Google Sheets and Postgres integration

The AI-powered MIS Agent is an intelligent, automated system built using n8n that streamlines email-based data collection and document organization for businesses. It classifies incoming emails, extracts and processes attachments or Drive links, and routes them to the correct destination folders in Google Drive. Additionally, it provides advanced file operations like cleaning, merging, joining, and transforming data. Advantages 📥 Automated Email and File Management Detects and processes emails containing attachments or Drive links, ensuring seamless classification and routing of business-critical files. 🧠 AI-Based Classification Uses LLMs (like GPT-4o Mini) to classify emails into categories such as Daily Sales, Customer Info, and Address based on their content. 📂 Smart File Routing and Upload Recognizes whether a file is a direct attachment or a Google Drive link, extracts the file ID if necessary, and uploads it to predefined folders. 📊 Powerful Data Operations Supports operations like append, join, group by, aggregation, and standardization of data directly from spreadsheets using Python and Pandas within the workflow. 🔁 Scheduled and Triggered Automation Supports scheduled runs and real-time email triggers, making it highly reliable and timely. 🔧 Fully Modular and Scalable Easily expandable with more logic, new folders, or different workflows. Clean architecture and annotations make maintenance simple. How It Works Email Trigger The system uses a Gmail trigger to monitor incoming emails with specific labels or attachments. Classification An LLM-based text classifier identifies the purpose of the email (e.g., sales data, address list, customer details). Conditional Logic Regex-based conditions check if the email contains Google Drive links or attachments. File Handling If it's a Drive link, it extracts the file ID and copies it to the correct folder. If it's an attachment, it uploads directly. Scheduled Data Management Periodically moves or logs files from predefined folders using a schedule trigger. Data Cleaning and Processing Performs data cleaning and transformation tasks like replacing missing values, standardizing formats, and joining datasets based on criteria provided by the user. Final Output Cleaned and processed files are saved in designated folders with their public links shared back through the system. Set Up Steps Configure Nodes: Gmail Trigger: Detects relevant incoming emails. Text Classifier: Uses OpenAI model to categorize email content. Regex Conditions: Determine whether a link or attachment is present. Google Drive Operations: Upload or copy files to categorized folders. Python Nodes: Handle data manipulation using Pandas. Google Sheets Nodes: Extract, clean, and write structured data. LLM-based Chat Models: Extract and apply cleaning configurations. Connect Nodes: Seamlessly connect Gmail inputs, classification, file processing, and data logic. Output links or processed files are uploaded back to Drive and ready to share. Credentials: Ensure OAuth credentials for Gmail, Google Drive, and OpenAI are correctly set. Ideal For Sales & CRM teams managing large volumes of email-based reports. Data teams needing structured pipelines from unstructured email inputs. Businesses looking to automate classification, storage, and transformation of routine data. Testing and Additional customization If you want to test this bot capability before purchasing the workflow. ask me on my mail [email protected] I will share the chat url and the links of associated google drives to see the result once you are satisfied then we are good to go. I have just kept $1 for testing purposes because of paid open ai . -If there is any customization needed like charts and other request like adding databases feel free to let me know i can do it accordingly. This is the first version i will come with more advancements based on the request and responses. Use it and let me know on [email protected]

🐶🤖 AI Agent for Pet Shops – Automate Customer Service & Bookings! 🐾💡 Transform Your Pet Shop with AI-Powered Automation! 🚀 Enhance customer experience and optimize operations with this n8n AI Agent designed for pet shops. 📲🐾 Automate client interactions, appointment scheduling, and service recommendations—saving time and increasing revenue! 🔹 Key Features: ✅ Instant WhatsApp responses – AI-powered chatbot handles customer inquiries. 💬 ✅ Automated appointment scheduling – Clients can book services hassle-free. 📅✂️ ✅ Personalized reminders – Reduce no-shows with automated notifications. 📢🐾 ✅ Customer data & service history management – Track interactions effortlessly. 📊📁 ✅ Product & service recommendations – Improve sales with smart suggestions. 🎁🐶 📌 How It Works 1️⃣ The workflow captures customer inquiries via WhatsApp. 2️⃣ AI processes requests, provides information, and offers booking options. 3️⃣ Clients can schedule grooming, vet visits, or other services in seconds. 4️⃣ Automated reminders ensure appointments are remembered. 5️⃣ Customer data is stored for better service personalization. ⚙️ Setup & Customization 🔧 Connect your WhatsApp API (evolution) for instant messaging. 🔧 Integrate with Google Calendar for appointment booking. 🔧 Customize reminders, services, and pricing rules to fit your business. 💡 Reduce manual work, improve customer satisfaction, and scale your pet shop with AI automation! 🐶🤖 [PT-BR] Agente de IA para Pet Shops – Atendimento e Agendamentos Automatizados! 🐾💡 Transforme Seu Pet Shop com Automação Inteligente! 🚀 Otimize o atendimento ao cliente e agilize processos com este Agente de IA para n8n. 📲🐾 Automatize interações, agendamentos e recomendações de serviços—economizando tempo e aumentando as vendas! 🔹 Principais Funcionalidades: ✅ Atendimento automático no WhatsApp – IA responde clientes instantaneamente. 💬 ✅ Agendamento de serviços automatizado – Clientes marcam banho, tosa ou consultas facilmente. 📅✂️ ✅ Lembretes personalizados – Reduza faltas com notificações automáticas. 📢🐾 ✅ Gestão de clientes e histórico de serviços – Controle dados de forma eficiente. 📊📁 ✅ Sugestão de produtos e serviços – Venda mais com recomendações inteligentes. 🎁🐶 📌 Como Funciona 1️⃣ O fluxo recebe perguntas dos clientes via WhatsApp. 2️⃣ A IA processa os pedidos e fornece opções de agendamento. 3️⃣ O cliente escolhe o serviço desejado e agenda em segundos. 4️⃣ Lembretes automáticos garantem que os clientes não esqueçam os horários. 5️⃣ O histórico do cliente é salvo para oferecer um atendimento mais personalizado. ⚙️ Configuração e Personalização 🔧 Conecte sua API do WhatsApp (evolution) para interação automática. 🔧 Integre ao Google Calendar para gerenciar agendamentos. 🔧 Personalize valores, serviços e regras de envio de lembretes conforme sua necessidade. 💡 Automatize processos, melhore a experiência do cliente e escale seu pet shop com IA! 🚀

How can you find your target market if you don't know what your product is. This simple philosophy changes the way we think about automated sales agents. Context changes everything. In this 4-part workflow, we start by creating a knowledge base that will act as context across the workflow. This context will guide and provide our AI Agents across the workflow to locate better leads and perform market research based on what the product actually offers. Use Case: Lead generation for Product-based Sales Tech Required Neon DB**: For storing Research and Lead Data. You can use Google sheets but it has a rate limiting problem. Google Serper**: As a web search tool for our AI. Google Drive**: For storing our knowledge base documents. Pinecone**: Vector DB for converting our knowledge base into context for AI. Hunter.io**: For finding emails for outreach. Email Client**: An email client, maybe gmail or anything that can send an email on your behalf. Gemini**: Our trusty AI LLM. Good to know All of the tools that I use in this workflow are either free or have an extremely generous free-tier. How it works We start by converting our knowledge base into context for AI. Take in the documents from Google drive and convert it into embeddings and store them in a vector store like Pinecone. This needs to be only run once, or whenever you have a new document in your knowledge base. Then we pass this context to an AI agent and tell it to generate search queries for locating companies that actually need my services. Then for each company that we've located, we determine the company staff that we need to reach out to for selling our product. This will be done by a combination of Google Serper and Hunter.io Once we have the list of employees and their emails, we start creating personalized emails based on the data we've collected for each of the employee and send them outreach emails.

How It Works This workflow automates end-to-end carbon emissions monitoring, strategy optimisation, and ESG reporting using a multi-agent AI supervisor architecture in n8n. Designed for sustainability managers, ESG teams, and operations leads, it eliminates the manual effort of tracking emissions, evaluating reduction strategies, and producing compliance reports. Data enters via scheduled pulls and real-time webhooks, then merges into a unified feed processed by a Carbon Supervisor Agent. Sub-agents handle monitoring, optimisation, policy enforcement, and ESG reporting. Approved strategies are auto-executed or routed for human sign-off. Outputs are consolidated and pushed to Slack, Google Sheets, and email, keeping all stakeholders informed. The workflow closes the loop from raw sensor data to actionable ESG dashboards with minimal human intervention. Setup Steps Connect scheduled trigger and webhook nodes to your emissions data sources. Add credentials for Slack (bot token), Gmail (OAuth2), and Google Sheets (service account). Configure the Carbon Supervisor Agent with your preferred LLM (OpenAI or compatible). Set approval thresholds in the Check Approval Required node. Map Google Sheets document ID for ESG report and KPI dashboard nodes. Prerequisites OpenAI or compatible LLM API key Slack bot token Gmail OAuth2 credentials Google Sheets service account Use Cases Corporate sustainability teams automating monthly ESG reporting Customisation Swap LLM models per agent for cost or accuracy trade-offs Benefits Eliminates manual emissions data aggregation and report generation

How It Works This workflow automates end-to-end carbon emissions monitoring, strategy optimisation, and ESG reporting using a multi-agent AI supervisor architecture in n8n. Designed for sustainability managers, ESG teams, and operations leads, it eliminates the manual effort of tracking emissions, evaluating reduction strategies, and producing compliance reports. Data enters via scheduled pulls and real-time webhooks, then merges into a unified feed processed by a Carbon Supervisor Agent. Sub-agents handle monitoring, optimisation, policy enforcement, and ESG reporting. Approved strategies are auto-executed or routed for human sign-off. Outputs are consolidated and pushed to Slack, Google Sheets, and email, keeping all stakeholders informed. The workflow closes the loop from raw sensor data to actionable ESG dashboards with minimal human intervention. Setup Steps Connect scheduled trigger and webhook nodes to your emissions data sources. Add credentials for Slack (bot token), Gmail (OAuth2), and Google Sheets (service account). Configure the Carbon Supervisor Agent with your preferred LLM (OpenAI or compatible). Set approval thresholds in the Check Approval Required node. Map Google Sheets document ID for ESG report and KPI dashboard nodes. Prerequisites OpenAI or compatible LLM API key Slack bot token Gmail OAuth2 credentials Google Sheets service account Use Cases Corporate sustainability teams automating monthly ESG reporting Customisation Swap LLM models per agent for cost or accuracy trade-offs Benefits Eliminates manual emissions data aggregation and report generation

How It Works This workflow automates end-to-end carbon emissions monitoring, strategy optimisation, and ESG reporting using a multi-agent AI supervisor architecture in n8n. Designed for sustainability managers, ESG teams, and operations leads, it eliminates the manual effort of tracking emissions, evaluating reduction strategies, and producing compliance reports. Data enters via scheduled pulls and real-time webhooks, then merges into a unified feed processed by a Carbon Supervisor Agent. Sub-agents handle monitoring, optimisation, policy enforcement, and ESG reporting. Approved strategies are auto-executed or routed for human sign-off. Outputs are consolidated and pushed to Slack, Google Sheets, and email, keeping all stakeholders informed. The workflow closes the loop from raw sensor data to actionable ESG dashboards with minimal human intervention. Setup Steps Connect scheduled trigger and webhook nodes to your emissions data sources. Add credentials for Slack (bot token), Gmail (OAuth2), and Google Sheets (service account). Configure the Carbon Supervisor Agent with your preferred LLM (OpenAI or compatible). Set approval thresholds in the Check Approval Required node. Map Google Sheets document ID for ESG report and KPI dashboard nodes. Prerequisites OpenAI or compatible LLM API key Slack bot token Gmail OAuth2 credentials Google Sheets service account Use Cases Corporate sustainability teams automating monthly ESG reporting Customisation Swap LLM models per agent for cost or accuracy trade-offs Benefits Eliminates manual emissions data aggregation and report generation

How It Works This workflow automates end-to-end carbon emissions monitoring, strategy optimisation, and ESG reporting using a multi-agent AI supervisor architecture in n8n. Designed for sustainability managers, ESG teams, and operations leads, it eliminates the manual effort of tracking emissions, evaluating reduction strategies, and producing compliance reports. Data enters via scheduled pulls and real-time webhooks, then merges into a unified feed processed by a Carbon Supervisor Agent. Sub-agents handle monitoring, optimisation, policy enforcement, and ESG reporting. Approved strategies are auto-executed or routed for human sign-off. Outputs are consolidated and pushed to Slack, Google Sheets, and email, keeping all stakeholders informed. The workflow closes the loop from raw sensor data to actionable ESG dashboards with minimal human intervention. Setup Steps Connect scheduled trigger and webhook nodes to your emissions data sources. Add credentials for Slack (bot token), Gmail (OAuth2), and Google Sheets (service account). Configure the Carbon Supervisor Agent with your preferred LLM (OpenAI or compatible). Set approval thresholds in the Check Approval Required node. Map Google Sheets document ID for ESG report and KPI dashboard nodes. Prerequisites OpenAI or compatible LLM API key Slack bot token Gmail OAuth2 credentials Google Sheets service account Use Cases Corporate sustainability teams automating monthly ESG reporting Customisation Swap LLM models per agent for cost or accuracy trade-offs Benefits Eliminates manual emissions data aggregation and report generation

This workflow automatically monitors and reports data quality for any SQL table using configurable checks and thresholds. It evaluates key metrics—including null values, duplicate records, row count anomalies, and outliers—and assigns a clear PASS, WARN, or FAIL status. Designed for efficiency, the workflow dynamically injects table and column names from a central Config node, so you don’t need to edit SQL queries manually. All checks run in parallel, and results are consolidated into a structured HTML report with clear status indicators. The report is automatically sent via email and logged into Google Sheets for historical tracking, auditing, and trend analysis. ⚙️ Setup Update the Config node with your table name, column names, thresholds, and email recipient. Connect your database credentials (Postgres/MySQL) in all query nodes. Set up Gmail or SMTP credentials in the email node. Connect your Google Sheets account and ensure required columns exist. Activate the workflow (runs daily by default, can be customized). This workflow is ideal for data analysts and analytics engineers who want a lightweight, automated solution to proactively monitor data quality without exporting large datasets or building complex pipelines.

AI SQL Analytics Agent Chat With Any PostgreSQL Database Using Natural Language This template turns your PostgreSQL database into an AI-powered analytics assistant. Instead of exporting data or uploading huge CSV files to an LLM, this workflow lets AI query your database directly using SQL, returning clean insights instantly. Just type your table name once and start asking questions in plain English. No SQL required.

Overview This workflow automates CSV data processing from upload to database insertion. It accepts CSV files via webhook, uses AI to detect schema and standardize columns, cleans and validates the data, and stores it in Postgres. Errors are logged separately, and notifications are sent for visibility. How It Works CSV Upload A webhook receives CSV files for processing. Validation The workflow checks if the uploaded file is a valid CSV format. Invalid files are rejected with an error report. Data Extraction The CSV is parsed into structured rows for further processing. Schema Detection AI analyzes the data to: Infer column types Normalize column names Detect inconsistencies Data Normalization Values are cleaned and converted into proper formats (numbers, dates, booleans), with optional unit standardization. Data Quality Validation The workflow checks: Type mismatches Missing values Statistical outliers Conditional Processing Clean data → prepared and inserted into Postgres Errors → detailed report generated Database Insert Valid data is stored in the configured Postgres table. Error Logging Errors are logged into Google Sheets for tracking and debugging. Notifications A Slack message is sent with processing results. Setup Instructions Configure the webhook endpoint for CSV uploads Set your Postgres table name in the configuration node Add Anthropic/OpenAI credentials for schema detection Connect Slack for notifications Connect Google Sheets for error logging Configure error threshold settings Test with sample CSV files Activate the workflow Use Cases Cleaning and standardizing messy CSV data Automating ETL pipelines Preparing data for analytics or dashboards Validating incoming data before database storage Monitoring data quality with error reporting Requirements n8n instance with webhook access Postgres database OpenAI or Anthropic API access Slack workspace Google Sheets account Notes You can customize schema rules and normalization logic in the Code node. Adjust error thresholds based on your data tolerance. Extend validation rules for domain-specific requirements. Replace Postgres or Sheets with other storage systems if needed.

Automate intelligent customer support responses with AI and Slack How it works Receive request via webhook with customer question Analyze sentiment and detect urgency using JavaScript Send urgent alerts to Slack for critical cases Search knowledge base and fetch conversation history from PostgreSQL Generate AI response with context-aware prompts Route intelligently: Auto-respond via email OR escalate to Slack Log everything to Google Sheets and PostgreSQL for analytics Setup steps Slack webhooks: Replace YOUR_URGENT_WEBHOOK and YOUR_ESCALATION_WEBHOOK with your webhook URLs Google Sheets: Replace YOUR_SPREADSHEET_ID with your spreadsheet ID and authenticate Email: Configure SMTP/Gmail credentials in the email node PostgreSQL (optional): Create support_conversations table or disable DB nodes Production: Replace mock AI nodes with OpenAI/Anthropic API nodes Key features Multi-language support (Japanese & English) Sentiment analysis with urgency detection Smart escalation routing Real-time Slack notifications Comprehensive analytics logging

How it works This workflow automatically detects completed orders in PostgreSQL and prepares them for AI-based post-purchase communication. It enriches each order with customer, product, and payment data, then generates a personalized message using an AI agent. The message is delivered via email and WhatsApp and finally logged in Google Sheets for tracking and auditing. Step-by-step Step 1: Fetch and prepare completed orders for AI processing** Postgres Trigger – Watches the orders table for updates and initiates the workflow. Postgres (Execute query) – Fetches only orders marked as completed. Split In Batches – Loops through completed orders safely and sequentially. Postgres (Execute query) – Retrieves full customer, product, and payment details using joins. AI Agent – Generates a personalized post-purchase message using order data. Groq Chat Model – Supplies the language model used by the AI agent. Merge – Combines AI-generated text with database results for downstream use. Step 2: Deliver messages and log post-purchase communication** Code – Formats AI output into clean email and WhatsApp message templates. Gmail – Sends the post-purchase email to the customer. WhatsApp – Sends the same message via WhatsApp. Set – Flags email and WhatsApp messages as successfully sent. Google Sheets – Appends customer, order, and communication details. Wait – Pauses before continuing to process the next completed order. Why use this? Automates post-purchase communication with zero manual effort. Ensures consistent, personalized messaging across email and WhatsApp. Adapts message tone automatically based on payment status. Creates a centralized audit log in Google Sheets. Scales easily as order volume grows.

How It Works This workflow automates customer feedback processing by analyzing sentiment, identifying key issues, generating personalized responses, and escalating critical cases to support teams when required. Designed for customer success managers, support teams, and product managers, it enables scalable feedback handling without compromising response quality or urgency. The workflow eliminates manual triage and response drafting by normalizing incoming feedback, performing sentiment and topic analysis, generating context-aware AI responses, validating tone and intent, escalating high-risk or negative feedback, logging all interactions for traceability, and delivering automated replies via email. Setup Steps Configure webhook trigger URL for feedback form integration or email parsing Add OpenAI API key for sentiment analysis and response generation Connect Anthropic Claude API for alternative response generation and validation Set up Google Sheets integration for feedback logging and analytics tracking Configure Gmail OAuth2 credentials for automated customer response delivery Integrate support ticket system (Zendesk, Freshdesk) for escalation routing Prerequisites OpenAI API key, Anthropic Claude API key (optional), Google Workspace account (Sheets, Gmail) Use Cases Product feedback management, customer support automation Customization Adjust sentiment scoring thresholds per industry standards, modify response templates Benefits Responds to feedback 95% faster, maintains consistent response quality across all interactions

How it works This workflow monitors customer health by combining payment behavior, complaint signals, and AI-driven feedback analysis. It runs on daily and weekly schedules to evaluate risk levels, escalate high-risk customers, and generate structured product insights. High-risk cases are notified instantly, while detailed feedback and audit logs are stored for long-term analysis. Step-by-step Step 1: Triggers & mode selection** Daily Risk Check Trigger – Starts the workflow on a daily schedule. Weekly schedule1 – Triggers the workflow for weekly summary runs. Edit Fields3 – Sets flags for daily execution. Edit Fields2 – Sets flags for weekly execution. Switch1 – Routes execution based on daily or weekly mode. Step 2: Risk evaluation & escalation** Fetch Customer Risk Data – Pulls customer, payment, product, and complaint data from PostgreSQL. Is High Risk Customer? – Evaluates payment status and complaint count. Prepare Escalation Summary For Low Risk User – Assigns low-risk status and no-action details. Prepare Escalation Summary For High Risk User – Assigns high-risk status and escalation actions. Merge Risk Result – Combines low-risk and high-risk customer records. Send a message4 – Sends the customer risk summary via Gmail. Send a message5 – Sends the same risk summary to Discord. Code in JavaScript3 – Appends notification status and timestamps. Append or update row in sheet3 – Logs risk evaluations and notification status in Google Sheets. Step 3: AI feedback & reporting** Get row(s) in sheet1 – Fetches customer records for feedback analysis. Loop Over Items1 – Processes customers one by one. Prompt For Model1 – Builds a structured prompt for product feedback analysis. HTTP Request1 – Sends data to the AI model for insight generation. Code in JavaScript – Merges AI feedback with original customer data. Append or update row in sheet – Stores AI-generated feedback in Google Sheets. Wait1 – Controls execution pacing between records. Merge1 – Prepares consolidated feedback data. Send a message1 – Emails the final AI-powered feedback report. Why use this? Detect customer churn risk early using payment and complaint signals Automatically escalate high-risk customers without manual monitoring Convert raw customer issues into executive-ready product insights Keep a complete audit trail of risk, feedback, and notifications Align support, product, and leadership teams with shared visibility

Monitor employee stress levels from Slack and tasks to Google Sheets This workflow functions as an automated "Chief Wellness Officer," helping HR teams and managers prevent employee burnout before it happens. It aggregates data from communication channels and work tools to provide an AI-driven daily assessment of employee well-being, while prioritizing privacy through data anonymization. Who is this for HR & People Operations Teams** wanting to track organizational health trends without invading personal privacy. Remote-first Companies** where physical cues of burnout are harder to spot. Team Managers** looking for data-backed insights to support their direct reports. What it does Daily Trigger: Runs automatically every morning (default: 2 AM) to analyze the previous day's activity. Data Collection: Fetches public Slack messages to analyze communication tone. Retrieves attendance data (work hours, late arrivals) and task completion rates via API. AI Analysis: Uses OpenAI to process the data, predicting a "Stress Level" based on sentiment and workload metrics. Privacy-First Reporting: Hashes employee IDs to anonymize data, then logs stress scores to Google Sheets for trend analysis. Direct Intervention: If the AI detects "High" stress, it sends a private, empathetic DM to the employee on Slack offering support or counseling resources. Requirements n8n** (Self-hosted or Cloud) Slack** Workspace (with a Bot User OAuth Token). OpenAI** API Key. Google Sheets** (for the dashboard). Postgres** (optional, for logging counseling actions). HR/Task Management Tools** (e.g., Jira, Asana, BambooHR) accessible via API. How to set up Configure Credentials: Set up your credentials for Slack, OpenAI, Google Sheets, and Postgres in n8n. Prepare Google Sheet: Create a sheet with headers: employee_hash, department, stress_score, stress_level, and analysis_date. Connect Data Sources: The workflow uses HTTP Request nodes as placeholders for Attendance and Task data. You must update these URLs to point to your specific HRIS or Project Management tool APIs (or replace them with native n8n nodes like Asana or Jira). Update Slack Settings: Ensure the Slack node has the correct permissions (channels:history, chat:write, users:read) and target channel/user mapping. How to customize Adjust Sensitivity:* Modify the system prompt in the *AI Stress Level Prediction node to change how "High" stress is defined. Change Data Sources:* Replace the generic HTTP Request nodes with specific n8n nodes for services like Harvest, *Jira, or Trello depending on your stack. Modify Action:** Instead of a direct DM, you can change the final step to alert a manager or create a ticket in a sensitive HR service desk.

Predict churn risk from customer data and send retention emails via OpenAI 👥 Who’s it for This workflow is designed for Customer Success Managers, Growth Teams, and SaaS Business Owners who want to proactively reduce churn using AI. It automates the analysis of customer health and the delivery of personalized retention offers without manual intervention. 🚀 What it does This template acts as an intelligent retention system that connects your data, AI, and communication channels. Aggregates Data: Pulls customer profiles from your CRM, support ticket history via API, and product usage logs from PostgreSQL. Predicts Risk: Uses OpenAI to analyze the combined data and calculate a "Churn Risk Score" for each customer. Automates Action: For customers identified as high-risk (score > 0.7): Generates a unique, dynamic discount coupon via Stripe. Drafts a highly personalized retention email using OpenAI. Sends the email via Gmail. Tracks Effectiveness: Logs all actions to Google Sheets. It also checks back (via SendGrid and CRM data) to track email opens and verify if the customer was retained after 30 days. ⚙️ How to set up Prepare Google Sheet: Create a sheet with columns for customer_id, risk_score, offer_type, email_status, and retention_result. Configure Credentials: Set up your credentials for OpenAI, Stripe, Gmail, Google Sheets, SendGrid, and PostgreSQL. API Endpoints: Update the HTTP Request nodes to point to your specific CRM and Support tool APIs (replace the placeholder URLs). Customize Logic: In the Postgres node, adjust the SQL query to match your product's event table. In the Code node (Offer Decision), define your rules for discounts (e.g., "Give 20% off if MRR > $10k"). Activate: The workflow is set to run daily at 3:00 AM. Toggle the Schedule Trigger to Active when ready. 📦 Requirements n8n** (v1.0 or later) OpenAI** API Key Google Workspace** (Gmail, Sheets) Stripe** Account (for coupon generation) SendGrid** Account (for email tracking) PostgreSQL** (or similar database) Access to CRM and Support Tool APIs 🎨 How to customize the workflow Adjust the AI Prompt:* Edit the OpenAI node* system message to match your brand's tone of voice. Change the Threshold:* Modify the If node* to target customers with a risk score higher or lower than 0.7. Internal Alerts:* Instead of emailing the customer directly, replace the Gmail node with a Slack node* to notify your CSM team to reach out manually. Swap Database:** If you use MySQL or Snowflake, simply replace the PostgreSQL node with the corresponding n8n node.

How It Works Automates sales data analysis and strategic insight generation for sales managers and strategists needing actionable intelligence. Fetches multi-source data from sales, marketing, and financial systems, validates data quality to prevent errors, applies advanced AI analysis via OpenAI to identify market trends and patterns, calculates comprehensive KPIs for performance measurement, generates prioritized recommendations, and automatically distributes detailed insights via Gmail alerts and Google Sheets dashboards—eliminating time-consuming manual analysis overhead. Setup Instructions OpenAI API: Add key via credentials Gmail: Authorize account for email delivery Google Sheets: Connect for dashboard logging Schedule: Set monthly trigger timing Modify data sources: Replace fetch nodes with your APIs Prerequisites OpenAI API key, Gmail account with send permissions, Google Sheets access, n8n instance, source data APIs (sales, marketing, financial systems). Use Cases E-commerce platforms analyzing sales trends; SaaS companies generating strategy reports; multi-channel retailers routing recommendations; Customization Add data sources via fetch nodes; swap OpenAI for Claude or Gemini; modify routing logic for different priority thresholds; Benefits Reduces analysis time from hours to minutes. Eliminates manual report crea

How It Works Scheduled processes retrieve customer feedback from multiple channels. The system performs sentiment analysis to classify tone, then uses OpenAI models to extract themes, topics, and urgency indicators. All processed results are stored in a centralized database for trend tracking. Automated rules identify high-risk or negative sentiment items and trigger alerts to the relevant teams. Dashboards and workflow automation then visualize insights and support follow-up actions. Setup Instructions Data Sources: Connect social media APIs, survey tools, and customer support platforms. AI Analysis: Configure the OpenAI API with sentiment and theme-extraction prompts. Database: Set up a feedback storage schema in your utility database. Alerts: Configure email notifications and CRM triggers for priority issues. Dashboards: Link your analytics and reporting tools for real-time insights. Prerequisites Social media/survey API credentials; OpenAI API key; database access; CRM system credentials; email notification setup Use Cases Customer sentiment tracking; product feedback aggregation; support ticket prioritization; brand monitoring; trend identification Customization Adjust sentiment thresholds; add new feedback sources; modify categorization rules Benefits Reduces analysis time 85%; captures actionable insights; enables rapid response to issues

How It Works Every day at 8 AM, the workflow automatically retrieves the latest F1 data—including driver standings, qualifying results, race schedules, and circuit information. All sources are merged into a unified dataset, and driver performance metrics are computed using historical trends. An AI agent, enhanced with vectorized race history, evaluates patterns and generates race-winner predictions. When the confidence score exceeds the defined threshold, the system pushes an automated Slack alert and records the full analysis in the database and Google Sheets. Setup Steps Update the workflow configuration with: newsApiUrl, weatherApiUrl, historicalYears, and confidenceThreshold. Connect PostgreSQL using the schema: prediction_date, predicted_winner, confidence_score, prediction_source, data_version, full_analysis. Provide the Slack channel ID for sending high-confidence alerts. Specify the Google Sheets document ID and sheet name for prediction logging. Test connectivity to the Ergast API (no authentication required). Prerequisites OpenAI account (GPT-4o access), Slack workspace admin access, PostgreSQL instance, Google Sheets account, n8n instance with LangChain community nodes enabled. Customization Extend by adding constructor predictions (modify AI prompt). Integrate Discord or Teams instead of Slack. Benefits Saves time by automating data collection, improves accuracy using multiple performance metrics and historical patterns.

How it works This workflow consolidates data from five different systems — Google Sheets, PostgreSQL, MongoDB, Microsoft SQL Server, and Google Analytics — into a single master Google Sheet. It runs on a scheduled trigger three times a week. Each dataset is tagged with a unique source identifier before merging, ensuring data traceability. Finally, the merged dataset is cleaned, standardized, and written into the output Google Sheet for reporting and analysis. Step-by-step Trigger the workflow Schedule Trigger** – Runs the workflow at set weekly intervals. Collect data from sources Google Sheets Source** – Retrieves records from a specific sheet. PostgreSQL Source** – Extracts customer data from the database. MongoDB Source** – Pulls documents from the defined collection. Microsoft SQL Server** – Executes a SQL query and returns results. Google Analytics** – Captures user activity and engagement metrics. Tag each dataset Add Sheets Source ID** – Marks data from Google Sheets. Add PostgreSQL Source ID** – Marks data from PostgreSQL. Add MongoDB Source ID** – Marks data from MongoDB. Add SQL Server Source ID** – Marks data from SQL Server. Add Analytics Source ID** – Marks data from Google Analytics. Merge and process Merge** – Combines all tagged datasets into a single structure. Process Merged Data** – Cleans, aligns schemas, and standardizes key fields. Store consolidated output Final Google Sheet** – Appends or updates the master sheet with the processed data. Why use this? Centralizes multiple data sources into a single, consistent dataset. Ensures data traceability by tagging each source. Reduces manual effort in data cleaning and consolidation. Provides a reliable reporting hub for business analysis. Enables scheduled, automated updates for up-to-date visibility.

Overview of the n8n Workflow This n8n workflow automates the transformation of spreadsheet data into professional charts and graphs using AI-driven analysis. Triggered via Slack, it processes uploaded files (Excel, CSV, Google Sheets, or Drive links), interprets the data with an AI agent to determine the best visualization type (e.g., bar, line, pie, doughnut, or bubble charts), generates images via QuickChart, uploads them to Google Drive, and delivers the results back to the user in Slack with titles and shareable links. It maintains conversation context in Postgres for seamless multi-turn interactions and handles audio or text inputs for chart requests. What this workflow does: Hooks up to Slack for seamless spreadsheet uploads Automatically extracts your data and generates bar charts, line graphs, bubble charts, and more Delivers stunning visualizations straight back to you in Slack Makes it easy to spot trends, patterns, and insights—on demand How it Works • Users upload spreadsheets or share links via Slack, along with a natural language request (e.g., "Create a bar chart of sales by month"). • The workflow detects file types, extracts and aggregates data, then uses an AI agent to parse the request and select an appropriate chart type. • Data is formatted and sent to QuickChart's API to generate the visualization image. • Images are uploaded to Google Drive, and a confirmation message with titles and links is sent back to Slack. Set Up Steps Setup takes about 15-30 minutes, mainly for credential configuration. Detailed node instructions are in the workflow's sticky notes—focus on pasting API keys and testing triggers. Once cloned, the workflow runs plug-and-play; only tweak credentials and optional prompts as needed. ⚙️ Turn Spreadsheets Into Charts & Graphics Automate turning uploaded spreadsheets into AI-generated charts (bar, line, pie, etc.) via Slack, with results shared as Google Drive links. 🧑‍💻 Author: LeeWei 🚀 Steps to Connect: Slack Bot Setup Create a Slack app at api.slack.com/apps and add scopes for chat:write, files:read, channels:read. Generate a Bot User OAuth Token and paste it into the Slack Trigger node's credentials in n8n. Invite the bot to your desired channel for file uploads and messages. OpenAI API Key Sign up at platform.openai.com and generate an API key. Paste this key into the OpenAI (gpt-4o-mini) node's credentials. 💡 For cost efficiency, monitor usage—basic charts use minimal tokens. Postgres Database Connection Set up a Postgres instance (e.g., via Supabase or your host) with a table named n8n_rodger_chat for chat history. Add connection details (host, database, user, password) to the Postgres nodes' credentials. This enables thread memory; skip if not using multi-turn chats (but recommended for context). Google Sheets & Drive Setup Create OAuth2 credentials at console.cloud.google.com with scopes for Sheets (read) and Drive (upload, share). Paste the credentials into the Google Sheets and implied Drive upload nodes. Test by sharing a sample sheet—ensures data extraction and image storage work. QuickChart Integration No API key needed for free tier (up to 500 charts/month); visit quickchart.io to confirm. For production (100k+ charts/month), upgrade to corporate plan ($40/month) and add any auth if required in the HTTP Request node. The node is pre-configured for chart generation—edit URL params only for custom styling. Plug and Play Instructions Clone the workflow JSON directly into n8n—all nodes (triggers, AI agents, extractors, switches) are pre-wired and ready. No re-setup needed beyond the steps above. Key editable fields (found in sticky notes): AI Agent Node: System Prompt** Customize the chart interpretation (default: auto-selects bar/line/pie/etc. based on data). Example: Change to prioritize "scatter plots for correlations" if needed. Switch Nodes (File Detection)** Add rules for new formats (e.g., .ods for OpenDocument) in the conditions for XLS/XLSX/CSV/Sheets/Drive. HTTP Request Node (QuickChart)** Tweak chart params like width=800&height=600 for size, or colors via ?chart=... for branding. Let User Know Upload Complete: Text** Adjust the confirmation message template for tone (e.g., add emojis or custom phrasing). Test with a sample Slack message: "Make a line graph from this sales CSV over months." Results appear instantly with links. Potential Customizations Add Chart Types**: Duplicate a chart branch (e.g., Line Graph) and integrate new QuickChart endpoints for scatter or funnel charts. Switch AI Provider**: Replace OpenAI with OpenRouter in the Chat Model node for alternative LLMs. Batch Size**: Edit the Loop Over Items node's batch for larger datasets (default handles small files efficiently). Error Handling**: Add IF nodes post-extraction to notify on invalid data. Considerations and Improvements Rate Limits**: QuickChart free tier suits testing; scale to paid for heavy use. OpenAI tokens add up for complex data. File Limits**: Supports up to ~10MB uploads; for larger, preprocess externally. Privacy**: Data passes through OpenAI—review for sensitive info. Enhancements**: Integrate image OCR for scanned tables, or export to PDF for reports. This workflow streamlines data viz without coding, perfect for teams analyzing trends on the fly. Questions? Drop a Slack message in your bot channel!

🏥 PATIENT APPOINTMENT REMINDER SYSTEM MULTI-DATABASE SUPPORT PURPOSE: Automatically send professional appointment reminders via email and SMS to reduce no-shows and improve patient experience. FEATURES: ✅ Dual reminders (3-day + 1-day before) ✅ Email + SMS notifications ✅ Phone number auto-formatting ✅ Timezone handling ✅ Professional HTML email templates ✅ Multi-database support: • PostgreSQL (Enterprise) • Google Sheets (Easy) • Airtable (Recommended) ✅ Flexible webhook support BUSINESS IMPACT: 📈 Reduce no-shows by 30-50% 💰 Increase revenue from better attendance ⏰ Save staff time on manual reminders 📱 Professional patient communication SETUP: Activate ONLY the database nodes your client needs!

How it works Regulatory monitoring**: Continuously tracks changes in laws, regulations, and compliance requirements across multiple jurisdictions Contract analysis**: AI-powered review of existing contracts to identify compliance gaps and risks Automated alerts**: Real-time notifications when regulatory changes affect your contracts or business operations Compliance reporting**: Generates audit-ready reports and documentation for regulatory compliance Set up steps Legal databases**: Connect to legal research platforms (Westlaw, LexisNexis, EUR-Lex) Contract repository**: Integrate with your contract management system or document storage Regulatory feeds**: Configure government and regulatory body RSS feeds and APIs AI legal analysis**: Set up OpenAI or specialized legal AI for contract analysis Compliance calendar**: Integrate with calendar systems for deadline tracking Audit trail**: Configure logging and documentation systems for compliance records Key Features 🔍 Multi-jurisdiction monitoring**: Tracks regulatory changes across different countries and regions 📊 Risk assessment**: Automatically scores compliance risks and potential impact ⚡ Real-time alerts**: Instant notifications when regulations affecting your business change 📋 Gap analysis**: Identifies discrepancies between current contracts and new requirements 🤖 AI-powered analysis**: Uses natural language processing to understand legal text 📈 Compliance dashboard**: Visual overview of compliance status across all contracts 🔄 Automated remediation**: Suggests contract amendments and compliance actions 📱 Mobile notifications**: Critical compliance alerts on mobile devices Compliance areas monitored Data protection**: GDPR, CCPA, and other privacy regulations Financial services**: Banking regulations, securities law, anti-money laundering Healthcare**: HIPAA, medical device regulations, pharmaceutical compliance Employment law**: Labor regulations, workplace safety, discrimination laws Environmental**: ESG requirements, environmental protection regulations Industry-specific**: Sector-specific regulations and standards Contract types supported Vendor agreements**: Supplier contracts and service agreements Employment contracts**: Employee agreements and contractor terms Data processing agreements**: Privacy and data handling contracts Customer agreements**: Terms of service and customer contracts Partnership agreements**: Joint ventures and strategic partnerships Licensing agreements**: Software licenses and intellectual property Automated responses Low risk (0-30)**: Routine monitoring and documentation Medium risk (31-60)**: Enhanced review and stakeholder notification High risk (61-80)**: Immediate legal review and action planning Critical risk (81-100)**: Emergency legal intervention and compliance measures Integration capabilities Legal research**: Westlaw, LexisNexis, Bloomberg Law Document management**: SharePoint, Google Drive, Dropbox Contract systems**: DocuSign, PandaDoc, ContractWorks Communication tools**: Slack, Teams, email for legal team alerts Calendar systems**: Outlook, Google Calendar for compliance deadlines This workflow ensures continuous legal compliance by monitoring regulatory changes and automatically assessing their impact on your contracts and business operations.

How it works Continuous monitoring**: Real-time surveillance of supplier performance, financial health, and operational status Risk scoring**: AI-powered assessment of supplier risks across multiple dimensions (financial, operational, geopolitical) Automated alerts**: Instant notifications when supplier risk levels exceed predefined thresholds Contingency activation**: Automatic triggering of backup suppliers and alternative sourcing plans Set up steps Supplier database**: Connect your ERP/procurement system with complete supplier information Financial data sources**: Integrate with credit monitoring services (Dun & Bradstreet, Experian) News monitoring**: Configure news APIs for real-time supplier-related news tracking Performance metrics**: Set up KPIs tracking (delivery times, quality scores, compliance) Alert systems**: Configure Slack, Teams, or email notifications for risk alerts Backup protocols**: Define alternative supplier activation procedures Key Features 🔍 360° supplier visibility**: Complete view of supplier ecosystem health and performance ⚡ Real-time risk detection**: Immediate identification of potential supply chain disruptions 📊 Predictive analytics**: Forecasting potential supplier issues before they impact operations 🚨 Automated escalation**: Risk-based alert system with appropriate stakeholder notifications 📈 Performance benchmarking**: Continuous comparison against industry standards and peers 🔄 Contingency management**: Automated backup supplier activation and procurement rerouting 🌍 Geopolitical monitoring**: Tracking of regulatory changes and political risks by region 💰 Cost impact analysis**: Financial impact assessment of supplier disruptions Risk categories monitored Financial stability**: Credit scores, payment delays, bankruptcy indicators Operational performance**: Delivery reliability, quality metrics, capacity utilization Compliance status**: Regulatory adherence, certifications, audit results Geopolitical risks**: Political instability, trade restrictions, regulatory changes Environmental factors**: Natural disasters, climate risks, sustainability metrics Cyber security**: Security breaches, data protection compliance Automated responses Low risk (0-30)**: Routine monitoring and performance tracking Medium risk (31-60)**: Enhanced monitoring with supplier engagement High risk (61-80)**: Immediate supplier contact and mitigation planning Critical risk (81-100)**: Emergency protocols and backup supplier activation Integration capabilities ERP systems**: SAP, Oracle, Microsoft Dynamics for procurement data Risk platforms**: Resilinc, Riskmethods, Prewave for specialized risk intelligence Financial services**: Credit monitoring and financial health assessment News APIs**: Real-time news monitoring and sentiment analysis Communication tools**: Slack, Teams, email for stakeholder notifications This workflow provides comprehensive supply chain visibility and proactive risk management, enabling companies to maintain operational continuity while minimizing disruption costs.

This comprehensive n8n workflow automates the entire travel business call management process, from initial customer inquiries to trip bookings and marketing outreach. The system handles incoming calls, validates trip details, processes bookings, captures leads, and manages outbound marketing campaigns to promote trip organizer services. It streamlines the complete sales cycle while maintaining organized data records for business intelligence. Essential Information The system operates across four distinct workflows to handle different aspects of travel call management. All call data is automatically captured and stored in organized spreadsheets for analysis and follow-up. The workflow validates trip details before processing to ensure data accuracy and prevent booking errors. Outbound marketing campaigns are automatically triggered based on lead detection and formatting. System Architecture Call Handling Pipeline**: The Detect Incoming Call node captures all incoming customer calls, followed by the Validate Trip Details node which verifies and processes trip information, and the Deliver Organizer Info node that provides relevant trip organizer details to callers. Booking Management Flow**: The Capture Voice Input node records customer booking requests, the Update Booking Record node processes and stores booking information, and the Send Booking Confirmation node delivers confirmation details to customers. Lead Generation Process**: The Detect New Lead node identifies potential customers from call data, the Format Lead Information node structures the lead data for marketing use, and the Initiate Marketing Outreach node launches targeted marketing campaigns. Data Management System**: The Receive Call Response node collects call interaction data, the Log User Input node records customer information in spreadsheets, and the Relay Response to System node ensures data synchronization across all components. Implementation Guide Import the workflow into n8n and configure phone system integration for call detection and voice capture. Set up spreadsheet connections for booking records, lead management, and call logging. Configure marketing automation tools for outbound campaign management. Test each workflow section independently before enabling the complete system. Monitor call handling accuracy and adjust validation rules as needed. Technical Dependencies Phone system API or telephony service for call detection and voice processing Spreadsheet service (Google Sheets, Excel Online) for data storage and management Marketing automation platform for outbound campaign execution Voice recognition service for capturing and processing customer input CRM integration for lead management and customer tracking Database & Sheet Structure Call Tracking Sheet**: Columns should include Call_ID, Customer_Phone, Call_Time, Call_Duration, Call_Status, Trip_Interest, Organizer_Assigned Booking Records Sheet**: Required columns are Booking_ID, Customer_Name, Customer_Phone, Destination, Travel_Dates, Group_Size, Booking_Status, Confirmation_Sent Lead Management Sheet**: Essential columns include Lead_ID, Customer_Name, Phone_Number, Email, Trip_Preference, Lead_Source, Lead_Status, Marketing_Campaign_Sent Trip Organizer Database**: Contains Organizer_ID, Organizer_Name, Specialization, Contact_Info, Availability_Status, Performance_Rating Marketing Outreach Log**: Tracks Campaign_ID, Lead_ID, Campaign_Type, Send_Date, Response_Status, Follow_up_Required Customization Possibilities Adjust the Validate Trip Details node to include specific travel validation rules or partner requirements. Modify the Format Lead Information node to match your CRM system's data structure and marketing campaign formats. Configure the Initiate Marketing Outreach node to integrate with your preferred marketing platforms and campaign templates. Customize the data logging structure in the Log User Input node to capture additional customer information or booking details. Add additional validation steps or approval workflows between booking capture and confirmation sending.

*Smartlead to HubSpot Performance Analytics A streamlined workflow to analyze your Smartlead performance metrics by tracking lifecycle stages in HubSpot and generating automated reports.* Who is this for? (Outbound) Automation Agencies, Sales and marketing teams using Smartlead for outreach campaigns who want to track their performance metrics and lead progression in HubSpot. What problem does this workflow solve? Manual tracking of lead performance across Smartlead and HubSpot is time-consuming and error-prone. This workflow automates performance reporting by connecting your Smartlead data with HubSpot lifecycle stages, providing clear insights into your outreach campaign effectiveness. What this workflow does Automatically pulls performance data from your Smartlead campaigns Cross-references contact status with HubSpot lifecycle stages Generates comprehensive performance reports in Google Sheets Provides customizable reporting schedules to match your team's needs Setup Requirements PostgreSQL Database Set up your PostgreSQL instance (includes $300 free GCP credits) Follow our step-by-step setup guide: Find a step-by-step guide here Google Account Integration Connect your Google Account to n8n Find the guide here Smartlead Configuration Connect your Smartlead instance: Detailed connection guide included in workflow How to customize this workflow Configure the Trigger node to adjust report frequency Modify the Google Sheets template to match your specific KPIs Customize HubSpot lifecycle stage mapping in the Function node Adjust PostgreSQL queries to track additional metrics Need assistance or have suggestions? lmk here

Sync your Google Sheets Data with your Postgres database table, requiring minimal adjustments. Follow these steps: Retrieve Data: Pull data from Google Sheets and PostgreSQL. Compare Datasets: Identify differences, focusing on new or updated entries. Update PostgreSQL: Apply changes to ensure both platforms mirror each other. Automate this process to regularly synchronize data. Before starting, grant necessary access to both Google Sheets and PostgreSQL, and specify the data details for synchronization. This streamlined workflow enhances data consistency across platforms. This example is a one-way synchronization from Google Sheets into your Postgres. With small adjustments, you can make it the other way around, or 2-way.

This template shows how to sync data from one service to another. Specifically, in this example we're saving a new qualified lead from a Postgres database to a Google Sheets file. Setup instructions are located inside the workflow template.