Workflow Templates

Quick overview This workflow triggers on a Gmail message from a specific sender, extracts bill of lading data from an email attachment using Google Gemini, validates the extracted values with Python, posts the structured JSON to a webhook URL, and replies to the sender with an HTML acceptance email. How it works Triggers when a new Gmail email arrives from the configured sender and downloads the attached document. Uses Google Gemini to analyze the attachment and rewrite the bill of lading content into a structured Markdown format. Uses a second Google Gemini agent with a structured output schema to convert the Markdown into validated JSON fields for shipper, consignee, shipment details, and line items. Runs a Python validation that compares customer order package/weight values against the carrier line items and appends a validation result to the JSON. Sends the resulting JSON payload to an external HTTP webhook endpoint. Uses Google Gemini to draft an HTML acceptance reply email based on the validated data and sends it back to the original sender via Gmail. Setup Connect your Gmail OAuth2 credentials and set the sender filter (and any other Gmail Trigger options) to match the emails you want to process. Add Google Gemini (PaLM) API credentials and confirm the selected Gemini models are available in your account. Ensure the incoming emails include a compatible bill of lading attachment and update the binary attachment property name if it differs from attachment_0. Replace the HTTP Request URL with your own endpoint that should receive the structured JSON payload. Review the structured JSON schema and the Python validation logic to match your required fields and matching rules before enabling the workflow. Customization Any LLM provider can do the task, change Gemini with any AI you can use Additional info This is an example of prompt chaining. It is an AI agent workflow that shows the benefit of chaining several LLMs together.

AI Agent Lead Funnel for AI Agencies An End-to-End Automation That Turns Demos Into Booked Calls This n8n workflow is a full inbound → outbound hybrid funnel designed for AI agencies. It captures warm leads through instant AI value, then automatically follows up with personalized, context-aware outreach and reminders until the lead either replies or books a call. No cold scraping. No manual follow-ups. Just leverage + timing. 🚀 How the Workflow Works 📋 PART 1 — Lead Capture & Instant Value 1 — Share High-Impact AI Image Edits You post before/after examples using the NanoBanna / Gemini image-editing model on social platforms. Each post includes a link to a lightweight form. The visual results do the selling for you. 2 — Lead Submits Image & Details The form collects: Image upload Edit instructions Name Email Company name This filters for high-intent prospects only. 3 — AI Edits the Image Instantly Once submitted, the workflow: Sends the image + instructions to the AI image editor Preserves lighting and camera angle unless specified Generates a polished result in seconds 4 — Result Delivered via Email The edited image is emailed directly to the user with: A friendly confirmation message Soft positioning for future work This establishes trust before any sales motion happens. 5 — Lead Is Logged Automatically All lead data is saved to Google Sheets: Name Company Email Timestamp This becomes your live CRM of warm inbound leads. 🤖 PART 2 — AI-Driven Personalized Outreach 6 — AI Analyzes the Lead An AI sales agent: Looks at the company name + context Reviews a library of proven automation ideas Either selects the best fit or creates a simple custom one 7 — AI Writes a Personalized Outreach Email The agent generates a short email that: Mentions a specific automation already built States you can help implement it quickly Invites them to book a call via your calendar No marketing fluff. No generic pitches. Every email feels hand-written. 8 — Outreach Email Is Sent Automatically The email is sent from your inbox (Outlook, Gmail, SMTP, etc.) and includes: Their name Their company A clear calendar booking link 📬 PART 3 — Smart Follow-Up System 9 — Wait 48 Hours The workflow pauses to give the lead time to respond naturally. 10 — Check for a Reply After 48 hours: If the lead replied → they are tagged as Interested If no reply → continue to follow-up (Current reply detection is placeholder logic and can be swapped for a live inbox listener.) 11 — AI Writes a Polite Follow-Up If there’s no response, an AI agent writes: A short, non-pushy follow-up Referencing the original automation idea Under 60 words 12 — Follow-Up Email Is Sent The follow-up goes out automatically and keeps the conversation alive without manual effort. 📈 Why This Workflow Converts So Well Instant Value First Leads experience AI results before being pitched anything. Context-Aware Outreach Every email is personalized based on the lead, not a template. Built-In Persistence The system follows up automatically — no leads fall through the cracks. Fully Automated Once live, this workflow handles: Lead capture AI delivery Outreach Follow-ups CRM updates You just keep posting content. 🔧 Setup Requirements To deploy this workflow, connect: Google Gemini API** (image editing + agents) Email provider** Outlook Gmail SMTP Google Sheets** Columns: Name, Company, Email, Time, Status Calendar booking link** Example: https://cal.com/your-link All credentials are modular and easily swappable. 🎯 Summary This n8n automation turns attention into action by: Delivering immediate AI value Following up with relevant, personalized ideas Nudging leads toward a booked call — automatically It’s not just a lead funnel. It’s an AI sales assistant that runs 24/7.

Quick overview This workflow runs daily, queries Snowflake for items at or below their reorder level, and groups them by supplier. For each supplier, it uses Anthropic Claude to draft a reorder email, optionally appends details to a Google Docs log, and sends the email via Microsoft Outlook. How it works A Schedule Trigger fires every day at 07:00 (Europe/Helsinki timezone) and passes through a Workflow Config node that holds your optional Google Docs log ID. A Snowflake query fetches all inventory rows where current_stock Error Workflow, point to a dedicated error-handler sub-workflow that wires into the Send Error Alert node (it is intentionally unwired in this workflow). Requirements Snowflake account with an inventory table containing current_stock, reorder_level, supplier_name, and supplier_email columns Anthropic API credentials (claude-sonnet-4-6) Microsoft 365 account with Outlook access Google Docs OAuth2 credentials (only if audit logging is enabled) Customization Adjust the Snowflake query to match your schema, add filters by warehouse or category, or join additional tables for richer item metadata. Modify the Claude system prompt in Build Email Draft to change tone, language, or email structure — for example to generate Finnish-language emails or include purchase order references. Change the cron expression in When 7am Daily to run at a different time or frequency. Extend the log entry format in Log to Google Docs to include item names, SKUs, or a hyperlink back to the workflow execution. Replace the Google Docs logging step with a Slack notification, a row insert into PostgreSQL, or any other audit destination by swapping the node after Check Configured Doc ID.

Who is this for? This workflow is for Product Managers, Indie Hackers, and Customer Success teams who collect feature requests but struggle to notify specific users when those features actually ship. It helps you turn old feedback into customer loyalty and potential upsells. What it does This workflow creates a "Semantic Memory" of user requests. Instead of relying on exact keyword tags, it uses Vector Embeddings to understand the meaning of a request. For example, if a user asks for "Night theme," and months later you release "Dark Mode," this workflow understands they are the same thing, finds that user, and drafts a personal email to them. How it works Listen: Receives new requests via Tally Forms, vectorizes the text using Nomic Embed Text (via Ollama or OpenAI), and stores them in Supabase. Watch: Monitors your Changelog (RSS) or waits for a manual trigger when you ship a new feature. Match: Performs a Vector Similarity Search in Supabase to find users who requested semantically similar features in the past. Notify: An AI Agent drafts a hyper-personalized email connecting the user's specific past request to the new feature, saving it as a Gmail Draft (for safety). Requirements Supabase Project:** You need a project with the vector extension enabled. AI Model:* This template is pre-configured for *Ollama (Local)** to keep it free, but works perfectly with OpenAI. Tally Forms & Gmail:** For input and output. Setup steps Database Setup (Crucial): Copy the SQL script provided in the workflow's Red Sticky Note and run it in your Supabase SQL Editor. This creates the necessary tables and the vector search function. Credentials: Add your credentials for Tally, Supabase, and Gmail. URL Config: Update the HTTP Request node with your specific Supabase Project URL. SQL Script Open your Supabase SQL Editor and paste this script to set up the tables and search function: -- 1. Enable Vector Extension create extension if not exists vector; -- 2. Create Request Table (Smart Columns) create table feature_requests ( id bigint generated by default as identity primary key, content text, metadata jsonb, embedding vector(768), -- 768 for Nomic, 1536 for OpenAI created_at timestamp with time zone default timezone('utc'::text, now()), user_email text generated always as (metadata->>'user_email') stored, user_name text generated always as (metadata->>'user_name') stored ); -- 3. Create Search Function create or replace function match_feature_requests ( query_embedding vector(768), match_threshold float, match_count int ) returns table ( id bigint, user_email text, user_name text, content text, similarity float ) language plpgsql as $$ begin return query select feature_requests.id, feature_requests.user_email, feature_requests.user_name, feature_requests.content, 1 - (feature_requests.embedding <=> query_embedding) as similarity from feature_requests where 1 - (feature_requests.embedding <=> query_embedding) > match_threshold order by feature_requests.embedding <=> query_embedding limit match_count; end; $$; ⚠️ Dimension Warning: This SQL is set up for 768 dimensions (compatible with the local nomic-embed-text model included in the template). If you decide to switch the Embeddings node to use OpenAI's text-embedding-3-small, you must change all instances of 768 to 1536 in the SQL script above before running it. How to customize Change Input:** Swap the Tally node for Typeform, Intercom, or Google Sheets. Change AI:** The template includes notes on how to swap the local Ollama nodes for OpenAI nodes if you prefer cloud hosting. Change Output:** Swap Gmail for Slack, SendGrid, or HubSpot to notify your sales team instead of the user directly.

{ "name": "IoT Sensor Data Aggregation with AI-Powered Anomaly Detection", "nodes": [ { "parameters": { "content": "## How it works\nThis workflow monitors IoT sensors in real-time. It ingests data via MQTT or a schedule, normalizes the format, and removes duplicates using data fingerprinting. An AI Agent then analyzes readings against defined thresholds to detect anomalies. Finally, it routes alerts to Slack or Email based on severity and logs everything to Google Sheets.\n\n## Setup steps\n1. Configure the MQTT Trigger with your broker details.\n2. Set your specific limits in the Define Sensor Thresholds node.\n3. Connect your OpenAI credential to the Chat Model node.\n4. Authenticate the Gmail, Slack, and Google Sheets nodes.\n5. Create a Google Sheet with headers: timestamp, sensorId, location, readings, analysis.", "height": 484, "width": 360 }, "id": "298da7ff-0e47-4b6c-85f5-2ce77275cdf3", "name": "Main Overview", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -2352, -480 ] }, { "parameters": { "content": "## 1. Data Ingestion\nCaptures sensor data via MQTT for real-time streams or runs on a schedule for batch processing. Both streams are merged for unified handling.", "height": 488, "width": 412, "color": 7 }, "id": "4794b396-cd71-429c-bcef-61780a55d707", "name": "Section: Ingestion", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -1822, -48 ] }, { "parameters": { "content": "## 2. Normalization & Deduplication\nSets monitoring thresholds, standardizes the JSON structure, creates a content hash, and filters out duplicate readings to prevent redundant API calls.", "height": 316, "width": 884, "color": 7 }, "id": "339e7cb7-491e-44c9-b561-983e147237d8", "name": "Section: Processing", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -1376, 32 ] }, { "parameters": { "content": "## 3. AI Anomaly Detection\nAn AI Agent evaluates sensor data against thresholds to identify anomalies, assigning severity levels and providing actionable recommendations.", "height": 528, "width": 460, "color": 7 }, "id": "ebcb7ca3-f70c-4a90-8a2a-f489e7be4c73", "name": "Section: AI Analysis", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -422, 24 ] }, { "parameters": { "content": "## 4. Routing & Archiving\nRoutes alerts based on severity (Critical = Email+Slack, Warning = Slack) and archives all data points to Google Sheets for historical analysis.", "height": 756, "width": 900, "color": 7 }, "id": "7f2b32a5-d3b2-4fea-844f-4b39b8e8a239", "name": "Section: Alerting", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ 94, -196 ] }, { "parameters": { "topics": "sensors/+/data", "options": {} }, "id": "bc86720b-9de9-4693-b090-343d3ebad3a3", "name": "MQTT Sensor Trigger", "type": "n8n-nodes-base.mqttTrigger", "typeVersion": 1, "position": [ -1760, 88 ] }, { "parameters": { "rule": { "interval": [ { "field": "minutes", "minutesInterval": 15 } ] } }, "id": "1c38f2d0-aa00-447e-bdae-bffd08c38461", "name": "Batch Process Schedule", "type": "n8n-nodes-base.scheduleTrigger", "typeVersion": 1.2, "position": [ -1760, 280 ] }, { "parameters": { "mode": "chooseBranch" }, "id": "f9b41822-ee61-448b-b324-38483036e0e1", "name": "Merge Triggers", "type": "n8n-nodes-base.merge", "typeVersion": 3, "position": [ -1536, 184 ] }, { "parameters": { "mode": "raw", "jsonOutput": "{\n \"thresholds\": {\n \"temperature\": {\"min\": -10, \"max\": 50, \"unit\": \"C\"},\n \"humidity\": {\"min\": 20, \"max\": 90, \"unit\": \"%\"},\n \"pressure\": {\"min\": 950, \"max\": 1050, \"unit\": \"hPa\"},\n \"co2\": {\"min\": 400, \"max\": 2000, \"unit\": \"ppm\"}\n },\n \"alertConfig\": {\n \"criticalChannel\": \"#iot-critical\",\n \"warningChannel\": \"#iot-alerts\",\n \"emailRecipients\": \"ops@example.com\"\n }\n}", "options": {} }, "id": "308705a8-edc7-4435-9250-487aa528e033", "name": "Define Sensor Thresholds", "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ -1312, 184 ] }, { "parameters": { "jsCode": "const items = $input.all();\nconst thresholds = $('Define Sensor Thresholds').first().json.thresholds;\nconst results = [];\n\nfor (const item of items) {\n let sensorData;\n try {\n sensorData = typeof item.json.message === 'string' \n ? JSON.parse(item.json.message) \n : item.json;\n } catch (e) {\n sensorData = item.json;\n }\n \n const now = new Date();\n const reading = {\n sensorId: sensorData.sensorId || sensorData.topic?.split('/')[1] || 'unknown',\n location: sensorData.location || 'Main Facility',\n timestamp: now.toISOString(),\n readings: {\n temperature: sensorData.temperature ?? null,\n humidity: sensorData.humidity ?? null,\n pressure: sensorData.pressure ?? null,\n co2: sensorData.co2 ?? null\n },\n metadata: {\n receivedAt: now.toISOString(),\n source: item.json.topic || 'batch',\n thresholds: thresholds\n }\n };\n \n results.push({ json: reading });\n}\n\nreturn results;" }, "id": "a2008189-5ace-418b-b0db-d51d63dcf2d8", "name": "Parse Sensor Payload", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -1088, 184 ] }, { "parameters": { "type": "SHA256", "value": "={{ $json.sensorId + '-' + $json.timestamp + '-' + JSON.stringify($json.readings) }}", "dataPropertyName": "dataHash" }, "id": "bf8db555-a10e-4468-a44a-cdc4c97e5b80", "name": "Generate Data Fingerprint", "type": "n8n-nodes-base.crypto", "typeVersion": 1, "position": [ -864, 184 ] }, { "parameters": { "compare": "selectedFields", "fieldsToCompare": "dataHash", "options": {} }, "id": "a45405e2-d211-449d-84d7-4538eaf56fcd", "name": "Remove Duplicate Readings", "type": "n8n-nodes-base.removeDuplicates", "typeVersion": 1, "position": [ -640, 184 ] }, { "parameters": { "text": "=Analyze this IoT sensor reading and determine if there are any anomalies:\n\nSensor ID: {{ $json.sensorId }}\nLocation: {{ $json.location }}\nTimestamp: {{ $json.timestamp }}\n\nReadings:\n- Temperature: {{ $json.readings.temperature }}°C (Normal: {{ $json.metadata.thresholds.temperature.min }} to {{ $json.metadata.thresholds.temperature.max }})\n- Humidity: {{ $json.readings.humidity }}% (Normal: {{ $json.metadata.thresholds.humidity.min }} to {{ $json.metadata.thresholds.humidity.max }})\n- CO2: {{ $json.readings.co2 }} ppm (Normal: {{ $json.metadata.thresholds.co2.min }} to {{ $json.metadata.thresholds.co2.max }})\n\nProvide your analysis in this exact JSON format:\n{\n \"hasAnomaly\": true/false,\n \"severity\": \"critical\"/\"warning\"/\"normal\",\n \"anomalies\": [\"list of detected issues\"],\n \"reasoning\": \"explanation of your analysis\",\n \"recommendation\": \"suggested action\"\n}", "options": { "systemMessage": "You are an IoT monitoring expert. Analyze sensor data and detect anomalies based on the provided thresholds. Be precise and provide actionable recommendations. Always respond in valid JSON format." } }, "id": "b60194ba-7b99-44e0-b0d7-9f1632dce4d4", "name": "AI Anomaly Detector", "type": "@n8n/n8n-nodes-langchain.agent", "typeVersion": 1.7, "position": [ -416, 184 ] }, { "parameters": { "jsCode": "const item = $input.first();\nconst originalData = $('Remove Duplicate Readings').first().json;\n\nlet aiAnalysis;\ntry {\n const responseText = item.json.output || item.json.text || '';\n const jsonMatch = responseText.match(/\\{[\\s\\S]*\\}/);\n aiAnalysis = jsonMatch ? JSON.parse(jsonMatch[0]) : {\n hasAnomaly: false,\n severity: 'normal',\n anomalies: [],\n reasoning: 'Unable to parse AI response',\n recommendation: 'Manual review required'\n };\n} catch (e) {\n aiAnalysis = {\n hasAnomaly: false,\n severity: 'normal',\n anomalies: [],\n reasoning: 'Parse error: ' + e.message,\n recommendation: 'Manual review required'\n };\n}\n\nreturn [{\n json: {\n ...originalData,\n analysis: aiAnalysis,\n alertLevel: aiAnalysis.severity,\n requiresAlert: aiAnalysis.hasAnomaly && aiAnalysis.severity !== 'normal'\n }\n}];" }, "id": "a145a8c7-538c-411a-95c6-9485acdcb969", "name": "Parse AI Analysis", "type": "n8n-nodes-base.code", "typeVersion": 2, "position": [ -64, 184 ] }, { "parameters": { "rules": { "values": [ { "conditions": { "options": { "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "id": "critical", "operator": { "type": "string", "operation": "equals" }, "leftValue": "={{ $json.alertLevel }}", "rightValue": "critical" } ] }, "renameOutput": true, "outputKey": "Critical" }, { "conditions": { "options": { "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "id": "warning", "operator": { "type": "string", "operation": "equals" }, "leftValue": "={{ $json.alertLevel }}", "rightValue": "warning" } ] }, "renameOutput": true, "outputKey": "Warning" } ] }, "options": { "fallbackOutput": "extra" } }, "id": "1ab9785d-9f7f-4840-b1e9-0afc62b00b12", "name": "Route by Severity", "type": "n8n-nodes-base.switch", "typeVersion": 3.2, "position": [ 160, 168 ] }, { "parameters": { "sendTo": "={{ $('Define Sensor Thresholds').first().json.alertConfig.emailRecipients }}", "subject": "=CRITICAL IoT Alert: {{ $json.sensorId }} - {{ $json.analysis.anomalies[0] || 'Anomaly Detected' }}", "message": "=CRITICAL IoT SENSOR ALERT\n\nSensor: {{ $json.sensorId }}\nLocation: {{ $json.location }}\nTime: {{ $json.timestamp }}\n\nReadings:\n- Temperature: {{ $json.readings.temperature }}°C\n- Humidity: {{ $json.readings.humidity }}%\n- CO2: {{ $json.readings.co2 }} ppm\n\nAI Analysis:\n{{ $json.analysis.reasoning }}\n\nDetected Issues:\n{{ $json.analysis.anomalies.join('\\n- ') }}\n\nRecommendation:\n{{ $json.analysis.recommendation }}", "options": {} }, "id": "28201a6c-10b5-4387-be89-10a57c634622", "name": "Send Critical Email", "type": "n8n-nodes-base.gmail", "typeVersion": 2.1, "position": [ 384, -80 ], "webhookId": "35b9f8fa-4a50-456e-b552-9fd20a25ccc5" }, { "parameters": { "select": "channel", "channelId": { "__rl": true, "mode": "name", "value": "#iot-critical" }, "text": "=🚨 CRITICAL IoT ALERT\n\nSensor: {{ $json.sensorId }}\nLocation: {{ $json.location }}\n\nReadings:\n• Temperature: {{ $json.readings.temperature }}°C\n• Humidity: {{ $json.readings.humidity }}%\n• CO2: {{ $json.readings.co2 }} ppm\n\nAI Analysis: {{ $json.analysis.reasoning }}\nRecommendation: {{ $json.analysis.recommendation }}", "otherOptions": {} }, "id": "c5a297be-ccef-40ba-9178-65805262efba", "name": "Slack Critical Alert", "type": "n8n-nodes-base.slack", "typeVersion": 2.2, "position": [ 384, 112 ], "webhookId": "19113595-0208-4b37-b68c-c9788c19f618" }, { "parameters": { "select": "channel", "channelId": { "__rl": true, "mode": "name", "value": "#iot-alerts" }, "text": "=⚠️ IoT Warning\n\nSensor: {{ $json.sensorId }} | Location: {{ $json.location }}\nIssue: {{ $json.analysis.anomalies[0] || 'Threshold approaching' }}\nRecommendation: {{ $json.analysis.recommendation }}", "otherOptions": {} }, "id": "5c3d7acf-0211-44dd-9f4b-a43d3796abb1", "name": "Slack Warning Alert", "type": "n8n-nodes-base.slack", "typeVersion": 2.2, "position": [ 384, 400 ], "webhookId": "37abfb19-f82f-4449-bd69-a65635b99606" }, { "parameters": {}, "id": "6bcbb42f-ec14-4f00-a091-babcc2d2d5c4", "name": "Merge Alert Outputs", "type": "n8n-nodes-base.merge", "typeVersion": 3, "position": [ 608, 184 ] }, { "parameters": { "operation": "append", "documentId": { "__rl": true, "mode": "list", "value": "" }, "sheetName": { "__rl": true, "mode": "list", "value": "" } }, "id": "6243aa23-408d-4928-a512-811eeb3b5f9e", "name": "Archive to Google Sheets", "type": "n8n-nodes-base.googleSheets", "typeVersion": 4.5, "position": [ 832, 184 ] }, { "parameters": { "model": "gpt-4o-mini", "options": { "temperature": 0.3 } }, "id": "61081e8a-ebc9-465f-8beb-88af225e59f2", "name": "OpenAI Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "typeVersion": 1.2, "position": [ -344, 408 ] } ], "pinData": {}, "connections": { "MQTT Sensor Trigger": { "main": [ [ { "node": "Merge Triggers", "type": "main", "index": 0 } ] ] }, "Batch Process Schedule": { "main": [ [ { "node": "Merge Triggers", "type": "main", "index": 1 } ] ] }, "Merge Triggers": { "main": [ [ { "node": "Define Sensor Thresholds", "type": "main", "index": 0 } ] ] }, "Define Sensor Thresholds": { "main": [ [ { "node": "Parse Sensor Payload", "type": "main", "index": 0 } ] ] }, "Parse Sensor Payload": { "main": [ [ { "node": "Generate Data Fingerprint", "type": "main", "index": 0 } ] ] }, "Generate Data Fingerprint": { "main": [ [ { "node": "Remove Duplicate Readings", "type": "main", "index": 0 } ] ] }, "Remove Duplicate Readings": { "main": [ [ { "node": "AI Anomaly Detector", "type": "main", "index": 0 } ] ] }, "AI Anomaly Detector": { "main": [ [ { "node": "Parse AI Analysis", "type": "main", "index": 0 } ] ] }, "Parse AI Analysis": { "main": [ [ { "node": "Route by Severity", "type": "main", "index": 0 } ] ] }, "Route by Severity": { "main": [ [ { "node": "Send Critical Email", "type": "main", "index": 0 }, { "node": "Slack Critical Alert", "type": "main", "index": 0 } ], [ { "node": "Slack Warning Alert", "type": "main", "index": 0 } ], [ { "node": "Merge Alert Outputs", "type": "main", "index": 0 } ] ] }, "Send Critical Email": { "main": [ [ { "node": "Merge Alert Outputs", "type": "main", "index": 0 } ] ] }, "Slack Critical Alert": { "main": [ [ { "node": "Merge Alert Outputs", "type": "main", "index": 0 } ] ] }, "Slack Warning Alert": { "main": [ [ { "node": "Merge Alert Outputs", "type": "main", "index": 0 } ] ] }, "Merge Alert Outputs": { "main": [ [ { "node": "Archive to Google Sheets", "type": "main", "index": 0 } ] ] }, "OpenAI Chat Model": { "ai_languageModel": [ [ { "node": "AI Anomaly Detector", "type": "ai_languageModel", "index": 0 } ] ] } }, "active": false, "settings": { "executionOrder": "v1" }, "versionId": "", "meta": { "instanceId": "15d6057a37b8367f33882dd60593ee5f6cc0c59310ff1dc66b626d726083b48d" }, "tags": [] }

Who’s it for This workflow is for hotel managers, travel agencies, and hospitality teams who receive booking requests via email. It eliminates the need for manual data entry by automatically parsing emails and attachments, assigning booking cases to the right teams, and tracking performance metrics. What it does This workflow goes beyond simple automation by including enterprise-grade logic and security: 🛡️ Gatekeeper:** Watches your Gmail and filters irrelevant emails before spending money on AI tokens. 🧠 AI Brain:** Uses OpenAI (GPT-5-mini) to extract structured data from unstructured email bodies and PDF attachments. ⚖️ Business Logic:** Automatically routes tasks to different teams based on urgency, room count, and VIP status. 🔒 Security:** Catches PII (like credit card numbers) and scrubs them before they hit your database. 🚨 Safety Net:** If anything breaks, a dedicated error handling path logs the issue immediately so no booking is lost. 📈 ROI Tracking:** Calculates the time saved per booking to prove the value of automation. How to set up Create your Google Sheet: Create a new sheet and rename the tabs to: Cases, Team Assignments, Error Logs, Success Metrics. Add Credentials: Go to n8n Settings → Credentials and add your Gmail (OAuth2), Google Sheets, and OpenAI API keys. Configure User Settings: Open the "Configuration: User Settings" node at the start of the workflow. Paste your specific Google Sheet ID and Admin Email there. Adjust Business Rules: Open the "Apply Business Rules" node (Code node) to adjust the logic for team assignment (e.g., defining what counts as a "VIP" booking). Customize Templates: Modify the email templates in the Gmail nodes to match your hotel's branding. Test: Send a sample booking email to yourself to verify the filters and data extraction. Setup requirements Gmail account (OAuth2 connected) Google Sheets (with the 4 tabs listed below) OpenAI API key (GPT-5-mini recommended) n8n Cloud or self-hosted instance How to customize Filter Booking Emails:** Update the trigger node keywords to match your specific email subjects (e.g., "Reservation", "Booking Request"). Apply Business Rules:** Edit the Javascript in the Code node to fit your company’s internal logic (e.g., changing priority thresholds). New Metrics:** Add new columns in the Google Sheet (e.g., “Revenue Metrics”) and map them in the "Update Sheet" node. AI Model:** Switch to GPT-5 if you need higher reasoning capabilities for complex PDF layouts. Google Sheets Structure Description This workflow uses a Google Sheets document with four main tabs to track and manage hotel booking requests. 1. Cases This is the main data log for all incoming booking requests. case_id:** Unique identifier generated by the workflow. processed_date:** Timestamp when the workflow processed the booking. travel_agency / contact_details:** Extracted from the email. number_of_rooms / check_in_date:** Booking details parsed by the AI. special_requests:** Optional notes (e.g., airport transfer). assigned_team / priority:** Automatically set based on business rules. days_until_checkin:** Dynamic field showing urgency. 2. Team Assignments Stores internal routing and assignment details. timestamp:** When the case was routed. case_id:** Link to the corresponding record in the Cases tab. assigned_team / team_email:** Which department handles this request. priority:** Auto-set based on room count or urgency. 3. Error Log A critical audit trail that captures details about any failed processing steps. error_type:** Categorization of the failure (e.g., MISSING_REQUIRED_FIELDS). error_message:** Detailed technical explanation for debugging. original_sender / snippet:** Context to help you manually process the request if needed. 4. Success Metrics Tracks the results of your automation to prove its value. processing_time_seconds:** The time savings achieved by the automation (run time vs. human time). record_updated:** Confirmation that the database was updated. 🙋 Support If you encounter any issues during setup or have questions about customization, please reach out to our dedicated support email: foivosautomationhelp@gmail.com

Overview Manual financial reconciliation is tedious and prone to error. This workflow functions as an AI Financial Controller, automatically monitoring your inbox for invoices, receipts, and bills, extracting the data using OCR, and syncing it to Google Sheets for approval. Unlike simple scrapers, this workflow uses a "Guardrail" AI agent to filter out non-financial emails (like newsletters) before they are processed, ensuring only actual transactions are recorded. Who is it for? Finance Teams:** To automate the collection of vendor invoices. Freelancers:** To track expenses and receipts for tax season. Operations Managers:** To monitor budget spend and categorize costs automatically. How it works Ingest: The workflow watches a specific Gmail label (e.g., "INBOX") for new emails. Guardrail: A Gemini-powered AI agent analyzes the email text to determine if it is a valid financial transaction. If not, the workflow stops. Extraction (OCR): If an attachment exists: An AI Agent (GPT-4o) extracts data from the PDF. If no attachment: An AI Agent extracts data directly from the email body. Validation: Code nodes check for missing fields or invalid amounts. Business Logic: The system automatically assigns General Ledger (GL) categories (e.g., "Uber" -> "Travel") and sets approval statuses based on amount thresholds. Sync: Validated data is logged to Google Sheets, and a confirmation email is sent. Errors are logged to a separate error sheet. How to set up Google Sheets: Copy this Google Sheet template to your drive. It contains the necessary tabs (Invoices, Error Logs, Success Metrics). Configure Workflow: Open the node named "Configuration: User Settings". Paste your Google Sheet ID (found in the URL of your new sheet). Enter the Admin Email address where you want to receive error notifications. Credentials: Connect your Gmail account. Connect your Google Sheets account. Connect your OpenAI (for OCR) and Google Gemini/PaLM (for Guardrails) accounts. Requirements n8n version 1.0 or higher. Gmail account. OpenAI API Key. Google Gemini (PaLM) API Key.

Who’s it for Marketing, growth, and analytics teams who want a decision-ready GA4 summary—automatically calculated, clearly color-coded, and emailed as a polished HTML report. How it works / What it does Get Client (Form Trigger)* collects *GA4 Property ID (“Account ID”), **Key Event, date ranges (current & previous), Client Name, and recipient email. Overall Metrics This Period / Previous Period (GA4 Data API)** pull sessions, users, engagement, bounce rate, and more for each range. Form Submits This Period / Previous Period (GA4 Data API)** fetch key-event counts for conversion comparisons. Code** normalizes form dates for API requests. AI Agent* builds a *valid HTML email**: Calculates % deltas, applies green for positive (#10B981) and red for negative (#EF4444) changes. Writes summary and recommendations. Produces the final HTML only. Send a message (Gmail)** sends the formatted HTML report to the specified email address with a contextual subject. How to set up 1) Add credentials: Google Analytics OAuth2, OpenAI (Chat), Gmail OAuth2. 2) Ensure the form fields match your GA4 property and event names; “Account ID” = GA4 Property ID. Property ID - https://take.ms/vO2MG Key event - https://take.ms/hxwQi 3) Publish the form URL and run a test submission. Requirements GA4 property access (Viewer/Analyst) • OpenAI API key • Gmail account with send permission. Resources Google OAuth2 (GA4) – https://docs.n8n.io/integrations/builtin/credentials/google/oauth-generic/ OpenAI credentials – https://docs.n8n.io/integrations/builtin/credentials/openai/ Gmail OAuth2 – https://docs.n8n.io/integrations/builtin/credentials/google/ GA4 Data API overview – https://developers.google.com/analytics/devguides/reporting/data/v1

🚀 Daily IndieHackers Reddit Trend Analysis to Slack > Transform Reddit chaos into actionable startup intelligence > Get AI-powered insights from r/indiehackers delivered to your Slack every morning 🎯 Who's It For This template is designed for startup founders, growth teams, and product managers who need to: Stay ahead of indie hacker trends without manual Reddit browsing Understand what's working in the entrepreneurial community Get actionable insights for product and marketing decisions Keep their team informed about emerging opportunities Perfect for teams building products for entrepreneurs or anyone wanting to leverage community intelligence for competitive advantage. ✨ What It Does Transform your morning routine with automated intelligence gathering that delivers structured, AI-powered summaries of the hottest r/indiehackers discussions directly to your Slack channel. 🧠 Smart Analysis Features | Feature | Description | |---------|-------------| | 🔥 Hotness Scoring | Calculates engagement scores using time-decay algorithms | | 📊 Topic Extraction | Identifies key themes and trending subjects | | 💰 Traction Signals | Spots revenue, metrics, and growth indicators | | 🎯 Theme Clustering | Groups posts into actionable categories | | ⚡ Action Items | Generates specific recommendations for your team | 📱 Slack Integration Receive beautifully formatted messages with: Executive summaries and key takeaways Top 3 hottest posts with engagement metrics Interactive buttons for deeper exploration Team discussion prompts ⚙️ How It Works graph LR A[🕐 Daily 8AM Trigger] --> B[📱 Fetch Reddit Posts] B --> C[🔄 Process Data] C --> D[🤖 Gemini AI Analysis] D --> E[✨ Groq Slack Formatting] E --> F[💬 Deliver to Slack] 🔄 The Complete Process Step 1: Automated Trigger Every morning at 8 AM, the workflow springs into action Step 2: Reddit Data Collection Fetches the latest 5 posts from r/indiehackers with full metadata Step 3: Data Processing Structures raw Reddit data for optimal AI analysis Step 4: AI-Powered Analysis Gemini AI performs deep analysis calculating hotness scores, extracting topics, and identifying patterns Step 5: Slack Formatting Groq AI Agent transforms insights into beautiful Slack Block Kit messages Step 6: Team Delivery Your designated Slack channel receives the formatted analysis 🛠️ Requirements You'll need API access for: Reddit (OAuth2), Google Gemini, Groq, and Slack (OAuth2). All have free tiers available. 🚀 Setup Guide 1️⃣ Configure Your Credentials Add these credentials in n8n: Reddit OAuth2, Google Gemini, Groq, and Slack OAuth2. The workflow will guide you through each setup. 2️⃣ Customize the Schedule Default: Daily at 8:00 AM To modify: Edit the "Daily Schedule" cron trigger node // Example: Run at 9:30 AM { "triggerTimes": { "item": [{ "hour": 9, "minute": 30 }] } } 3️⃣ Set Your Slack Destination Open the "Send to Slack" node Select your target channel Configure notification preferences 4️⃣ Adjust Analysis Parameters Post Limit: Change from default 5 posts // In "Get many posts" Reddit node "limit": 10 // Recommended: 3-10 posts Context Customization: { "channel_type": "team", "audience": "Growth, Product, and Founders", "cta_link": "https://your-dashboard.com", "timeframe_label": "This Week" } 🎨 Customization Options 🔍 Analysis Focus Areas Transform the workflow for different insights: SaaS-Focused Analysis Add to Gemini prompt: "Focus on SaaS and B2B insights, prioritizing recurring revenue and product-market fit signals" Geographic Targeting Add: "Prioritize posts relevant to [your region/market]" Stage-Specific Insights Add: "Focus on [early-stage/growth-stage] startup challenges" 📈 Hotness Algorithm Tweaking Default Formula: (ups + 2*num_comments) * freshness_decay Emphasize Comments: (ups + 3*num_comments) * freshness_decay Include Upvote Ratio: (ups * upvote_ratio + 2*num_comments) * freshness_decay 🌐 Multi-Subreddit Analysis Expand beyond r/indiehackers: Additional Communities: r/startups r/entrepreneur r/SideProject r/buildinpublic r/nocode 💾 Data Storage Extensions Enhance with historical tracking: | Node Type | Purpose | Benefit | |-----------|---------|---------| | Google Sheets | Trend storage | Historical analysis | | Airtable | Advanced data management | Rich analytics | | Webhook | External analytics | Custom dashboards | 📊 Expected Output 📱 Daily Slack Message Structure 🚀 IndieHackers Trends — This Week 📋 TL;DR: [One-sentence key insight] 🔥 Hot Posts (Top 3) [Post Title] (Hotness: 8.7) Topics: SaaS launch, pricing strategy 💬 23 comments | 👍 156 ups | 📅 Posted 4 hours ago [Open Reddit Button] 🧭 Themes Summary Go-to-market tactics — 3 posts, hotness: 24.1 Product launches — 2 posts, hotness: 18.3 ✅ What to Do Now Test pricing page variations based on community feedback Consider cold email strategies mentioned in hot posts Validate product ideas using discussed frameworks [Open Dashboard Button] 💡 Pro Tips for Success 🎯 Optimization Strategies Week 1-2: Baseline Monitor output quality and team engagement Note which insights generate the most discussion Week 3-4: Refinement Adjust AI prompts based on feedback Fine-tune hotness scoring for your needs Month 2+: Advanced Usage Add historical trend analysis Create custom dashboards with stored data Build feedback loops for continuous improvement 🚨 Common Pitfalls to Avoid | Issue | Solution | |-------|---------| | API Rate Limits | Reduce post count or increase time intervals | | Poor Insight Quality | Refine prompts with specific examples | | Team Engagement Drop | Rotate focus areas and encourage thread discussions | | Information Overload | Limit to top 3 posts and key themes only | 🔧 Troubleshooting ❌ Common Issues & Solutions "Model not found" Error Cause: Gemini regional availability Fix: Check supported regions or switch to alternative AI model Slack Formatting Broken Cause: Invalid Block Kit JSON Fix: Validate JSON structure in AI Agent output Missing Reddit Data Cause: API credentials or rate limits Fix: Verify OAuth2 setup and check usage quotas AI Timeouts Cause: Too much data or complex prompts Fix: Reduce post count or simplify analysis requests ⚡ Performance Optimization Keep analysis under 10 posts for optimal speed Monitor execution times in n8n logs Add error handling nodes for production reliability Use webhook timeouts for external API calls 🌟 Advanced Use Cases 📈 Competitive Intelligence Modify prompts to track specific competitors or market segments mentioned in discussions 🎯 Product Validation Focus analysis on posts related to your product category for market research 📝 Content Strategy Use trending topics to inform your content calendar and thought leadership 🤝 Community Engagement Identify opportunities to participate in discussions and build relationships Ready to transform your startup intelligence gathering? 🚀 Deploy this workflow and start receiving actionable insights tomorrow morning!

🤖 AI Business Automation Opportunity Finder Turn automation audits into high-ticket sales with this ROI-focused n8n workflow powered by 4 specialized AI agents that identify and quantify automation opportunities in any business. What It Does This workflow analyzes any business and delivers a comprehensive automation blueprint with concrete ROI calculations in under 60 seconds. Perfect for agencies, consultants, and automation experts looking to generate qualified leads and close high-value deals. Unlike generic automation advice, this delivers personalized, quantified opportunities ranked by return on investment - making it incredibly easy for prospects to say yes. 🤖 Four Specialized AI Agents Business Analyst - Deep analysis of business model, workflows, pain points, tech stack, and scalability challenges Process Mapper - Maps all repetitive processes, calculates time waste, identifies bottlenecks across the entire operation Automation Architect - Designs 15+ specific automation solutions with tools, complexity ratings, and implementation steps ROI Calculator - Calculates detailed ROI for each automation, ranks top 10, creates 90-day implementation roadmap ✨ Key Features Concrete Dollar Savings**: Every automation shows exact time saved, labor cost saved, and payback period Top 10 Ranked by ROI**: Opportunities prioritized by impact vs. effort with detailed financial analysis 90-Day Implementation Roadmap**: Month-by-month plan showing progressive savings milestones Comprehensive Process Mapping**: Identifies inefficiencies they didn't even mention Tool-Specific Recommendations**: Exact tools and platforms needed (n8n, Zapier, Make, etc.) Beautiful HTML Reports**: Professional, conversion-focused email with 3-tier pricing built in Multiple CTAs**: Strategically placed conversion points throughout the report 📊 What Gets Analyzed Business Analysis Business model and revenue streams Operational workflows and processes Current tech stack assessment Team capacity and resource allocation Growth stage and scalability blockers Industry-specific automation patterns Process Mapping Comprehensive workflow documentation Time waste analysis (hours per month) Bottleneck identification Process dependencies and integration opportunities Quick win vs. strategic project categorization Automation Architecture For each of 15+ automation opportunities: Clear description of what it automates Specific tools required Step-by-step implementation flow Complexity rating (Easy/Medium/Hard) Prerequisites and requirements Additional benefits beyond time savings Real-world use case examples ROI Calculations For each automation: Time saved per week/month/year Labor cost savings (calculated from team size/industry) One-time implementation cost Ongoing monthly costs Payback period in months 12-month net savings ROI percentage Priority score (0-10) 💼 Perfect For Automation Agencies**: High-value lead magnet that pre-sells your services Business Consultants**: Demonstrate ROI before engagement No-Code Developers**: Show concrete value of your expertise Digital Transformation Consultants**: Quantify the opportunity SaaS Companies**: Lead gen for automation/workflow tools Freelancers**: Land bigger clients with data-driven proposals 🚀 Why This Converts Better Than Other Lead Magnets Traditional Lead Magnets: Generic advice ("You should automate") Subjective benefits ("Save time") No clear next steps Conversion rate: 5-10% This Workflow: Specific to their business** (personalized analysis) Quantified in dollars** ($50K+ annual savings) Prioritized action plan** (top 10 ranked by ROI) Clear implementation path** (90-day roadmap) Conversion rate: 20-30%** to strategy call 40-50% of calls close** to paid engagement 📈 Expected Business Results Per 100 Form Submissions: 25-30 strategy calls booked** (25-30% conversion) 10-15 deals closed** (40-50% call-to-close rate) $12K-18K in initial revenue** (mix of Tier 1 & 2) 2-4 retainer clients** ($30K-60K annual value) Total potential: $42K-78K** from 100 leads Why It Works: Self-qualifying**: Detailed form filters serious prospects Pre-sold**: They see the value before the call ROI-focused**: Speaks CFO language (dollars, not features) Urgency**: Shows money being wasted daily Social proof**: Built-in testimonials and case studies 📋 What You Need Required n8n instance (self-hosted or cloud) Anthropic API key (Claude Sonnet 4.5) Gmail account or SMTP provider Optional Enhancements CRM integration (HubSpot, Salesforce, Pipedrive) Slack notifications for high-value leads Calendly for automatic call booking Zapier/Make for additional workflows Analytics tracking (Mixpanel, Segment) ⚙️ Technical Details AI Model**: Claude Sonnet 4.5 (4 sequential agents) Average Runtime**: 50-70 seconds Cost Per Analysis**: ~$0.20-0.30 Form Fields**: 9 (business description, industry, team size, tasks, tools, bottleneck, revenue, email, name) Output**: Comprehensive HTML email with all analyses, pricing, and CTAs 🎨 Customization Options The workflow is fully customizable and includes detailed documentation: Adjust ROI calculation parameters (labor rates by industry) Modify agent prompts for specific niches Customize pricing tiers and packages Add/remove form fields White-label the entire report Integrate with your CRM/marketing stack Segment responses by company size or revenue Add video walkthroughs or personalized messages Create industry-specific versions 📊 Form Fields Explained The 9-field form is strategically designed to gather intelligence: Business Description (textarea): Core operations and offerings Industry/Niche (text): Context for automation patterns Team Size (dropdown): Affects ROI calculations and tool recommendations Repetitive Tasks (textarea): Gold mine for automation opportunities Current Tools (textarea): Integration points and tech stack assessment Biggest Bottleneck (textarea): Primary pain point for targeting Monthly Revenue (optional dropdown): For accurate ROI estimates and lead scoring Email (required): For report delivery Name (required): For personalization 🔧 Setup Difficulty Basic - Requires basic n8n knowledge and API configuration Setup Steps Import workflow JSON to n8n Add Anthropic API credentials Configure Gmail/SMTP credentials Customize branding and pricing in email template Test with sample business scenarios Deploy form on your website Set up follow-up sequences (recommended) 📚 Included Documentation Comprehensive sticky notes** for every component Setup instructions** with prerequisites Customization guide** for different industries Pricing strategy** breakdown and alternatives Conversion optimization** tips Follow-up sequence** recommendations Sales script** suggestions for strategy calls Marketing promotion** ideas 🌟 Advanced Use Cases 1. Lead Magnet Embed on website to capture qualified automation leads continuously 2. Discovery Tool Use during sales calls to demonstrate immediate value and build credibility 3. Content Marketing Offer in LinkedIn posts, email campaigns, YouTube videos for viral growth 4. Partner Program White-label for partners/affiliates to generate leads in their networks 5. Upsell Sequence For existing clients, identify additional automation opportunities 6. Industry Templates Create versions for specific industries (real estate, e-commerce, agencies) 7. Competitive Intelligence Analyze competitor operations and position your services ⚡ Why This Workflow Stands Out Compared to Generic Automation Audits: ✅ Quantified in dollars vs. vague "save time" claims ✅ Personalized to their business vs. generic templates ✅ Prioritized by ROI vs. random feature lists ✅ Actionable roadmap vs. overwhelming possibilities ✅ Tool-specific vs. theoretical concepts Compared to Manual Analysis: ✅ 60 seconds vs. 2-3 hours of consultant time ✅ $0.25 cost vs. $300-500 in labor ✅ Consistent quality vs. variable analyst experience ✅ Scalable vs. bottlenecked by human capacity ✅ 24/7 available vs. business hours only 🤝 Support & Community 📖 Website: https://evervise.ai/ ✨ Support: mark.marin@evervise.com N8N Link 🎁 Bonus Resources Included Follow-up email sequence** (3 emails over 10 days) Sales call script** for strategy calls Objection handling** guide Pricing calculator** spreadsheet Marketing assets** (social media templates) Case study template** for testimonials Tags automation lead-generation roi-calculator business-analysis process-mapping ai-agents anthropic claude workflow-automation business-consulting no-code n8n-workflows high-ticket-sales conversion-optimization saas-tools Ready to turn automation audits into recurring revenue? Import this workflow and start attracting qualified leads who can see the exact dollar value you provide before they even talk to you. Average user results: $42K-78K revenue from first 100 form submissions.

What problem does this solve? Earlier this year, as I got more involved with n8n, I committed to helping users on our community forums and the n8n subreddit. The volume of questions was growing, and I found it was a real challenge to keep up and make sure no one was left without an answer. I needed a way to quickly see what people were struggling with, without spending hours just searching for new posts. So, I built this workflow. It acts as my personal AI research assistant. Twice a day, it automatically scans Reddit and the n8n forums for me. It finds relevant questions, summarizes the key points using AI, and sends me a digest with direct links to each post. This allows me to jump straight into the conversations that matter and provide support effectively. While I built this for n8n support, you can adapt it to monitor any community, track product feedback, or stay on top of any topic you care about. It transforms noisy forums into an actionable intelligence report delivered right to your inbox. How it works Here’s the technical breakdown of my two-part system: AI Reddit Digest (Daily at 9AM / 5 PM): Fetches the latest 50 posts from a specified subreddit. Uses an AI Text Classifier to categorize each post (e.g., QUESTION, JOB_POST). Isolates the posts classified as questions and uses an AI model to generate a concise summary for each. Formats the original post link and its new summary into an email-friendly format and sends the digest. AI n8n Forum Digest (Daily at 9AM / 5 PM): Scrapes the n8n community forum to get a list of the latest post links. Processes each link individually, fetching the full post content. Filters these posts to keep only those containing a specific keyword (e.g., "2025"). Summarizes the filtered posts using an AI model. Combines the original post link with its AI summary and sends it in a separate email report. Set up steps This workflow is quite powerful and requires a few configurations. Setup should take about 15 minutes. Add Credentials: First, add your credentials for your AI provider (like OpenRouter) and your email service (like Gmail or SMTP) in the Credentials section of your n8n instance. Configure Reddit Digest: In the Get latest 50 reddit posts node, enter the name of the Subreddit you want to follow. Fine-tune the AI's behavior by editing the prompt in the Summarize Reddit Questions node. (Optional) Add more examples to the Text Classifier node to improve its accuracy. Configure n8n Forum Digest: In the Filter 2025 posts node, change the keyword to track topics you're interested in. Edit the prompt in the Summarize n8n Forum Posts node to guide the AI's summary style. Activate Workflow: Once configured, just set the workflow to Active. It will run automatically on schedule. You can also trigger it manually with the When clicking 'Test workflow' node.

Overview: This n8n workflow automates the complete blog publishing process from topic research to WordPress publication. It researches topics, writes SEO-optimized content, generates images, publishes posts, and notifies teams—all automatically from Google Sheets input. How It Works: Step 1: Client Management & Scheduling Client Data Retrieval:** Scans master Google Sheet for clients with "Active" project status and "Automation" blog publishing setting Publishing Schedule Validation:** Checks if current day matches client's weekly frequency (Mon, Tue, Wed, Thu, Fri, Sat, Sun) or if set to "Daily" Content Source Access:** Connects to client-specific Google Sheet using stored document ID and sheet name Step 2: Content Planning & Selection Topic Filtering:** Retrieves rows where "Status for Approval" = "Approved" and "Live Link" = "Pending" Content Validation:** Ensures Focus Keyword field is populated before proceeding Single Topic Processing:** Selects first available topic to maintain quality and prevent API rate limits Step 3: AI-Powered Research & Writing Comprehensive Research:** Google Gemini analyzes search intent, competitor content, audience needs, trending subtopics, and LSI keywords Content Generation:** Creates 800-1000 word articles with natural keyword integration, internal linking, and conversational tone optimized for Indian investors Quality Assessment:** Evaluates content for human-like writing, conversational tone, readability, and engagement factors Content Optimization:** Automatically fixes grammar, punctuation, sentence flow, and readability issues while maintaining HTML structure Step 4: Visual Content Creation Image Prompt Generation:** OpenAI creates detailed prompts based on blog title and content for professional visuals Image Generation:** Ideogram AI produces 1248x832 resolution images with realistic styling and professional appearance Binary Processing:** Downloads and converts generated images to binary format for WordPress upload Step 5: WordPress Publication Media Upload:** Uploads generated image to WordPress media library with proper filename and headers Content Publishing:** Creates new WordPress post with title, optimized content, and embedded image Featured Image Assignment:** Sets uploaded image as post's featured thumbnail for proper display Category Assignment:** Automatically assigns posts to predefined category Step 6: Tracking & Communication Status Updates:** Updates Google Sheet with live blog URL in "Live Link" column using S.No. as identifier Team Notification:** Sends Discord message to designated channel with published blog link and review request Process Completion:** Triggers next iteration or workflow conclusion based on remaining topics Setup Steps: Estimated Setup Time: 45-60 minutes Required API Credentials: 1. Google Sheets API Service account with sheets access OAuth2 credentials for client-specific sheets Proper sharing permissions for all target sheets 2. Google Gemini API Active API key with sufficient quota Access to Gemini Pro model for content generation Rate limiting considerations for bulk processing 3. OpenAI API GPT-4 access for creative prompt generation Sufficient token allocation for daily operations Fallback handling for API unavailability 4. Ideogram AI API Premium account for quality image generation API key with generation permissions Understanding of rate limits and pricing 5. WordPress REST API Application passwords for each client site Basic authentication setup with proper encoding REST API enabled in WordPress settings User permissions for post creation and media upload 6. Discord Bot API Bot token with message sending permissions Channel ID for notifications Guild access and proper bot roles Master Sheet Configuration: Document Structure: Create primary tracking sheet with columns Client Name:** Business identifier Project Status:** Active/Inactive/Paused Blog Publishing:** Automation/Manual/Disabled Website URL:** Full WordPress site URL with trailing slash Blog Posting Auth Code:** Base64 encoded username: password On Page Sheet:** Google Sheets document ID for content planning WeeklyFrequency:** Daily/Mon/Tue/Wed/Thu/Fri/Sat/Sun Discord Channel:** Channel ID for notifications Content Planning Sheet Structure: Required Columns (exact naming required): S.No.:** Unique identifier for tracking Focus Keyword:** Primary SEO keyword Content Topic** Article title/subject Target Page:** Internal linking target Words:** Target word count Brief URL:** Content brief reference Content URL:** Draft content location Status for Approval:** Pending/Approved/Rejected Live Link:** Published URL (auto-populated) WordPress Configuration: REST API Activation:** Ensure wp-json endpoint accessibility User Permissions:** Create dedicated user with Editor or Administrator role Application Passwords:** Generate secure passwords for API authentication Category Setup:** Create or identify category ID for automated posts Media Settings:** Configure upload permissions and file size limits Security:** Whitelist IP addresses if using security plugins Discord Integration Setup: Bot Creation:** Create application and bot in Discord Developer Portal Permissions:** Grant Send Messages, Embed Links, and Read Message History Channel Configuration:** Set up dedicated channel for blog notifications User Mentions:** Configure user ID for targeted notifications Message Templates:** Customize notification format and content Workflow Features & Capabilities: Content Quality Standards: SEO Optimization:** Natural keyword integration with LSI keywords and related terms Readability:** Conversational tone with short sentences and clear explanations Structure:** Proper HTML formatting with headings, lists, and internal links Length:** Consistent 800-1000 word count for optimal engagement Audience Targeting:** Content tailored for Indian investor audience with relevant examples Image Generation Specifications: Resolution:** 1248x832 pixels optimized for blog headers Style:** Realistic professional imagery with human subjects Design:** Clean layout with heading text placement (bottom or left side) Quality:** High-resolution output suitable for web publishing Branding:** Light beige to gradient backgrounds with golden overlay effects Error Handling & Reliability: Graceful Failures:** Workflow continues even if individual steps encounter errors API Rate Limits:** Built-in delays and retry mechanisms for external services Data Validation:** Checks for required fields before processing Backup Processes:** Alternative paths for critical failure points Logging:** Comprehensive tracking of successes and failures Security & Access Control: Credential Encryption:** All API keys stored securely in n8n vault Limited Permissions:** Service accounts with minimum required access Authentication:** Basic auth for WordPress with encoded credentials Data Privacy:** No sensitive information exposed in logs or outputs Access Logging:** Track all sheet modifications and blog publications Troubleshooting: Common Issues: API Rate Limits:** Check your API quotas and usage limits WordPress Authentication:** Verify your basic auth credentials are correct Sheet Access:** Ensure Google Sheets API has proper permissions Image Generation Fails:** Check Ideogram API key and quotas Need Help?: For technical support or questions: Email: info@incrementors.com Contact Form: https://www.incrementors.com/contact-us/