The competitive edge, delivered. This Customer Intelligence Engine simultaneously analyzes the web, Reddit, and X/Twitter to generate a professional, actionable executive briefing. 🎯 Problem Statement Traditional market research for Customer Intelligence (CI) is manual, slow, and often relies on surface-level social media scraping or expensive external reports. Service companies, like HVAC providers, struggle to efficiently synthesize vast volumes of online feedback (Reddit discussions, real-time tweets, web articles) to accurately diagnose systemic service gaps (e.g., scheduling friction, poor automated systems). This inefficiency leads to delayed strategic responses and missed opportunities to invest in high-impact solutions like AI voice agents . ✨ Solution This workflow deploys a sophisticated Multisource Intelligence Pipeline that runs on a scheduled or ad-hoc basis. It uses parallel processing to ingest data from three distinct source types ( SERP API, Reddit, and X/Twitter ), employs a zero-cost Hybrid Categorization method to semantically identify operational bottlenecks, and uses the Anthropic LLM to synthesize the findings into a clear, executive-ready strategic brief. The data is logged for historical analysis while the brief is dispatched for immediate action. ⚙️ How It Works (Multi-Step Execution) 1. Ingestion and Parallel Processing ( The Data Fabric ) Trigger: The workflow is initiated either on an ad-hoc basis via an n8n Form Trigger or on a schedule (Time Trigger). Parallel Ingestion: The workflow immediately splits into three parallel branches to fetch data simultaneously: SERP API: Captures authoritative content and industry commentary ( Strategic Context ). Reddit (Looping Structure): Fetches posts from multiple subreddits via an Aggregate Node workaround to get authentic user experiences ( Qualitative Signal ). X/Twitter (HTTP Request): Bypasses standard rate limits to capture real-time social complaints ( Sentiment Signal ). 2. Analysis and Fusion ( The Intelligence Layer ) Cleanup and Labeling (Function Nodes): Each branch uses dedicated Function Nodes to filter noise (e.g., low-score posts) and normalize the data by adding a source tag (e.g., 'Reddit'). Merge: A Merge Node (Append Mode) fuses all three parallel streams into a single, unified dataset. Hybrid Categorization (Function Node): A single Function Node applies the Hybrid Categorization Logic. This cost-free step semantically assigns a pain_point category (e.g., 'Call Hold/Availability') and a sentiment_score to every item, transforming raw text into labeled metrics. 3. Dispatch and Reporting ( The Executive Output ) Aggregation and Split (Function Node): The final Function Node calculates the total counts, deduplicates the final results, and generates the comprehensive summaryString . Data Logging: The aggregated counts and metrics are appended to Google Sheets for historical logging. LLM Input Retrieval (Function Node): A final Function Node retrieves the summary data using the $items() helper (the serial route workaround). AI Briefing: The Message a model (Anthropic) Node receives the summaryString and uses a strict HTML System Prompt to synthesize the strategic brief, identifying the top pain points and suggesting AI features. Delivery: The Gmail Node sends the final, professional HTML brief to the executive team. 🛠️ Setup Steps Credentials Anthropic: Configure credentials for the Language Model (Claude) used in the Message a model node. SERP API, Reddit, and X/Twitter: Configure API keys/credentials for the data ingestion nodes. Google Services: Set up OAuth2 credentials for Google Sheets (for logging data) and Gmail (for email dispatch). Configuration Form Configuration: If using the Form Trigger, ensure the Target Keywords and Target Subreddits are mapped correctly to the ingestion nodes. Data Integrity: Due to the serial route, ensure the Function (Get LLM Summary) node is correctly retrieving the LLM_SUMMARY_HOLDER field from the preceding node's output memory. ✅ Benefits Proactive CI & Strategy: Shifts market research from manual, reactive browsing to proactive, scheduled data diagnostic. Cost Efficiency: Utilizes a zero-cost Hybrid Categorization method (Function Node) for intent analysis, avoiding expensive per-item LLM token costs. Actionable Output: Delivers a fully synthesized, HTML-formatted executive brief, ready for immediate presentation and strategic sales positioning. High Reliability: Employs parallel ingestion, API workarounds, and serial routing to ensure the complex workflow runs consistently and without failure.