Workflow Templates

8 nodes3 views

Get multimodal AI dating advice on Telegram with Google Gemini and Sheets

by John Alejandro SIlva

Rizz AI: The Multimodal Dating Assistant 💘 Rizz AI is not just a chatbot; it's a full-featured, AI-powered CRM for your dating life. Built entirely in n8n, this workflow turns Telegram into a powerful "Wingman" that helps you craft the perfect reply, remember details about your matches, and optimize your dating strategy using Google Gemini 1.5 Pro. 🔥 Key Features 👁️ Multimodal Vision:** Send a screenshot of a Tinder/Hinge profile or a WhatsApp chat, and the AI will analyze the text, subtext, and vibe to give you tactical advice. 🗣️ Audio Analysis:** Forward voice notes, and the AI will transcribe and analyze the tone to tell you if they are interested. 🧠 Long-Term Memory:** Remembers details about specific matches (e.g., "Sofia likes sushi") so you don't ask the same thing twice. 📊 Lead Management (CRM):** Automatically tracks matching stage, interest level, and next steps in Google Sheets. 🎨 Personalized Style:** Adapts advice to your specific "Rizz Style" (e.g., Mystery, Direct, Funny) defined in your profile. 🛠️ How It Works Ingest: You send text, audio, or images to your private Telegram Bot. Process: The workflow routes the input through Gemini Vision (for images) or Whisper/Gemini (for audio). Retrieve: It queries your Google Sheet to see if this person is a new lead or an existing match. Reason: The AI Agent (with tools) decides the best move: suggesting a reply, logging a red flag, or scheduling a date. Respond: You receive 3 draft options to copy-paste directly into your dating app. 📋 Setup Instructions 1. Google Sheets (Database) Make a copy of the Rizz AI Database Template. Share/Connect your Google Drive credential in n8n. Update the Sheet ID in the Get Rizzler Profile and other Sheet nodes. 2. Telegram Bot Talk to @BotFather on Telegram to create a new bot. Copy the API Token into the Telegram Trigger and Send Message nodes. 3. Google Gemini Get a free API Key from Google AI Studio. Connect it to the Google Gemini Chat Model node. 💡 Need Assistance? If you’d like help customizing or extending this workflow, feel free to reach out: 📧 Email: johnsilva11031@gmail.com 🔗 LinkedIn: John Alejandro Silva Rodríguez

7 nodes

Auto-respond and classify WhatsApp leads with Ollama AI and log to Google Sheets

by Tony Adijah

Who is this for This workflow is built for sales teams, agencies, and small businesses that receive inbound leads via WhatsApp and want to automate their first response, lead qualification, and CRM logging — without missing a single message. What this workflow does It listens for incoming WhatsApp messages, uses an AI agent to classify each message by intent (hot lead, warm lead, support, or needs qualification), sends a tailored auto-reply, logs every interaction to Google Sheets, and automatically books Google Calendar meetings with Meet links for qualified leads. How it works WhatsApp Trigger receives incoming messages and filters out bot/status messages to prevent loops. AI Agent (powered by Ollama or any connected LLM) classifies the message into one of four intent categories with confidence scoring. Smart Router directs each intent down a dedicated path. Hot & Warm Leads receive an instant reply, get logged to Google Sheets, have a Google Calendar meeting auto-booked, and receive the Meet link via WhatsApp. Support requests are logged and receive a ticket confirmation. Vague or incomplete messages trigger a smart follow-up question. Conversation memory ensures the AI re-classifies correctly when the user replies with more context. Setup steps Connect your WhatsApp Business API credentials (Meta Cloud API). Connect Google Sheets OAuth and set your spreadsheet ID in all three logging nodes. Connect Google Calendar OAuth and select your calendar in both booking nodes. Configure your LLM (Ollama endpoint, OpenAI, or any supported model). Update the BOT_NUMBERS array in the "Parse WhatsApp Message" node to match your WhatsApp Business phone number ID. Update the phoneNumberId in all WhatsApp Send nodes to your number. Send a test message and verify the full flow. Requirements WhatsApp Business API (Meta Cloud API) access Google Sheets and Google Calendar accounts with OAuth credentials An LLM endpoint (Ollama, OpenAI, or any n8n-supported model) n8n instance (cloud or self-hosted) How to customize Swap the AI model in the Ollama Chat Model node for OpenAI, Anthropic, or any supported LLM. Edit the auto-reply templates in each Reply code node to match your brand voice. Adjust meeting booking times (default: Hot = 2 hours out, Warm = 4 hours out). Add Slack or email notifications by branching from the Google Sheets logging nodes. Modify the AI classification prompt to add custom intent categories for your business.

7 nodes

Newsletter Summarization & Briefing with Gmail, AI, Google Sheets & Email

by Jan Zaiser

Your inbox is overflowing with daily newsletters: Public Affairs, ESG, Legal, Finance, you name it. You want to stay informed, but reading 10 emails every morning? Impossible. What if you could get one single digest summarizing everything that matters, automatically? ❌ No more copy-pasting text into ChatGPT ❌ No more scrolling through endless email threads ✅ Just one smart, structured daily briefing in your inbox Who Is This For Public Affairs Teams: Stay ahead of political and regulatory updates—without drowning in emails. Executives & Analysts: Get daily summaries of key insights from multiple newsletters. Marketing, Legal, or ESG Departments: Repurpose this workflow for your own content sources. How It Works Gmail collects all newsletters from the day (based on sender or label). HTML noise and formatting are stripped automatically. Long texts are split into chunks and logged in Google Sheets. An AI Agent (Gemini or OpenAI) summarizes all content into one clean daily digest. The workflow structures the summary into an HTML email and sends it to your chosen recipients. Setup Guide • You’ll need Gmail and Google Sheets credentials. • Add your own AI Model (e.g., Gemini or OpenAI) with an API key. • Adjust the prompt inside the “Public Affairs Consultant” node to fit your topic (e.g., Legal, Finance, ESG, Marketing). • Customize the email subject and design inside the “Structure HTML-Mail” node. • Optional: Use Memory3 to let the AI learn your preferred tone and style over time. Cost & Runtime Runs once per day. Typical cost: ~$0.10–0.30 per run (depending on model and input length). Average runtime: <2 minutes.

🤖AI & Machine Learning

7 nodes6 views

Beginner's Guide to Workflow Automation with OpenAI, LangChain & API Integrations

by Meelioo

How it works This beginner-friendly workflow demonstrates the core building blocks of n8n. It guides you through: Triggers – Start workflows manually, on a schedule, via webhooks, or through chat. Data processing** – Use Set and Code nodes to create, transform, and enrich data. Logic and branching – Apply conditions with IF nodes and merge different branches back together. API integrations** – Fetch external data (e.g., users from an API), split arrays into individual items, and extract useful fields. AI-powered steps** – Connect to OpenAI for generating fun facts or build interactive assistants with chat triggers, memory, and tools. Responses** – Return structured results via webhooks or summary nodes. By the end, it demonstrates a full flow: creating data → transforming it → making decisions → calling APIs → using AI → responding with outputs. Set up steps Time required: 5–10 minutes. What you need: An n8n instance (cloud or self-hosted). Optional: API credentials (e.g., OpenAI) if you want to test AI features. Setup flow: Import this workflow. Add your API keys where needed (OpenAI, etc.). Trigger the workflow manually or test with webhooks. >👉 Detailed node explanations and examples are already included as sticky notes inside the workflow itself, so you can learn step by step as you explore.

📊Data Processing

6 nodes1 views

Monitor Brand Mentions on X with Gemini AI Visual Analysis & Telegram Alerts

by Atta

This workflow automates brand monitoring on X by analyzing both the text and the images in posts. It uses multi-modal AI to score brand relevance, filters out noise, logs important mentions in Airtable, and sends real-time alerts to a Telegram group for high-priority posts. What it does Traditional brand monitoring tools often miss the most authentic user content because they only track text. They can't "see" your logo in a photo or your product featured in a video without a direct keyword mention. This workflow acts as an AI agent that overcomes this blind spot. It finds mentions of your brand on X and then uses Google Gemini's multi-modal capabilities to perform a comprehensive analysis of both the text and any attached images. This allows it to understand the full context of a mention, score its relevance to your brand, and take the appropriate action, creating a powerful "visual intelligence" system. How it works The workflow runs on a schedule to find, analyze, and triage brand mentions. Get New Tweets: The workflow begins by using an Apify actor to scrape X for recent posts based on a defined set of search terms (e.g., Tesla OR $TSLA). It then filters these results to find unique mentions not already processed. Check for Duplicates: It cross-references each found tweet with an Airtable base to ensure it hasn't been analyzed before, preventing duplicate work. Analyze Post Content: For each new, unique post, the workflow performs two parallel analyses using Google Gemini: Analyze the Photos: The AI examines the images in the post to describe the scene, identify logos or products, and determine the visual mood. Analyze the Text: A separate AI call analyzes the text of the post to understand its context and sentiment. Final Relevance Check: A "Head Strategist" AI node receives the outputs from both the visual and text analyses. It synthesizes this information to assign a final brand relevance score from 1 to 10. Triage and Action: Based on this score, the workflow automatically triages the post: High Relevance (Score > 7): The post is logged in the Airtable base, and an instant, detailed alert is sent to a Telegram monitoring group. Medium Relevance (Score 4-7): The post is quietly logged in Airtable for later strategic review. Low Relevance (Score < 4): The post is ignored, effectively filtering out noise. Setup Instructions To get this workflow running, you will need to configure your Airtable base and provide credentials for Apify, Google, and Telegram. Required Credentials Apify: You will need an Apify API Token to run the X scraper. Airtable: You will need Airtable API credentials to connect to your base. Google AI: You will need credentials for the Google AI APIs to use the Gemini models. Telegram: You will need a Bot Token and the Chat ID for the channel where you want to receive high-relevance alerts. Of course. Based on the Config node parameters you provided, the setup process is much more centralized. Here is the corrected and rewritten "Step-by-Step Configuration" section. Of course. Here is the rewritten "Step-by-Step Configuration" section with the link to the advanced search documentation. Step-by-Step Configuration Set up Your Airtable Base: Before configuring the workflow, create a new table in your Airtable base. For the workflow to function correctly, this table must contain fields to store the analysis results. Create fields with the following names: postId, postURL, postText, postDateCreated, authorUsername, authorName, sentiment, relevanceScore, relevanceReasoning, mediaPhotosAnalysis, and status. Once the table is created, have your Base ID and Table ID ready to use in the Config node. Edit the Config Node: The majority of the setup is handled in the first Config node. Click on it and edit the following parameters in the "Expressions" tab: searchTerms: Replace the example with the keywords, hashtags, and accounts you want to monitor. The field supports advanced search operators for complex queries. For a full list of available parameters, see the Twitter Advanced Search documentation. airtableBaseId: Paste your Airtable Base ID here. airtableTableId: Paste your Airtable Table ID here. lang: Set the two-letter language code for the posts you want to find (e.g., "en" for English). min_faves: Set the minimum number of "favorites" a post should have to be considered. tweetsToScrape: Define the maximum number of posts the scraper should find in each run. actorId: This is the specific Apify actor for scraping X. You can leave this as is unless you intend to use a different one. Configure the Telegram Node: In the final node, "Send High Relevance Posts to Monitoring Group", you need to manually set the destination for the alerts. Enter the Chat ID for your Telegram group or channel. How to Adapt the Template This workflow is a powerful framework that can be adapted for various monitoring needs. Change the Source:* Replace the *Apify** node with a different trigger or data source. You could monitor Reddit, specific RSS feeds, or a news API for mentions. Customize the AI Logic:* The core of this workflow is in the AI prompts. You can edit the prompts in the *Google Gemini** nodes to change the analysis criteria. For example, you could instruct the AI to check for specific competitor logos, analyze the sentiment of comments, or identify if the post is from an influential account. Modify the Scoring:** Adjust the logic in the "Switch" node to change the thresholds for what constitutes a high, medium, or low-relevance post to better fit your brand's needs. Change the Actions:* Replace the *Telegram** node with a different action. Instead of sending an alert, you could: Create a ticket in a customer support system like Zendesk or Jira. Send a summary email to your marketing team. Add the post to a content curation tool or a social media management platform.

🤖AI & Machine Learning

8 nodes2 views

Detect Viral YouTube Videos with AI Analysis and Email Reports

by gclbck

Analyze YouTube videos for virality with an AI-powered report This workflow automates the discovery and analysis of potentially viral YouTube videos. It searches for recent, popular videos based on a keyword, calculates a unique "Algorithmic Lift Score" to measure virality, and uses an AI agent to generate an insightful summary report that is sent directly to your email. What it does This workflow identifies videos that are outperforming their channel's baseline, a key indicator of viral potential. It operates in several stages: Searches YouTube: It finds recent, top-performing videos based on your specified keyword and timeframe. Gathers Data: For each video found, it fetches detailed statistics for both the video (views, likes, comments) and its channel (subscriber count, total views). Calculates Virality Score: It calculates an "Algorithmic Lift Score" for each video. This custom metric prioritizes videos that achieve high view counts and engagement relative to their channel's subscriber base. Analyzes with AI: The top 5 videos, sorted by their virality score, are sent to an AI agent (pre-configured for OpenAI). The AI generates a concise summary highlighting trends, top performers, and other noteworthy patterns. Sends Email Report: The final AI-generated analysis is converted to HTML and emailed to you, providing a ready-to-read report on what's trending in your niche. Who it's for This workflow is perfect for: Content Creators** looking for trending topics and content ideas. Digital Marketers** conducting competitor analysis or market research. Social Media Managers** wanting to understand what content resonates on YouTube. Data Analysts** who need to automate the collection and analysis of YouTube trends. Requirements A Google API Key with the "YouTube Data API v3" enabled. An OpenAI API Key (or another compatible AI model credential). A connected Gmail account in n8n to send the final report. How to set up Configure the Setup Node: Click on the "Setup" node and fill in the values: query: The keyword you want to search for (e.g., "AI tools"). GoogleAPIkey: Your Google API key. daysback: How many days in the past to search for new videos. maxResult: The number of videos to analyze (e.g., 20). email: The email address where the report will be sent. Set AI Credentials: Click the "OpenAI Chat Model" node and add your OpenAI API key to the credentials. Set Gmail Credentials: Click the "Send_Report" node and connect your Gmail account to the credentials.

6 nodes1 views

Create AI-generated Spotify playlists with GPT-5 and email the link

by Ufuk Ören

How it works: When a user submits a form with event details, the workflow sends this information to OpenAI's GPT-5 model, which generates a curated list of 18–32 songs tailored to the occasion and audience. The workflow then searches Spotify for each recommended song, creates a new playlist on the user's Spotify account, adds all the tracks, and sends the user an email with a direct link to listen. Key Features: AI-Powered Playlist Generation: Uses OpenAI's GPT-5 model to create contextually relevant song recommendations based on event type, audience, and personal preferences. Automated Spotify Integration: Searches Spotify's API for each song and automatically adds them to a new playlist on the user's account. One-Click Listening: Users receive an email with a direct Spotify link to listen, follow, and modify the playlist immediately after creation. Multi-Language Support: The AI responds in the same language as the user's input, making the workflow accessible globally. Email Confirmation: Users instantly receive an email notification confirming playlist creation with a shareable Spotify link. Step-by-step: Form Submission Trigger: User submits event details (occasion, guests, preferences, and email) through the web form. AI Playlist Generation: OpenAI's GPT-5 generates a curated list of 18–32 songs based on the provided event context. Spotify Integration: The workflow searches Spotify for each song, creates a new playlist, and adds all found tracks to the user's account. Email Notification: User receives an email with the playlist name and a direct Spotify link to listen immediately. API Keys Required OpenAI API Key: Required to access GPT-5 for generating playlist recommendations. Spotify Client ID & Secret: Required to authenticate with Spotify's API for searching, creating playlists, and adding tracks. SMTP Credentials: Required to send the confirmation email to the user (server, port, username, password, and sender email). Response Generation The AI model receives event details and generates a structured JSON response containing a playlist name, 18–32 song recommendations with artist credits, and HTML-formatted content for website integration, all in the user's language.

6 nodes

Automate unified marketing reports with Google Analytics, Google Ads, Meta Ads & HubSpot

by Avkash Kakdiya

How it works This workflow runs on scheduled weekly and monthly triggers to generate unified marketing performance reports. It processes multiple websites by collecting analytics data, paid ads performance, and CRM leads, then calculates KPIs and insights automatically. The workflow sends structured reports via email and stores historical data in Google Sheets. It ensures consistent reporting without manual effort. Step-by-step Step 1: Trigger & report type detection** Schedule Trigger2 – Triggers the workflow weekly at a predefined time. Schedule Trigger3 – Triggers the workflow monthly at a predefined time. check month and week1 – Identifies whether the run is weekly or monthly and sets flags. Set Websites and Campaings1 – Defines websites, GA4 property IDs, and mapped ad campaigns. Expand Websites1 – Expands the website array into individual website items. Attach Run Flags1 – Attaches weekly or monthly flags to each website record. Step 2: Website & ads data processing** Loop Websites1 – Iterates through each website independently. Get a report – Fetches website traffic and engagement metrics from analytics. Get many campaigns – Retrieves Google Ads campaign data. Fetch Meta Ads – Fetches Meta Ads performance data via API. Filter Google Ads By Website1 – Filters Google Ads campaigns by website. Filter Meta Ads By Website1 – Filters Meta Ads campaigns by website. Merge1 – Merges analytics, Google Ads, and Meta Ads datasets. Build Website Dataset1 – Builds a unified dataset per website. Calculate KPIs & Campaign Insights1 – Calculates spend, CTR, CPA, CPL, conversions, and performance insights. Append or update row in sheet2 – Stores website-level marketing metrics in Google Sheets. Step 2.1: Marketing report generation** Prepare Report Data2 – Combines all website datasets into a unified report object. Switch – Routes execution based on weekly or monthly report type. Send Weekly Marketing report2 – Sends the weekly marketing performance email. Send Monthly Marketing Report2 – Sends the monthly marketing performance email. Step 3: HubSpot lead analysis** Fetch1 – Fetches leads from HubSpot CRM. Filter Hubspot Leads – Filters leads based on weekly or monthly time range. Summarize Hubspot Leads – Aggregates lead status and lifecycle metrics. Prepare Report Data3 – Prepares CRM summary data for reporting. Step 3.1: CRM reporting & storage** Switch3 – Routes CRM reporting by report type. Send Weekly Marketing report3 – Sends the weekly CRM summary email. Send Monthly Marketing Report3 – Sends the monthly CRM summary email. Code in JavaScript1 – Transforms CRM data for storage. Append or update row in sheet3 – Stores CRM lead performance data in Google Sheets. Switch3 – Routes CRM reporting by report type. Send Weekly Marketing report3 – Sends the weekly CRM summary email. Send Monthly Marketing Report3 – Sends the monthly CRM summary email. Code in JavaScript1 – Transforms CRM data for storage. Append or update row in sheet3 – Stores CRM lead performance data in Google Sheets. Why use this? Automates complex weekly and monthly marketing reporting. Unifies website analytics, ad platforms, and CRM data in one flow. Delivers consistent KPI calculations and insights every run. Maintains historical performance logs in Google Sheets. Scales easily across multiple websites and campaigns.

7 nodes

Automate Lead Qualification & Routing with GPT-4o-mini, Google Sheets & HighLevel CRM

by Rahul Joshi

Description Automatically qualify and route new leads from a Google Sheet into your CRM with AI-powered scoring and instant sales notifications. Turn raw form submissions into prioritized opportunities—effortlessly. ⚡ What This Template Does Monitors a Google Sheet for new form submissions. 📄 Uses Azure OpenAI (GPT-4o-mini) to analyze lead details (value, stage, company) and generate action items. 🤖 Parses the AI response into clean JSON for structured processing. 🗂️ Saves qualified lead data and AI-generated action items into a Lead Status sheet for tracking. 💾 Categorizes leads into Hot, Warm, or Cold based on AI scoring. 🔥❄️ Creates/updates the contact in HighLevel CRM. 📇 Sends an email notification to the assigned sales rep with lead details and priority. 📧 Key Benefits Save time with automated lead qualification instead of manual checks. ⏱️ Ensure consistent Hot/Warm/Cold scoring across all leads. ✅ Centralize lead data in both Google Sheets and CRM for tracking. 📊 Keep sales teams aligned with instant notifications. 🚀 Fully no-code configurable and customizable for your business logic. 🧩 Features Google Sheets Trigger for new form rows. 📥 AI Agent with Azure OpenAI (GPT-4o-mini) for lead scoring. 🧠 JSON parsing node to clean AI output. ✂️ Lead logging to “Lead Status” sheet. 📊 Function node to categorize leads by score. 🎯 CRM sync with HighLevel to update/create contact records. 🔗 SMTP email notification to sales reps. ✉️ Requirements n8n instance (cloud or self-hosted). 🧰 Google Sheet with headers: Lead Name, Lead Email, Lead Contact No., Company Name, Opportunity Value, Stage of Lead; shared with n8n Google account. 📑 Azure OpenAI access with a GPT-4o-mini deployment. ☁️ HighLevel CRM account connected via OAuth. 📇 SMTP email account configured in n8n. 📧 Target Audience Sales teams handling inbound leads. 📈 Agencies managing multiple client pipelines. 🤝 Founders/startups wanting quick qualification and CRM sync. 🚀 Ops teams needing reliable reporting of lead qualification. 🗂️ Step-by-Step Setup Instructions (Concise) Create a Google Sheet with required headers; share with n8n account. 📋 Configure the Google Sheets Trigger with the sheet’s Document ID. 🔐 Connect your Azure OpenAI credentials and link to the AI Agent node. 🧠 Assign your HighLevel CRM account credentials. 📇 Set up SMTP credentials for the email send node. ✉️ Import the workflow, update node configs, and run a test submission. ▶️ Security Best Practices Share Google Sheets only with the n8n Google account (Editor). 🔒 Keep API keys and credentials encrypted in n8n, not hardcoded. 🛡️ Validate AI outputs before saving to CRM (via the parse node). ✅ Regularly back up your Lead Status sheet and CRM data. 📂

6 nodes3 views

Triage customer complaint emails from Gmail with GPT-4o-mini, Slack and Sheets

by Incrementors

Description Activate this workflow once and it monitors your Gmail inbox every minute automatically. Every incoming email is scanned for complaint keywords — and if a complaint is detected, GPT-4o-mini scores its urgency, identifies the right department, writes a one-line summary, and drafts a ready-to-send reply. The complaint is logged to Google Sheets and a formatted alert is posted to the correct Slack channel instantly. Built for support teams, customer success managers, and small businesses who can't afford to miss or delay a complaint. What This Workflow Does Automatic inbox monitoring** — Checks your Gmail every minute so no complaint sits unread while your team is busy with other work Keyword-based complaint detection** — Scans subject lines and email bodies for 12 complaint signals and silently ignores everything else AI urgency scoring** — Rates every complaint from 1 to 10 so your team knows at a glance what needs attention first Department routing** — Classifies each complaint as Billing, Technical, or General and sends the alert to the right Slack channel automatically Reply draft generation** — GPT-4o-mini writes a professional, empathetic reply your agent can send immediately — no drafting from scratch Permanent complaint log** — Appends a 12-column record to Google Sheets for every complaint, giving you a searchable history for reporting and pattern analysis Clean non-complaint exit** — Emails that are not complaints pass through silently without triggering any logging or alerts Setup Requirements Tools Needed n8n instance (self-hosted or cloud) Gmail account (the inbox you want monitored) OpenAI account with GPT-4o-mini API access Slack workspace with OAuth2 app configured Google Sheets (one sheet with a tab named Complaint Log) Credentials Required Gmail OAuth2 OpenAI API key Slack OAuth2 Google Sheets OAuth2 Estimated Setup Time: 15–20 minutes Step-by-Step Setup Import the workflow — Open n8n → Workflows → Import from JSON → paste the workflow JSON → click Import Connect Gmail — Open node 1. Gmail — Inbox Monitor → click the credential dropdown → select OAuth2 → sign in with the Gmail account you want monitored → authorize access Fill in your config values — Open node 2. Set — Config Values → replace all seven placeholders: | Field | What to put here | |---|---| | YOUR COMPANY NAME | Your business name (used in the AI reply) | | YOUR_GOOGLE_SHEET_ID | The ID from your Google Sheet URL (the string between /d/ and /edit) | | Complaint Log | Leave as-is, or match your sheet tab name exactly | | #billing-support | Your Slack channel for billing complaints | | #tech-support | Your Slack channel for technical complaints | | #customer-support | Your Slack channel for general complaints | | support@yourcompany.com | Your support team's reply-from email | Connect OpenAI — Open node 6. OpenAI — GPT-4o-mini Model → click the credential dropdown → add your OpenAI API key → test the connection Connect Google Sheets — Open node 9. Google Sheets — Log Complaint → click the credential dropdown → connect your Google account via OAuth2 → make sure your sheet has a tab named exactly Complaint Log with these 12 column headers in row 1: Email ID, Received Date, Sender Name, Sender Email, Subject, One Line Summary, Department, Urgency Score, Urgency Level, Suggested Reply, Slack Channel Alerted, Logged At Connect Slack — Open node 10. Slack — Send Department Alert → click the credential dropdown → connect your Slack workspace via OAuth2 → invite the n8n bot to all three complaint channels in Slack (/invite @n8n in each channel) Activate the workflow — Toggle the workflow to Active — it will begin polling Gmail every minute immediately How It Works (Step by Step) Step 1 — Gmail: Inbox Monitor This step checks your Gmail inbox every 60 seconds for new emails. Every new message that arrives is passed to the next step for processing. Nothing is read, flagged, or deleted — it is read-only access. Step 2 — Set: Config Values Your company name, Google Sheet ID, sheet tab name, three Slack channel names, and support email are stored here as named variables. You set these once and every other step in the workflow uses them automatically. Step 3 — Code: Extract Email Fields This step pulls the sender name, sender email, subject line, and email body out of the raw Gmail data. The email body is trimmed to 3,000 characters to keep AI processing fast and cost-efficient. A timestamp is also captured for the log. Step 4 — IF: Is This a Complaint? This is the filter step. It checks whether the subject or body contains any of 12 complaint signals: words like "complaint", "refund", "not working", "unacceptable", "very unhappy", "demand refund", and others. If a match is found (YES path), the email moves forward to AI triage. If no match is found (NO path), the email flows to 11. NoOp — Not a Complaint and the workflow stops silently — no logging, no alert. Step 5 — AI Agent: Triage Complaint GPT-4o-mini reads the sender name, subject, and email body. It returns exactly four pieces of data: an urgency score from 1 to 10, the correct department (Billing, Technical, or General), a one-line summary of the complaint, and a ready-to-send reply draft under 80 words addressed to the customer by name. Step 6 — OpenAI: GPT-4o-mini Model This is the language model powering the triage step. It runs at temperature 0.3 for consistent, structured responses and is capped at 600 tokens to keep costs low per complaint. Step 7 — Parser: Structured Triage Output This step enforces the exact data structure GPT-4o-mini must return. It validates that urgency score is a number, department is one of the three valid options, and both the summary and reply fields are present. This prevents malformed AI output from reaching your sheet or Slack. Step 8 — Code: Combine Triage Data All AI results are merged with the original email data here. This step also converts the numeric urgency score into a human-readable label: 1–3 = Low, 4–5 = Medium, 6–7 = High, 8–10 = CRITICAL. It also selects the correct Slack channel based on the department GPT-4o-mini assigned. Step 9 — Google Sheets: Log Complaint A new row is appended to your Complaint Log sheet with all 12 fields: email ID, received date, sender details, subject, AI summary, department, urgency score, urgency label, suggested reply, which Slack channel was alerted, and the time it was logged. Step 10 — Slack: Send Department Alert A formatted Slack message is posted to the correct channel — #billing-support, #tech-support, or #customer-support — depending on the department. The message shows the urgency label, score, sender details, subject, AI summary, and the suggested reply text ready for the agent to copy and send. The final result: your team sees a structured complaint alert in Slack within seconds of the email arriving, with the reply already written. Key Features ✅ Runs without any manual trigger — Gmail polling fires every minute automatically once the workflow is active ✅ Zero noise for non-complaints — Regular emails pass through silently with no logging, no alerts, and no wasted API calls ✅ CRITICAL flag for urgent cases — Any complaint scoring 8 or above is labelled CRITICAL in Slack so high-risk customers get immediate attention ✅ Ready-to-send reply included — Every Slack alert contains a drafted reply addressed to the customer by name — agents copy, review, and send ✅ Structured AI output enforced — A schema parser ensures GPT-4o-mini always returns the correct fields in the correct format, preventing broken logs ✅ Three-channel Slack routing — Billing, Technical, and General complaints each go to their own channel automatically — no manual sorting ✅ 12-column complaint history — Every complaint is permanently recorded in Google Sheets for weekly reporting, pattern spotting, and team reviews ✅ Token-efficient processing — Email body is capped at 3,000 characters and AI is capped at 600 tokens — keeping costs predictable at scale Customisation Options Add more complaint keywords — In node 4. IF — Is This a Complaint?, add more trigger phrases to the condition (e.g. very frustrated, cancel my account, escalate) to catch complaints your current list misses. Adjust urgency thresholds — In node 8. Code — Combine Triage Data, change the score cutoffs for Low, Medium, High, and CRITICAL to match your team's definition of urgency (e.g. raise CRITICAL from 8 to 9 for a stricter threshold). Add a Gmail label to processed complaints — After node 10. Slack — Send Department Alert, add a Gmail node set to "Add Label" to tag every processed complaint in your inbox (e.g. "Triaged") so agents know which emails the workflow has already handled. Send a copy to email — After node 8. Code — Combine Triage Data, add a Gmail Send node to also email the suggested reply and triage details to your support team inbox as a backup alongside Slack. Weekly summary report — Add a Schedule trigger that runs every Monday morning, reads the Google Sheet via a Sheets node, counts complaints by department and urgency level, and posts a summary to a #support-weekly Slack channel. Route CRITICAL complaints to a separate Slack channel — In node 8. Code — Combine Triage Data, add a condition: if urgencyLabel === 'CRITICAL', override slackChannel with a dedicated #escalations channel so your most urgent cases never get buried. Troubleshooting Gmail not triggering the workflow: Make sure the workflow is toggled to Active — inactive workflows do not poll Check that the Gmail OAuth2 credential in node 1. Gmail — Inbox Monitor is connected and not expired — re-authorize if needed Send a test email to the monitored inbox and wait up to 60 seconds for the next poll cycle OpenAI credential not working: Confirm the API key is connected in node 6. OpenAI — GPT-4o-mini Model, not in a different step Check that your OpenAI account has available credits Verify the key has access to gpt-4o-mini — restricted keys may block this model Google Sheets not logging rows: Confirm the Sheet ID in node 2. Set — Config Values matches the ID in your Google Sheet URL exactly Make sure the tab is named Complaint Log — the name must match sheetName in Config Values exactly, including capitalization Check that the Google Sheets OAuth2 credential in node 9. Google Sheets — Log Complaint is connected and authorized Slack alerts not arriving: Confirm the Slack OAuth2 credential in node 10. Slack — Send Department Alert is connected Make sure the n8n bot has been invited to all three channels — type /invite @n8n in each channel in Slack Check that the channel names in node 2. Set — Config Values include the # prefix and match exactly AI returning wrong department or malformed output: The structured output parser in node 7. Parser — Structured Triage Output enforces the schema — if GPT returns an unexpected format, check the execution log of node 5. AI Agent — Triage Complaint for the raw output If department values are inconsistent, the prompt in node 5 explicitly requires exactly Billing, Technical, or General — any deviation means the AI call failed and you should re-run Support Need help setting this up or want a custom version built for your team or agency? 📧 Email: info@incrementors.com 🌐 Website: https://www.incrementors.com/contact-us/

6 nodes

Validate property compliance risk and orchestrate actions with OpenAI, Google Calendar, Gmail, Slack, and Google Sheets

by Cheng Siong Chin

How It Works This workflow automates medical imaging analysis and diagnostic reporting for radiology departments, imaging centers, and hospital networks managing high patient volumes. Designed for radiologists, medical imaging technicians, and diagnostic coordinators, it solves the challenge of rapidly analyzing imaging studies, prioritizing critical findings, routing cases appropriately, and generating structured reports while maintaining diagnostic accuracy and regulatory compliance. The system triggers on new imaging studies, fetches imagery and metadata, prepares data through AI agents (Validation ensures image quality and completeness), calculates risk scores, routes by validation status and risk level through multiple pathways, deploys specialized AI agents for comprehensive analysis (Orchestration coordinates findings, Google Calendar manages scheduling, Slack Tool enables team communication, Email Actions handles notifications, Water Monitoring tracks contrast protocols, Compliance Validation ensures regulatory adherence, Leave Management coordinates radiologist availability), and generates final diagnostic reports with complete audit trails. Organizations reduce diagnosis turnaround time by 60%, improve critical finding detection rates, ensure consistent reporting standards, and enable radiologists to focus on complex cases requiring expert judgment. Setup Steps Connect imaging trigger for automatic study notifications Configure PACS/VNA system APIs with credentials for DICOM image retrieval and metadata access Add AI model API keys to Validation Agent and specialized diagnostic agents Define risk stratification criteria in routing logic based on clinical protocols and imaging findings Link Google Calendar API for radiologist scheduling and case assignment workflows Configure Slack integration for care team communication and critical finding alerts Connect email system for patient/referring physician notifications and report distribution Prerequisites PACS/VNA system API access, HIPAA-compliant AI service accounts Use Cases Emergency radiology triage (stroke, trauma), lung nodule detection and tracking Customization Modify AI models for modality-specific analysis (CT, MRI, X-ray, ultrasound) Benefits Reduces diagnosis turnaround time by 60%, improves critical finding detection rates