Customer pain analysis & AI briefing with Anthropic, Reddit, X, and SerpAPI
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) 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*). 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 painpoint category (e.g., 'Call Hold/Availability') and a sentimentscore to every item, transforming raw text into labeled metrics. 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 LLMSUMMARYHOLDER 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.
By Bhuvanesh RGenerate custom icons with OpenAI GPT image & Google Drive auto-storage
Workflow Description: Turn a simple text idea into production-ready icons in seconds. With this workflow, you input a subject (e.g., “Copy”, “Banana”, “Slack Mute”), select a style (Flat, 3D, Cartoon, etc.), and off it goes. Here’s what happens: A form trigger collects your icon subject, style and optional background. The workflow uses an LLM to construct an optimised prompt. An image-generation model (OpenAI image API) renders a transparent-background, 400×400 px PNG icon. The icon is automatically uploaded to Google Drive, and both a download link and thumbnail are generated. A styled completion card displays the result and gives you a “One More Time” option. Perfect for designers, developers, no-code creators, UI builders and even home-automation geeks (yes, you can integrate it with Home Assistant or Stream Deck!). It saves you the manual icon-hunt grind and gives consistent visual output across style variants. 🔧 Setup Requirements: n8n instance (self-hosted or cloud) OpenAI API access (image generation enabled) Google Drive credentials (write access to a folder) (Optional) Modify to integrate Slack, Teams or other file-storage destinations ✅ Highlights & Benefits: Fully automated prompt creation → consistent icon quality Transparent background PNGs size-ready for UI use Saves icons to Drive + gives immediate link/thumbnail Minimal setup, high value for creative/automation workflows Easily extendable (add extra sizes, style presets, share via chat/bot) ⚠️ Notes & Best-Practices: Check your OpenAI image quota and costs - image generation may incur usage. Confirm Google Drive folder permissions to avoid upload failures. If you wish a different resolution or format (e.g., SVG), clone the image node and adjust parameters.
By ArkadiuszDetect and score refund risk with Webhook, OpenAI and Google Sheets
How it works This workflow automatically evaluates refund and chargeback risk for incoming e-commerce orders. Orders are received via a webhook, processed individually, and checked to avoid duplicate analysis. Each transaction is normalized and sent to OpenAI for structured risk scoring and classification. Results are logged for auditing, alerts are triggered for high-risk cases, and processed orders are marked to prevent reprocessing. Step-by-step Step 1 – Ingest incoming orders Webhook – Receives single or bulk order payloads from external systems. Split Out – Breaks array-based payloads into individual order records. Split In Batches – Iterates through each order in a controlled loop. Step 2 – Deduplication check IF (DEDUPE CHECK) – Verifies whether an order was already processed and skips duplicates. Step 3 – Normalize transaction data Code (Normalize Data) – Validates required fields and standardizes order, customer, and behavioral attributes. Step 4 – AI risk assessment OpenAI (Message a model) – Sends normalized transaction data to the AI model and requests a strict JSON risk evaluation. Step 5 – Parse AI output Code (Parse AI Output) – Cleans the AI response and extracts risk score, risk level, key drivers, and recommendations. Step 6 – Log results Google Sheets (Append) – Stores timestamps, order details, and AI risk outcomes for reporting and audits. Step 7 – Risk decision and alerts IF (High Risk) – Filters only transactions classified as HIGH risk. Discord – Sends real-time alerts to operations or finance teams. Gmail – Emails finance stakeholders with full risk context. Step 8 – Mark order as processed Google Sheets (Update) – Updates the source row to prevent duplicate processing. Why use this? Automatically detects high refund or chargeback risk before losses occur. Eliminates manual review with consistent, AI-driven risk scoring. Sends instant alerts so teams can act quickly on high-risk orders. Maintains a clear audit trail for compliance and reporting. Scales easily to handle single or bulk order evaluations.
By Avkash Kakdiya