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:

Mode	Input	Example
News → Companies	A news item. Extract companies/people mentioned, qualify them.	"Here's an article about new FDA regulations on telehealth"
Companies → News	A list of companies. Find recent news about them, evaluate relevance.	"Check these 50 companies for any news we can use as an outreach angle"
Person → News	A person or list of people. Find recent news about them or their company, evaluate relevance.	"Check if any of these prospects have been in the news"

---

Step 0: Configuration (One-Time Setup)

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

ICP Definition

Question	Purpose	Stored As
What does your company do? (1-2 sentences)	Relevance matching	company_description
What problem do you solve?	Connection angle identification	pain_point
What industries do you sell to?	ICP filter	target_industries
What company sizes?	ICP filter	target_company_size
What geographies?	ICP filter (optional)	target_geographies
Any disqualifiers?	Hard no's	disqualifiers
Who are your buyers? (titles)	Contact finding	buyer_titles
Who are your champions? (titles)	Contact finding	champion_titles
Who are your users? (titles)	Contact finding	user_titles

Your Company Context

Question	Purpose	Stored As
What specific outcomes does your product deliver?	Relevance angle building	product_outcomes
Name 2-3 proof points (customers, metrics)	Email credibility	proof_points
What categories of news are most relevant to your product?	Helps prioritize	relevant_news_categories

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

Question	Options	Stored As
How should we find news?	Web search / Google News / RSS feeds / Social media	news_tool
How far back should we look? (when scanning companies for news)	7 / 14 / 30 / 60 days	lookback_days

Contact Finding & Outreach Config

Question	Options	Stored As
How should we find contacts?	Apollo / LinkedIn / Clearbit / Web search	contact_tool
Where do you want outreach sent?	Smartlead / Instantly / Outreach.io / CSV export	outreach_tool
Email or multi-channel?	Email only / Email + LinkedIn	outreach_channels

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:

Event Category	How It Creates Urgency	Example
Regulation change	They need to comply, your product helps them comply or adapt faster	"New data privacy law → they need [your compliance tool] before enforcement date"
Acquisition / Merger	Systems need integration, processes need standardization, new leadership evaluates stack	"Acquired a company → need to unify [function your product handles]"
Market expansion	New market = new challenges, need tools that scale	"Expanding to EMEA → need [your product] for localized [function]"
Product launch	Scaling up means scaling operations	"Launching enterprise tier → need [your product] to handle enterprise [function]"
Competitive pressure	Competitor moved, they need to respond	"Competitor launched [X] → they need to level up [area your product addresses]"
Cost cutting / Layoffs	Do more with less, automation becomes essential	"Cut 15% of staff → need [your product] to maintain output with smaller team"
Crisis / Incident	Reactive buying — they need a solution NOW	"Data breach → urgently need [your security product]"
Partnership	New partner = new workflows, new opportunities	"Partnered with [company] → need [your product] to support the integration"
Earnings / Growth	Over-performing = scaling challenges. Under-performing = efficiency pressure	"Revenue grew 3x → [function your product handles] can't keep up manually"
Industry trend / Report	Category awareness is high, they're thinking about this	"Industry report says [trend] → they're likely evaluating solutions in this space"
Person-level news	Published an article, spoke at a conference, posted on LinkedIn about a topic you solve	"Posted about [pain] → they're actively thinking about this problem"

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?"

Strength	Criteria	Example
Direct	The news explicitly describes a problem your product solves	Layoff in your product's department → they need automation
Adjacent	The news implies a downstream need your product addresses	Market expansion → implies scaling, which implies need for your tool
Thematic	The news is in the same category as your product's domain	Industry report about the trend you're in → awareness play

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

Connection Strength	Recommended Framework	Why
Direct	Signal-Proof-Ask	The news IS the hook — reference it directly, show proof, ask
Adjacent	PAS	Problem (implied by the news) → Agitate (what happens if they don't act) → Solve
Thematic	AIDA	Attention (news reference) → Interest (how it relates to them) → Desire (your product) → Action

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

Process

1. If the entity is already a person (Mode C or person mentioned in news):

   • They're the primary contact. Still find 1-2 additional contacts at their company (buyer if they're a champion, champion if they're a buyer) for multi-threading.

2. If the entity is a company:

   • Use configured contact_tool to find people matching buyer_titles, champion_titles, user_titles
   • Prioritize people whose role is closest to the news event:

   News Category	Prioritize These Contacts
   Regulation / Compliance	Legal, Compliance, Operations leadership
   Acquisition / Merger	COO, CTO, VP Operations, Integration leads
   Market expansion	VP Sales, VP Marketing, Country/Regional leads
   Cost cutting / Layoffs	COO, CFO, VP Operations
   Product launch	CTO, VP Product, VP Engineering
   Crisis / Incident	CISO, VP Engineering, CTO (for security), CEO/COO (for operational)
   General growth	Default to buyer_titles from config

3. For each contact, note their relevance to the news:

   • Are they directly affected by the news? (Their department, their function)
   • Are they the decision-maker for the response to this news?
   • Are they the person who will feel the pain this news creates?

Output Contract

contacts: [
  {
    person: {
      full_name: string
      first_name: string
      last_name: string
      title: string
      email: string | null
      linkedin_url: string | null
      role_type: "buyer" | "champion" | "user"
      news_relevance: string         # Why THIS person for THIS news
    }
    company: {
      name: string
      domain: string
    }
    connection: {
      specific_angle: string
      connection_strength: string
      urgency_reason: string
    }
    news: {
      headline: string
      event_category: string
      source_url: string | null
    }
  }
]

Human Checkpoint

## Contacts Found

### Acme Corp — "Acquired BetaCo" (Direct connection)
| Name | Title | Role | Why This Person |
|------|-------|------|----------------|
| Sarah Kim | COO | Buyer | Owns post-acquisition integration |
| David Park | VP Operations | Champion | Will manage unified workflows |
| Amy Chen | Director of Sales Ops | User | Directly affected by CRM unification |

### MedTech Inc — "Expanding to EMEA" (Adjacent connection)
| ... |

Total: X contacts across Y companies

Approve before we draft emails?

---

Step 5: Draft Personalized Outreach

Purpose: Draft outreach where the news event is the hook, your product is the solution, and the email demonstrates you understand their specific situation. Pure LLM reasoning — inherently tool-agnostic.

Input Contract

contacts: [...]                       # From Step 4 output
your_company: {
  description: string
  pain_point: string
  product_outcomes: string[]
  proof_points: string[]
}
sequence_config: {
  touches: integer                    # Default: 3
  timing: integer[]                   # Default varies by connection strength (see below)
  tone: string                       # Default: "casual-direct"
  cta: string                       # Default: "15-min call"
}

Process

1. Adjust sequence timing by connection strength:

   Strength	Timing	Rationale
   Direct	Day 1 / 3 / 7	Urgency is real — they're actively dealing with this
   Adjacent	Day 1 / 5 / 12	Standard timing — urgency is implied, not immediate
   Thematic	Day 1 / 7 / 14	Slower cadence — this is awareness, not crisis response

2. Build the email around the news, not the product:

   The news is the subject. Your product is the punchline. Never lead with the product.

   Element	Source	How to Use
   News hook	Step 1 news.headline	Open with what happened — show you're informed
   Impact on them	Step 3 connection.urgency_reason	Explain what this means for their specific role
   Your angle	Step 3 connection.specific_angle	Connect the dots to your product naturally
   Proof	Config proof_points	Show a peer who faced a similar situation
   CTA	Config	Low-friction ask

3. Email structure by connection strength:

   Direct connection (Signal-Proof-Ask):

   Hook: Reference the specific news event
   Impact: What this means for them (1 sentence)
   Proof: A peer who faced the same situation and used your product
   Ask: Soft CTA
   

   Adjacent connection (PAS):

   Problem: The downstream challenge the news creates
   Agitate: What happens if they don't address it (1 sentence)
   Solve: How your product helps, with a proof point
   Ask: Soft CTA
   

   Thematic connection (AIDA):

   Attention: Reference the news/trend
   Interest: How it relates to their company specifically
   Desire: What your product does in this context
   Action: Soft CTA
   

4. Personalization layers:

   Layer	What Gets Personalized	Source
   News reference	The specific event and its relevance	Step 1 news data
   Company context	What their company does, their industry, their situation	Step 2 ICP research
   Role context	Why THIS person cares about this news	Step 4 news_relevance
   Your company fit	How your product specifically helps in this scenario	Step 3 connection angle

5. Follow email-drafting skill hard rules. Additionally:

   • Never sensationalize negative news. If the signal is a layoff, breach, or crisis, be empathetic, not opportunistic. "I know this is a challenging time" not "Your layoffs mean you need our tool!"
   • Don't pretend you just happened to see the news. Be direct: "Saw the news about [event]" not "I came across an interesting article."
   • If the news is about a crisis, wait 48-72 hours before reaching out. Immediate outreach during a crisis looks predatory.

Output Contract