🌑 Dark Funnel Intelligence Engine

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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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🏗️ System Architecture & Data Flow

The engine uses a modular, 5-layer pipeline architecture designed for high throughput, low latency, and zero-cost noise rejection.

flowchart TD
    subgraph DS ["1. Multi-Source Scraping Layer"]
        A1["Reddit B2B"]
        A2["G2 Reviews"]
        A3["LinkedIn Discovery"]
        A4["Hacker News"]
        A5["GitHub Issues"]
        A6["News API"]
    end

    subgraph PRE ["2. Zero-Cost Noise Rejection"]
        B1["HTML Entity Decoding & Normalization"]
        B2["Content Fingerprinting & Deduplication"]
        B3["Negative Filter: Listicles / SEO / Bug Ads"]
        B4["Topic Relevance Thresholding"]
    end

    subgraph CLS ["3. Multidimensional NLP Engine"]
        C1["Intent Detector<br/>Budget / Timeline / Evaluation"]
        C2["Switching Detector<br/>Competitor Migration"]
        C3["Persona NER<br/>C-Suite / VP / Director"]
        C4["Compound Pain Engine<br/>Pricing + Vendor Lock"]
        C5["Sentiment Classifier<br/>AFINN Lexicon"]
    end

    subgraph SCR ["4. Lead Qualification & Scoring"]
        D1["Source Multipliers<br/>r/revops 1.5x / G2 1.5x"]
        D2["Recency Exponential Decay"]
        D3["Lead Priority Matrix<br/>URGENT / HIGH / MEDIUM"]
    end

    subgraph SNK ["5. Output & Delivery Layer"]
        E1["Apify Datasets"]
        E2["Webhook Batch Dispatcher"]
        E3["Executive Summaries & Alerts"]
    end

    DS --> PRE
    PRE -->|Accepted Signals| CLS
    CLS --> SCR
    SCR --> SNK

🧩 Core Architecture Components

Pipeline Layer	Component	Key Responsibilities
1. Scraping Layer		Scrapes public posts, reviews & search results across Reddit, G2, LinkedIn, HN, GitHub, and News.
2. Preprocessing		Drops ~85% of noise (SEO spam, listicles, developer bug reports) at zero cost using regex patterns and content hashes.
3. NLP Intelligence		Classifies buying stage (Awareness → Evaluation → Decision), detects competitor switching intent, extracts decision-maker job titles, and identifies compound pain points.
4. Scoring Engine		Computes lead priority (URGENT, HIGH, MEDIUM, LOW) using weighted source multipliers, star counts, and exponential recency decay.
5. Integration Layer		Formats CRM-ready JSON payloads, calculates executive summaries, triggers high-priority alerts, and dispatches batched Webhook notifications.

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🚀 How It Works: The 4-Stage Hybrid Pipeline

Our engine avoids the fatal flaw of standard web scrapers: drowning in noise.

1. Multi-Source Signal Collection

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

• LinkedIn Discovery: Captures public buying signals, professional intent, and vendor evaluation requests via search engine discovery.
• G2 Reviews: Uncovers deep dissatisfaction, pricing complaints, and vendor evaluations via Yahoo Dorking.
• Reddit (B2B): Monitors targeted subreddits (r/revops, r/salesops, r/saas) for peer-to-peer vendor recommendations.
• Hacker News: Captures early-stage technical founder and engineering evaluation signals via Algolia API.
• GitHub: Scans public issues to detect technical implementation pain and migration discussions.
• News: Scans publication feeds for market announcements and corporate moves.

2. Fast Heuristics (Zero-Cost Filtering)

Scans candidate texts for pain keywords, buyer personas, and commercial relevance. Drops 85%+ of noise (listicles, hiring posts, bug reports) before invoking heavier processing.

3. Deep Source Weighting & Recency Decay

Applies domain-specific multipliers:

• Source Multipliers: High-intent forums (r/revops, G2) get a 1.5x boost, while generic technical subreddits (r/reactjs) are scaled at 0.7x.
• GitHub Multipliers: Repos with >1k stars receive 1.2x, >5k stars receive 1.4x.
• Recency Decay: Exponential decay prioritizes fresh signals (100% at Day 0, ~40% at Day 30, ~7% at Day 90).

4. Compound Pain & Switch Signal Scoring

• Compound Pain Boost: Detects high-value pain combinations (e.g., pricing + vendor lock → 1.4x boost).
• Switch Signal Detection: Isolates explicit switching statements (e.g., "moving off Salesforce", "replacing HubSpot") to tag SDR leads as URGENT.

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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",
  "painComboBoost": true,
  "painSignals": {
    "hasPainSignal": true,
    "painTypes": ["pricing", "vendor_lock"],
    "compoundComboMatched": "pricing+vendor_lock"
  },
  "switchSignals": {
    "switchingDetected": true,
    "switchingFrom": "HubSpot"
  },
  "recommendedOutreachAngle": "Lead with cost reduction and easy migration",
  "createdAt": "2026-06-19T00:00:00.000Z"
}

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⚙️ Configuration (Inputs)

Required Inputs

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

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.

Webhook Integration (New!)

• webhookUrl: URL to send POST requests with high-intent signals (JSON payloads).
• webhookBatchSize: Number of high-intent signals to send per webhook request (1-100, default: 25).

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).
• skipLanguageFilter: If true, skip filtering out non-English content (for non-English markets).
• forceEnableAll: If true, bypass circuit breakers and enable all scrapers even if they've had consecutive failures (for debugging).
• maxRequestsPerCrawl: Max results per company per source (1-100, default: 5).

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🔗 Webhook Payload Format

When webhookUrl is configured, high-intent signals are sent in batches:

{
  "event": "high_intent_signal",
  "signals": [
    { /* CRM-ready signal object */ }
  ],
  "actorRunId": "your-actor-run-id",
  "timestamp": "2026-06-19T00:00:00.000Z"
}

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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

┌─────────────────────────────────────────────────────────┐
│                 MULTI-SOURCE INGESTION                  │
│  [Reddit]     [LinkedIn]    [G2 Reviews]    [GitHub]    │
└───────────────────────────┬─────────────────────────────┘
                            │ Raw Unstructured Text
                            ▼
┌─────────────────────────────────────────────────────────┐
│              STAGE 1 & 2: FAST HEURISTICS               │
│  • Deduplication & Spam Filtering                       │
│  • NLP Keyword & Sentiment Analysis                     │
│  • Persona & Entity Extraction                          │
└───────────────────────────┬─────────────────────────────┘
                            │ Heuristic Intent Score (0-100)
                            ▼
                     [EARLY DATE FILTER]
                      /            \
         >90 days (actual)        ≤90 days
               │                         │
               ▼                         ▼
      [DISCARD]              ┌─────────────────────────────┐
                            │ COMPOUND PAIN + RECENCY     │
                            │ Multiplier Application       │
                            └─────────────┬───────────────┘
                                          │
                                          ▼
                            [✅ HIGH-INTENT CRM LEAD ✅]

Key Technologies

• Crawlee: Scalable web scraping framework.
• Hybrid NLP Engine: Custom AFINN-based sentiment analysis + keyword-based intent detection.
• 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