name, role in the story — subject, affected party, partner, competitor)
   • People mentioned (name, title, company, role in the story)
   • The core event (what happened, in one sentence)
   • Event category (regulation, acquisition, partnership, product launch, market event, crisis, expansion, contraction, etc.)
   • Date of event
   • Affected industries

3. Expand if needed: If the news implies a broader set of affected companies beyond those mentioned:

   • "New FDA regulation on telehealth" → all telehealth companies, not just ones in the article
   • "Major data breach at [company]" → the breached company AND their competitors (who can capitalize)
   • "Industry report shows X trend" → companies in that industry

Mode B: Companies → News

1. For each company, search for recent news using configured news_tool:

   • Web search: "{company_name}" AND (news OR announced OR launches OR raises OR expands OR partners) within lookback_days
   • Filter results against relevant_news_categories from config
   • Extract the same fields as Mode A for each news item found

2. Group results: Company → list of news items, ranked by relevance to your product

Mode C: People → News

1. For each person, search for recent news/activity:

   • Web search: "{full_name}" AND "{company}" within lookback_days
   • LinkedIn activity (if available): recent posts, shares, comments
   • Look for: promotions, speaking engagements, published articles, quoted in press, new projects

2. Group results: Person → list of news items/activity

Output Contract

extracted_signals: [
  {
    entity: {
      type: "company" | "person"
      name: string
      company: string               # Company name (same as name if type=company)
      domain: string | null
      role_in_news: string           # "subject", "affected", "partner", "competitor", "mentioned"
    }
    news: {
      headline: string              # One-line summary of what happened
      event_category: string        # "regulation", "acquisition", "expansion", "crisis", etc.
      event_date: string
      full_summary: string          # 2-3 sentence summary
      source_url: string | null
      affected_industries: string[]
    }
  }
]

Human Checkpoint

## Extracted Signals

Source: [news source/input description]
Event: [one-line summary]
Category: [event category]

### Companies/People Extracted
| Entity | Type | Role in News | Industry |
|--------|------|-------------|----------|
| Acme Corp | Company | Subject | Healthcare |
| Jane Doe | Person | Quoted (CEO) | Healthcare |
| HealthTech sector | Industry | Affected | Healthcare |

Also evaluating: X companies in [affected industry] not directly mentioned

Proceed with ICP qualification? (Y/n)

Step 2: Qualify Against ICP

Purpose: For each extracted entity, determine if they're an ICP fit. Drop companies/people that don't match. Pure LLM reasoning — inherently tool-agnostic.

Input Contract

extracted_signals: [...]              # From Step 1 output
icp_criteria: {
  target_industries: string[]
  target_company_size: string
  target_geographies: string[]
  disqualifiers: string[]
}
your_company: {
  description: string
  pain_point: string
}

Process

For each entity:

1. If entity is a company:

   • Check industry against target_industries
   • Estimate company size (from news context or quick web search)
   • Check geography if relevant
   • Check against disqualifiers
   • Result: Pass / Fail with reasoning

2. If entity is a person:

   • Identify their company
   • Qualify the company through the same ICP checks above
   • Additionally check: is this person's role relevant? (matches buyer_titles, champion_titles, or user_titles)
   • Result: Pass / Fail with reasoning

3. For entities implied but not mentioned (e.g., "all telehealth companies" from a regulation news):

   • Use web search or existing company lists to identify specific companies in the affected space
   • Qualify each against ICP
   • This step may surface new companies not in your existing pipeline

Output Contract

icp_qualified: [
  {
    entity: { ... }                   # From Step 1
    news: { ... }                     # From Step 1
    icp_assessment: {
      fit: "strong" | "moderate"
      industry_match: boolean
      size_match: boolean | "unknown"
      reasoning: string               # Why they're a fit
    }
  }
]
icp_disqualified: [
  {
    entity_name: string
    reason: string
  }
]

Human Checkpoint

## ICP Qualification

### Qualified (X entities)
| Entity | Type | Industry | Size | ICP Fit | Reasoning |
|--------|------|----------|------|---------|-----------|
| Acme Corp | Company | Healthcare SaaS | ~200 | Strong | Core ICP industry, right size |
| MedTech Inc | Company | HealthTech | ~500 | Moderate | Adjacent industry, large |

### Disqualified (X entities)
| Entity | Reason |
|--------|--------|
| BigPharma Co | Enterprise (50K+ employees) — above target size |

Approve qualified list?

Step 3: Identify Connection Angle

Purpose: This is the critical thinking step. For each ICP-qualified entity, determine the specific connection between the news event and your product. Why should they care about your product RIGHT NOW because of THIS news? Pure LLM reasoning — inherently tool-agnostic.

Input Contract

icp_qualified: [...]                  # From Step 2 output
your_company: {
  description: string
  pain_point: string
  product_outcomes: string[]
  proof_points: string[]
  relevant_news_categories: string[]
}

Process

For each qualified entity, answer three questions:

Question 1: "Why does this news create urgency for our product?"

Map the news event category to a product relevance pattern:

Question 2: "What's the specific angle?"

Craft a one-sentence connection:

"Because [news event], [company] now needs [specific outcome your product delivers]."

Examples:

• "Because Acme just acquired BetaCo, they need to unify two separate CRM systems — exactly what [product] does in 30 days."
• "Because the new HIPAA amendment takes effect in Q3, [company] needs to audit their data handling — [product] automates this."
• "Because [person] just posted about struggling with [pain], they're actively looking for a solution — [product] solves this."

Question 3: "How strong is this connection?"

Output Contract

connection_angles: [
  {
    entity: { ... }
    news: { ... }
    icp_assessment: { ... }
    connection: {
      urgency_reason: string          # Why this news creates urgency
      specific_angle: string          # One-sentence connection
      connection_strength: "direct" | "adjacent" | "thematic"
      timing_note: string             # How time-sensitive this outreach is
      recommended_framework: string   # Which email framework fits best
    }
  }
]

Framework Selection Based on Connection Strength

Human Checkpoint

## Connection Angles

### Direct Connections (X entities) — Act quickly
| Entity | News | Angle | Timing |
|--------|------|-------|--------|
| Acme Corp | Acquired BetaCo | "Need to unify CRM systems — [product] does this in 30 days" | This week (integration planning starts immediately) |

### Adjacent Connections (X entities)
| Entity | News | Angle | Timing |
|--------|------|-------|--------|
| MedTech Inc | Expanding to EMEA | "Localized [function] becomes a requirement — [product] supports 15 languages" | This month |

### Thematic Connections (X entities)
| Entity | News | Angle | Timing |
|--------|------|-------|--------|
| HealthCo | Industry report on [trend] | "They're likely evaluating [category] solutions" | Flexible |

Approve these angles before we find contacts?

Step 4: Find Relevant People

Purpose: For each qualified entity with a connection angle, find the right people to contact.

Input Contract

connection_angles: [...]              # From Step 3 output
buyer_titles: string[]                # From config
champion_titles: string[]             # From config
user_titles: string[]                 # From config
max_contacts_per_company: integer     # Default: 3-5