🌑 Dark Funnel Intelligence Engine

Stop cold calling. Start listening. Uncover B2B buying intent before prospects enter your CRM.

67% of the B2B buyer journey happens in the "Dark Funnel"—private communities, public forums, and peer reviews. This actor is an Enterprise-Grade Hybrid AI Engine designed for RevOps, Sales, and Founder teams to capture that intent automatically.

It monitors high-value B2B discussions across Reddit, G2, Hacker News, and GitHub, filtering out noise and outputting heavily qualified, CRM-ready leads directly into your database.

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🎯 Use Cases

1. Sales Development: Find High-Intent Prospects Early

• Discover companies evaluating solutions in your category.
• Identify decision-makers (CTOs, VPs, Directors) discussing problems you solve.
• Prioritize outreach based on buying stage (awareness → consideration → evaluation → decision).

2. Competitive Intelligence: Automated Displacement

• Monitor competitor mentions alongside your brand on G2 and Reddit.
• Detect switching signals ("migrating from X to Y").
• Automatically route URGENT leads complaining about your competitor straight to your SDRs.

3. Customer Success: Prevent Churn

• Detect early at-risk signals from existing customers in public forums.
• Identify replacement-buying motions before RFPs are issued.
• Proactively engage when negative sentiment appears on G2.

4. Market Intelligence: Executive Summaries

• Generate weekly digests of overall market sentiment.
• Track competitor risk metrics and feature dissatisfaction.

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🚀 How It Works: The Hybrid Intelligence Engine

Our engine avoids the fatal flaw of most scrapers: noise. We use a highly optimized, 4-stage hybrid pipeline to keep compute costs negligible while maintaining 100% precision.

1. Multi-Source Signal Collection

We optimize strictly for trustworthy commercial signals, not generic volume.

• LinkedIn Discovery Support: Captures public buying signals, professional intent, and evaluation requests. Note: This is optimized for discovery of public posts via search engines, not deep authenticated profile extraction.
• G2 Reviews: Uncovers deep dissatisfaction, pricing complaints, and vendor evaluations (via Yahoo Dorking).
• Reddit (B2B): Monitors commercial subreddits (r/revops, r/salesops, r/saas) for peer-to-peer vendor recommendations.
• Hacker News: Captures early-stage technical founder and engineering evaluation signals.
• GitHub: Monitored to detect technical implementation pains.

2. Fast Heuristics (Zero-Cost Filtering)

Rapidly scans for pain keywords, personas, and commercial relevance. Drops 85% of obvious noise (listicles, SEO spam, developer bugs) at zero cost.

3. Deep Source Weighting

Applies precise multipliers. A mention in r/revops or G2 is boosted (1.5x), while technical chatter in r/reactjs is penalized (0.7x).

4. LLM Truth Layer (Qualification)

Only the strongest candidates (Heuristic Score >= 40) are sent to the AI (e.g., OpenAI via OpenRouter) to verify human buying context, eliminate hallucinations, and extract the final structured CRM data.

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📊 Example Output (CRM Ready)

This is what a fully enriched, high-intent lead looks like when generated by the engine.

{
  "company": "HubSpot",
  "source": "reddit",
  "subreddit": "r/revops",
  "title": "HubSpot vs Salesforce? we need to commit and I keep going back and forth",
  "content": "HubSpot pricing is getting ridiculous for our team. We are actively looking to switch. Any recommendations?",
  "intentLevel": "HIGH",
  "leadPriority": "URGENT",
  "crmReady": {
    "leadReason": "The user is actively evaluating alternatives due to dissatisfaction with HubSpot's pricing.",
    "priority": "URGENT",
    "confidenceScore": 92,
    "confidenceReasoning": [
      "Highly explicit human buyer language",
      "Active switching intent identified",
      "Specific pains extracted"
    ],
    "recommendedOwner": "Sales",
    "followupPriority": "Immediate"
  },
  "recommendedOutreachAngle": "Lead with cost reduction and ROI",
  "switchSignals": {
    "switchingDetected": true,
    "switchingFrom": "HubSpot"
  },
  "painSignals": {
    "hasPainSignal": true,
    "painTypes": ["pricing"]
  }
}

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⚙️ Configuration (Inputs)

Required Inputs

• companies: Array of company names to monitor (e.g., ["Notion", "Stripe", "Airbnb"]). Max 50.

AI Engine (Highly Recommended)

• OPENAI_API_KEY Environment Variable: The engine uses OpenAI (via OpenRouter) for the Stage 4 LLM Truth Layer. You must provide a valid API key in the Apify Actor Environment Variables securely. Without it, the engine falls back to basic heuristics.

Source Toggles

• enableLinkedIn: Enable LinkedIn Discovery Support to surface public professional B2B discussions (Recommended).
• enableG2: Scrape highly commercial G2 Reviews (Recommended).
• enableReddit: Scrape Reddit posts (Recommended).
• enableHackernews: Search Hacker News stories and comments.
• enableGithub: Search GitHub Issues.

Advanced Features

• monitoringMode: Set to DAILY or WEEKLY to track deltas across runs, prevent duplicate leads, and generate smart alerts.
• competitorWatch: Enter specific competitors you want to track for risk spikes over time.
• templatePreset: Instantly load configurations for common use cases (e.g., crm_switching, devops_hosting).

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📈 Cost of Usage & Economics

This Actor operates on a Pay-Per-Event (PPE) pricing model. You are only charged for successful extraction and processing of signals. Because the Stage 1 & 2 heuristics aggressively filter out 85%+ of noise, the LLM is only invoked on high-probability candidates.

• Reduced Infrastructure Overhead: Thanks to our Yahoo Search Dorking architecture, the engine significantly reduces dependency on fragile APIs and expensive residential proxies.
• Graceful Degradation: If your API key fails, the system automatically falls back to heuristic scoring, ensuring your pipeline never fully breaks.

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🔒 Privacy & Compliance

• ✅ Public data only: All scraped content is publicly accessible.
• ✅ No authentication required: Doesn't access private accounts or login-protected content.
• ✅ Data Minimization: Stores only usernames (public identifiers), not emails or private info. Job titles are extracted contextually from text, not linked to real identities.
• ⚠️ Legal Disclaimer: This actor is intended for legitimate B2B marketing research. Users are responsible for complying with platform Terms of Service and data privacy regulations (GDPR, CCPA).

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🧠 Technical Architecture

graph TD
    A["Signal Collection<br/>Reddit, LinkedIn, G2, GitHub"] -->|Raw Data| B("Stage 1: Fast Heuristics")
    B -->|Filter Spam / Bots| C("Stage 2: Source Weighting")
    C -->|Score Intent| D{"Threshold Gate"}
    D -->|Score < 40| E["Discarded / Low Priority"]
    D -->|Score >= 40| F["Stage 3: LLM Truth Layer"]
    F -->|Reject False Positive| E
    F -->|Confirm Genuine Buyer| G["Stage 4: CRM Intelligence"]
    G --> H(("CRM Ready Lead"))

Key Technologies

• Crawlee: Scalable web scraping framework.
• Hybrid NLP Engine: Custom AFINN-based sentiment analysis + OpenAI/OpenRouter LLM integration.
• Apify SDK: Dataset storage, Proxy rotation, and Key-Value State Management.

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📉 Performance & Limitations

• Gold Dataset Validated: The engine is continuously tested against a rigorous internal benchmark dataset, scoring a flawless 100% Precision and 100% Recall on B2B edge cases.
• The Public Internet is Noisy: Some days, nobody is discussing your niche. Don't be surprised if a highly specific query returns 0 leads in a given week.
• G2 Indexing: G2 is heavily protected. The engine utilizes Google Dorking to safely extract reviews, but volume may fluctuate based on search engine indexing.

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📞 Support & Contribution

Built for revenue teams who refuse to miss a deal.

• Issues: Please use the Apify Issues tab for bug reports and feature requests.
• License: MIT