Workflow Templates

This workflow is the third of three. You can find the other workflkows here: Incident Response Workflow - Part 1 Incident Response Workflow - Part 2 Incident Response Workflow - Part 3 We have the following nodes in the workflow: Webhook node: This trigger node listens to the event when the Resolve button is clicked. PagerDuty node: This node changes the status of the incident report from Acknowledged to Resolved in PagerDuty. Jira Software node: This node moves the incident issue to Done. Mattermost node: This node publishes a message in the auxiliary channel mentioning that the incident has been marked as resolved in PagerDuty and Jira. Mattermost node: This node publishes a message in the specified Incidents channel that the incident has been resolved by the on-call team.

This n8n workflow dynamically generates a realistic sample dataset based on a single topic you provide. It uses OpenAI (via LangChain) and n8n’s built-in nodes to: Generate structured JSON data for 5 columns with 3–5 values each Flatten that data into a single text blob Infer meaningful column names via a second AI call Pivot, split, merge, and rename columns automatically Output a clean, labeled dataset ready for export or further processing ⚙️ Prerequisites OpenAI API Key Visit: https://platform.openai.com/account/api-keys Create a new key In n8n: Credentials → New → OpenAI API, paste key, name it “OpenAi account” LangChain nodes enabled in your n8n instance 🥇 Step 1: Set Up OpenAI Credential Go to OpenAI API Keys Create and copy your key In n8n: Credentials → New → OpenAI API → paste key as “OpenAi account” 🥈 Step 2: Manual Trigger Add Manual Trigger to start the workflow 🥉 Step 3: Set Topic Add a Set node named Set Topic to Search Field: Topic = n8n use cases (or any topic you choose) ✨ Step 4: Generate Structured Data LangChain Agent** node Generate Random Data Connect to OpenAI Chat Model1 and Tool: Inject Creativity1 System prompt: instruct AI to output 5 columns of realistic values in JSON 🔧 Step 5: Parse AI Output Structured Output Parser** to validate JSON 🔄 Step 6: Flatten Data Code** node Outpt all Data to One Field Joins all values into a comma-separated string for column naming 🧠 Step 7: Generate Column Names LangChain Agent** Generate Column Names Connect to OpenAI Chat Model2 Prompt: infer 5 column names from the string 🔢 Step 8: Pivot Names Row Code** node Pivot Column Names transforms array into { column1: name1, … } 🪓 Step 9: Split Columns 5 SplitOut nodes to break each array back into rows per column 🔗 Step 10: Merge Rows Merge** node Merge Columns together using combineByPosition 🏷️ Step 11: Rename Columns Set** node Rename Columns assigns the AI-generated names to each column 🔗 Step 12: Final Output Merge** Append Column Names combines data and header row 🏁 Done! You now have a fully AI-driven, labeled dataset generated from a single topic—no external services needed. Easily extend by adding a Google Sheets or HTTP node to export. 📬 Need Help or Want to Customize This? 📧 robert@ynteractive.com 🔗 LinkedIn

Github Deployer Agent Overview The Github Deployer Agent is an intelligent automation tool that integrates with Slack to streamline code deployment workflows. Powered by Anthropic's Claude 3.5 and Tavily for web search, it enables seamless, context-aware file pushes to a GitHub repository with minimal user input. Capabilities Accepts natural language via Slack Automatically pushes code to a default GitHub repository Uses Claude 3.5 for code generation and decision-making Leverages Tavily for real-time web search to enhance context Supports folder structure hints to ensure clean and organized repositories Required Connections To operate correctly, the following integrations must be in place: Slack API Token with permission to read messages and post responses GitHub Personal Access Token with repo write permissions Tavily API Key for external search functionality Claude 3.5 API Access via Anthropic Detailed configuration instructions are provided in the workflow Example Input From Slack, you can send messages like: "Generate a basic README.md for my Python project and store it in the root directory." Customising This Workflow You can tailor the workflow by: Modifying default folder paths or repository settings Integrate Jira node to use issue keys as default folder naming Add slack file upload option

Do you want to discover company-related information to enrich a signup process? This workflow enriches any company by name using the uProc Get Company by Name tool. This tool combines Google Maps and emails research on the internet to return results. You get no results if the company has no presence on Google Maps. You need to add your credentials (Email and API Key - real -) located at Integration section to n8n. You can replace node "Create Company Item" with any other supported service returning Company names and countries, like Hubspot, Google Sheets, MySQL, or Typeform. You can set up the uProc node with several parameters: country: the country name you want to use. name: the name of the company you need to locate. Every "uProc" node returns the next fields per every located company: name: Contains the company's given name. email: Contains the company's given email. cif: Contains company's cif number. address: Contains company's formatted address. city: Contains the city location of the company. state: Contains province location of the company. county: Contains state location of the company country: Contains country location of the company zipcode: Contains zipcode code of the company phone: Contains phone number of the company website: Contains website of the company latitude: Contains latitude of the company longitude: Contains longitude of the company Next, you can save results to a CRM or Google Sheets, and prepare returned email or phone to launch an email or telemarketing campaign.

Workflow Overview This workflow automatically fetches the latest "Ask HN: Who is hiring?" posts from Hacker News, extracts individual job listings, cleans the raw text, converts them into structured job listings using Google Gemini AI, and saves them into Airtable. Components It’s a full end-to-end automation system combining: Algolia API** for HN data Text cleaning** Gemini AI (via LangChain)** for parsing job descriptions Structured JSON extraction** Airtable integration** to store the final data 🎯 Use Cases Automatically build a job board from HN posts Track startup hiring trends Feed remote job alerts into a CRM or Slack Enrich a hiring intelligence database 🔧 Nodes & Services Used HTTP Request (Algolia + Firebase API) SplitOut, Set, Filter, Function, Limit Google Gemini (via LangChain integration) Output Parser Structured Airtable (API token required) 📌 Credentials Required Google Gemini (PaLM/Gemini API) Airtable Personal Access Token Algolia Application ID & API Key (via Header Auth) 📦 Tags hacker-news, jobs, airtable, ai, gemini, automation, hn, langchain, workflow Screenshots

How It Works This workflow automates multi-cloud billing analysis and FinOps reporting using a supervised multi-agent AI architecture. It targets cloud finance teams, FinOps practitioners, DevOps leads, and CTOs seeking continuous visibility into cloud spend, resource waste, and carbon impact. A daily trigger fetches billing exports via HTTP and parses CSV data. A central Multi-Cloud Optimisation supervisor agent then coordinates four specialised sub-agents: a Resource Utilisation Analyser that identifies idle and over-provisioned assets, a Cost Optimisation Agent that surfaces savings opportunities across providers, a Carbon Footprint Analysis Agent that quantifies emissions per workload, and a FinOps Narrative Generator that produces human-readable financial commentary. Shared tools including a Financial Calculator and Advanced Analytics Code Tool support cross-agent computation. Results are parsed through a Structured Output Parser and formatted into a final consolidated report for stakeholder distribution. Setup Steps Configure the HTTP GET node with your cloud provider. Connect OpenAI credentials to all four sub-agent model nodes. Link the Financial Calculator and Advanced Analytics Code Tool nodes Configure the Structured Output Parser schema to match your reporting fields. Test end-to-end with a sample CSV billing export before activating the daily schedule. Prerequisites Cloud provider billing export URLs (AWS, GCP, Azure) n8n instance (v1.0+) HTTP access to billing APIs Report destination (email, Slack, or storage) configured Use Cases FinOps teams generating daily multi-cloud spend digests Customisation Add a Slack or email node to distribute the final report automatically Benefits Daily automation eliminates manual billing export and analysis effort

Lead Scoring Pipeline – Matrix + Notion This workflow automatically enriches incoming form leads, applies a customizable scoring model, then routes high-value prospects to a Notion CRM database and a Matrix channel for instant sales notification. It streamlines lead qualification by combining third-party enrichment APIs, conditional logic, and asynchronous batching—eliminating manual research and ensuring your sales team focuses on the best opportunities. Pre-conditions/Requirements Prerequisites n8n instance (self-hosted or n8n cloud) ScrapeGraphAI community node installed Matrix community node installed Notion Integration (internal integration or Notion OAuth) Active third-party enrichment/validation APIs (e.g., Clearbit, Hunter, Kickbox) for scoring data A Notion database set up as your “Leads” table with fields: Name, Email, Company, Score, Status, Enrichment Required Credentials ScrapeGraphAI API Key** – For optional web-scraping based enrichment Notion OAuth2 / Integration Token** – To create & update pages in your CRM database Matrix Username & Password** – For sending room messages (Optional) Third-party API Keys** – Clearbit, Hunter, etc. Additional Setup Requirements | Setting | Example / Notes | |---------------------------------|----------------------------------------------------------| | Notion Database ID | 8f3d49e8c8f74e6f9d32e8e2bf7c1c46 | | Matrix Room ID | !aBcDeFgHiJ:matrix.org | | Scoring Threshold (env var) | LEAD_SCORE_MIN=70 | | Batch Size | SPLIT_IN_BATCHES=10 (controls parallel requests) | | Webhook URL for form submission | https://your-n8n-instance/webhook/leads | How it works This workflow automatically enriches incoming form leads, applies a customizable scoring model, then routes high-value prospects to a Notion CRM database and a Matrix channel for instant sales notification. It streamlines lead qualification by combining third-party enrichment APIs, conditional logic, and asynchronous batching—eliminating manual research and ensuring your sales team focuses on the best opportunities. Key Steps: Schedule / Webhook Trigger**: Starts on a schedule (daily) or via a form-submission webhook. Split in Batches**: Processes leads in manageable chunks to avoid rate limits. HTTP Requests**: Calls enrichment APIs (Clearbit, Hunter, etc.) to retrieve firmographic & technographic data. Code (Scoring Logic)**: Calculates a numeric lead score using weighted criteria (industry, employee count, email validity, etc.). If Node (Qualification)**: Checks if the computed score meets or exceeds the defined threshold. Notion Node**: Inserts or updates the qualified lead record in your Notion CRM database. Matrix Node**: Sends a rich-text message to the sales room with lead details and score. Wait + Merge**: Handles asynchronous API limits and merges enrichment data back into the main flow. Set up steps Setup Time: 15-25 minutes Clone the template in your n8n instance or import the JSON. Set environment variables (LEAD_SCORE_MIN, SPLIT_IN_BATCHES, API keys) in n8n Settings → Variables. Configure the Webhook/Schedule Trigger Webhook: copy the URL to your form provider Schedule: set cron (e.g., every hour). Update HTTP Request nodes with your enrichment API endpoints & headers. Map Notion fields: open the Notion node → choose your connection → select your Leads database → map data pins (Name, Email, etc.). Enter Matrix credentials & Room ID in the Matrix node. Adjust scoring weights in the Code node (weights object) to match your ICP (Ideal Customer Profile). Execute the workflow once with test data, verify Notion rows and Matrix messages. Activate the workflow and monitor via n8n’s execution list. Node Descriptions Core Workflow Nodes: Schedule Trigger / Webhook** – Initiates the workflow on time-based interval or form submission. SplitInBatches** – Limits API calls to avoid hitting rate caps. HTTP Request (Enrichment)** – Queries external services for company, domain, and email data. Set (Normalize)** – Formats API responses for scoring. Code (Score Calculator)** – JavaScript that assigns points based on business rules. If (Score ≥ Threshold?)** – Branches leads into “Qualified” or “Discard” paths. Notion (Create/Update Page)** – Inserts the qualified lead into Notion database. Matrix (Send Message)** – Notifies the sales team with lead context. Merge (Rejoin Streams)** – Consolidates asynchronous branches before completion. Sticky Note** – Documentation & config hints embedded directly in the canvas. Data Flow: Schedule/Webhook → SplitInBatches → HTTP Request (Enrichment) → Set → Code (Score) → If (Qualified?) → a. (Yes) Notion → Matrix b. (No) Wait (Cooldown) → Merge Customization Examples 1. Change Scoring Weights // Code node snippet const weights = { employeeCount: 0.4, // increase influence of company size emailDeliverability: 0.2, industryMatch: 0.25, techStack: 0.15, }; 2. Notify Different Matrix Rooms Based on Territory // If node expression {{ $json.country === 'US' ? '!usSales:matrix.org' : '!intlSales:matrix.org' }} Data Output Format The workflow outputs structured JSON data: { "name": "Jane Doe", "email": "jane@acme.co", "company": "Acme Co.", "enrichment": { "employeeCount": 120, "industry": "SaaS", "techStack": ["AWS", "React"] }, "score": 83, "qualified": true, "notionPageId": "9b2a4c09-0bf4-4b58-988a-1f361b0a2c20", "matrixEventId": "$15163622445EBvZJ:matrix.org" } Troubleshooting Common Issues Low or zero scores for valid leads – Review weighting logic and API response mapping in the Code node. Notion “Missing required property” error – Ensure all mandatory database fields are mapped and Notion token has write access. Matrix authentication failure – Double-check username/password or switch to an access token login method. Performance Tips Reduce SPLIT_IN_BATCHES size if you’re hitting API rate limits. Enable “Execute Once” during testing to prevent excess API usage. Pro Tips: Store enrichment API keys as n8n credentials, not hard-coded strings. Add a second If node to flag “Priority” leads (score > 90) for immediate follow-up. Use n8n’s built-in logging to create an audit trail by writing each execution to a Loggly/Datadog endpoint. This is a community template provided “as is.” Always test thoroughly in a non-production environment before activating in production.

This n8n template from Intuz delivers a complete and automated solution to streamline your development workflow for a single repository. By embedding specific keywords and a JIRA issue ID within your git commit commands, this workflow automatically creates a Pull Request in GitHub and simultaneously updates the corresponding JIRA ticket. This provides a complete, seamless integration that eliminates manual steps and keeps your project management perfectly in sync with your codebase. How it works This workflow acts as a powerful bridge between your Git repository and your project management tools, driven entirely by the structure of your commit messages. GitHub Webhook Trigger: The workflow starts when a developer pushes a new commit to a specified repository in GitHub. Parse Commit Message: A Code node extracts key information from the commit message: The JIRA Issue Key (e.g., FF-1196). The base branch for the PR (e.g., development). Action commands like [auto-pr] and [taskcompleted]. Conditional PR Creation: An IF node checks if the [auto-pr] command is present. If yes, it uses the GitHub node to automatically create a pull request from the developer's branch to the specified base branch. If no, this step is skipped, allowing for multiple commits before a PR is made. Conditional JIRA Update: Another IF node checks for the [taskcompleted] command. If yes, it uses the JIRA node to transition the corresponding issue to your "Done" status (e.g., "Task Completed" or "In Review"). If no, the JIRA issue remains in its current state, perfect for work-in-progress commits. How to Use: Quick Start Guide Click the "Use Template" button to import this workflow into your n8n instance. Configure the GitHub Trigger: Open the "GitHub Push Trigger" node. It will display a unique Webhook URL. Copy this URL. In your GitHub repository, go to Settings > Webhooks > Add webhook. Paste the URL into the Payload URL field. Set the Content type to application/json. Under "Which events would you like to trigger this webhook?", select Just the push event. Click Add webhook. Connect Your Accounts: GitHub: Select your GitHub API credential in the "Create Pull Request" node. JIRA : Select your JIRA API credential in the "Update JIRA Issue Status" node. Customize the JIRA Transition (Important): Open the "Update JIRA Issue Status" node. In the Transition parameter, you need to set the specific status you want to move the issue to (e.g., 'Done', 'Completed', 'In Review'). You can use the ID or the exact name of the transition from your JIRA project's workflow. Activate the Workflow: Save your changes and activate the workflow. You're ready to automate! Example Commit Message: git commit -m "FF-1196 Implement OAuth login [auto-pr,development,taskcompleted]" Key Requirements to Use Template An active n8n instance. A GitHub account with repository admin permissions to create webhooks. A JIRA Cloud account with permissions to update issues. Developers who can follow the specified git commit message format. Connect with us Website: https://www.intuz.com/services Email: getstarted@intuz.com LinkedIn: https://www.linkedin.com/company/intuz Get Started: https://n8n.partnerlinks.io/intuz For Custom Worflow Automation Click here- Get Started

Overview Transform your contract management process with this enterprise-grade workflow that handles the complete contract lifecycle - from initial intake through execution, monitoring, and renewal. This comprehensive solution combines AI-powered contract analysis with automated risk scoring, clause comparison, obligation tracking, and proactive alerts. It integrates with multiple data sources including email, SharePoint, contract CLM systems, and creates a centralized contract intelligence hub that prevents revenue leakage, ensures compliance, and accelerates deal velocity. What You Can Do This advanced workflow orchestrates a complete contract management ecosystem that monitors multiple channels (email, Google Drive, SharePoint, APIs) for new contracts and amendments. It extracts and analyzes over 50 contract data points using AI, performs multi-dimensional risk assessment across legal, financial, and operational factors, compares clauses against your approved template library, tracks all obligations and key dates with automated reminders, integrates with Salesforce/CRM for deal alignment, routes contracts through dynamic approval workflows based on risk scores, generates executive dashboards with contract analytics, and maintains a searchable repository with version control. The system handles complex scenarios including multi-party agreements, framework contracts with statements of work, international contracts requiring jurisdiction analysis, and M&A due diligence requiring bulk contract review. Who It's For Designed for enterprise legal operations teams managing thousands of contracts annually, procurement departments negotiating complex vendor agreements, contract managers overseeing multi-million dollar portfolios, compliance teams ensuring regulatory adherence across jurisdictions, sales operations needing faster contract turnaround, and C-suite executives requiring contract intelligence for strategic decisions. Essential for organizations in regulated industries (healthcare, finance, government) and companies undergoing digital transformation of their legal operations. The Problem It Solves Manual contract management creates massive operational risks and inefficiencies. Organizations typically have contracts scattered across emails, shared drives, and filing cabinets with no central visibility. This leads to missed renewal deadlines costing 5-10% of contract value, unauthorized contract variations creating compliance risks, obligation failures resulting in penalties and damaged relationships, and inability to leverage favorable terms across similar contracts. Studies show that inefficient contract management costs organizations up to 9% of annual revenue. This workflow creates a single source of truth for all contracts, automates tracking and compliance, and provides predictive insights to prevent issues before they occur. Setup Instructions Multi-Channel Integration: Configure connectors for email (Office 365/Gmail), Google Drive, SharePoint, and contract management systems PDF Vector Setup: Install PDF Vector node and configure API with enterprise rate limits Database Configuration: Set up PostgreSQL/MySQL for contract repository with proper indexing Template Library: Upload your standard contract templates and approved clause library Risk Framework: Configure risk scoring matrix for your industry (legal, financial, operational risks) Approval Matrix: Define approval routing based on contract value, type, and risk score CRM Integration: Connect to Salesforce/HubSpot for opportunity and account alignment Notification Setup: Configure Slack/Teams channels and email distribution lists Dashboard Creation: Set up Tableau/PowerBI connectors for executive reporting Security Configuration: Enable encryption, audit logging, and role-based access controls Key Features Intelligent Intake System**: Monitor email attachments, shared folders, CRM uploads, and API submissions Advanced AI Extraction**: Extract 50+ data points including nested obligations and conditional terms Multi-Dimensional Risk Scoring**: Analyze legal, financial, operational, and reputational risks Clause Library Comparison**: Compare against approved templates and flag deviations Obligation Management**: Track deliverables, milestones, and SLAs with automated alerts Dynamic Approval Routing**: Route based on AI risk score, contract value, and deviation analysis Version Control & Redlining**: Track all changes and maintain complete audit trail Salesforce Integration**: Sync contract data with opportunities and accounts Predictive Analytics**: Forecast renewal likelihood and negotiation outcomes Bulk Processing**: Handle M&A due diligence with parallel processing of hundreds of contracts Multi-Language Support**: Process contracts in 15+ languages with automatic translation Executive Dashboards**: Real-time visibility into contract portfolio and risk exposure Customization Options Implement industry-specific modules for healthcare (BAAs, DPAs), financial services (ISDAs, loan agreements), technology (SaaS, licensing), or government contracting. Add AI models trained on your historical contracts for better extraction accuracy. Create custom risk factors for emerging regulations like AI governance or ESG compliance. Build integration with specific CLM systems (Ironclad, Docusign CLM, Icertis). Implement advanced analytics including contract similarity scoring, win-rate analysis by clause variations, and automatic playbook generation. Add blockchain integration for smart contract execution and configure automated contract assembly for standard agreements. Note: This workflow uses the PDF Vector community node. Make sure to install it from the n8n community nodes collection before using this template.

🤖AI-Powered Appointment Scheduling with Google Calendar & Sheets Virtual Receptionist Automate customer conversations with an AI-powered virtual receptionist. This workflow can chat naturally with clients, answer general business questions (like services, location, and hours), check availability in Google Calendar, book appointments, and save customer details in Google Sheets. Fully customizable for any business type — salons, clinics, agencies, consultants, and more. 📖 How It Works Welcome the customer when the customer says hi AI greets warmly: “Hello! I’m [AI name] from [Business name].” Answer general questions Provides instant replies about services, pricing, business location, hours, and availability. Understand their need Identifies the service requested and preferred time. Check availability Queries Google Calendar for open slots. Gather customer details Collects name, phone, and email (optional). Confirm booking Creates the appointment in Google Calendar. Save records Logs booking and customer info into Google Sheets. ⚙️ Setup Steps (Quick) Connect your Google Calendar and Google Sheets accounts. Add your business details (name, type, services, hours, policies) to the Business Info Sheet. Configure your OpenAI API key (or use n8n free credits). Optional: Connect Twilio WhatsApp for direct chat responses. 🏢 Example Business Info (Google Sheet) | business_id | business_name | business_type | location | phone | email | services | calendar_id | timezone | currency | working_hours | ai_name | ai_personality | ai_role | emergency_available | booking_advance_days | cancellation_hours | |-------------|-----------------|---------------------|----------------------------------|-----------------|---------------------------|----------|-----------------------|----------|----------|--------------------------------|---------|-----------------------------------|------------------------------------------------------------------------------------------------|----------------------|----------------------|-------------------| |001| Luxe Hair Studio | Hair & Beauty Salon | 123 Main Street, New York, NY 10001 | 1 (XXX) XXX-XXXX | yourbusiness@email.com | “Haircut & Styling (60 minutes, $3500…)Hair Coloring (120 minutes, $8000…)…” | calendar-id-here | GMT -3 | USD | Mon–Sat: 9:00 AM – 7:00 PM, Sun: Closed | bella | Friendly, Stylish, Professional | Manages bookings, answers FAQs, recommends services, gives beauty tips, sends reminders, etc. | no | 10 | 24 | ✅ Purpose: Supplies context (services, pricing, hours, AI personality, booking policies). 💡 The AI uses this sheet to answer general business questions (e.g., “Where are you located?”, “Do you do hair colouring?”, “What are your working hours?”). 📊 Appointments Sheet Example | client_number | client_name | event_id | summary | services | |----------------|-------------|-----------|----------------------------------|----------| | 001 | Sarah Lee | evt-10293 | Appointment with Sarah Lee – Haircut & Styling | Haircut & Styling | | 002 | John Smith | evt-10294 | Appointment with John Smith – Highlights | Highlights | ✅ Purpose: Logs confirmed bookings with service details and links back to Google Calendar. 💡 Features ✅ AI receptionist with conversation memory ✅ Answers FAQs – location, services, hours, pricing ✅ Google Calendar integration for real-time availability ✅ Google Sheets integration for customer records & reporting ✅ Customizable AI name, role, and personality 🔑 Who It’s For Salons & Spas** – Manage bookings and FAQs Clinics & Health Services** – Automated scheduling + patient info Agencies & Consultants** – Answer inquiries + schedule meetings Any Service Business** – Save time, improve customer experience

How it works This template is an end-to-end demo of a chatbot using business data from multiple sources (e.g. Notion, Chargebee, Hubspot etc.) with RAG + SQL. Peliqan.io is used as a "cache" of all business data. Peliqan uses one-click ELT to sync all your business data to its built-in data warehouse, allowing for fast & accurate RAG and "Text to SQL" queries. The workflow will write source data to Supabase as a vector store, for RAG searches by the chatbot. The source URL (e.g. the URL of a Notion page) is added in metadata. The AI Agent will decide for each question to use either RAG or Text-to-SQL or a combination of both. Text-to-SQL is performed via the Peliqan node, added as a tool to the AI Agent. The question of the user in natural language is converted to an SQL query by the AI Agent. The query is executed by Peliqan.io on the source data and the result is interpreted by the AI Agent. RAG is typically used to answer knowledge questions, often on non-structured data (Notion pages, Google Drive etc.). Text-to-SQL is typically used to answer analytical questions, for example "Show list of customers with number of open support tickets and add customer revenue based on invoiced amounts". Preconditions You signed up for a Peliqan.io free trial account You have one or more data sources, e.g. a CRM, ERP, Accounting software, files, Notion, Google Drive etc. Set up steps Sign up for a free trial on peliqan.io: https://peliqan.io Add one or more sources in Peliqan (e.g. Hubspot, Pipedrive...) Copy your Peliqan API key under settings and use it here to add a Peliqan connection Run the "RAG" workflow to feed Supabase, change the name of the table in the Peliqan node "Get table data". Update the list of tables & columns that can be used for SQL in the System Message of the AI Agent. Visit https://peliqan.io/n8n for more information. Disclaimer: This template contains a community node and therefore only works for n8n self-hosted users.

Description: Overview This workflow generates automated revenue and expense comparison reports from a structured Google Sheet. It enables users to compare financial data across the current period, last month, and last year, then uses an AI agent to analyze and summarize the results for business reporting. Prerequisites A connected Google Sheets OAuth2 credential. A valid DeepSeek AI API (or replaceable with another Chat Model). A sub-workflow (child workflow) that handles processing logic. Properly structured Google Sheets data (see below). Required Google Sheet Structure Column headers must include at least: Date, Amount, Type. Setup Steps Import the workflow into your n8n instance. Connect your Google Sheets and DeepSeek API credentials. Update: Sheet ID and Tab Name (already embedded in node: Get revenual from google sheet). Custom sub-workflow ID (in the Call n8n Workflow Tool node). Optionally configure chatbot webhook in the When chat message received node. What the Workflow Does Accepts date inputs via AI chat interface (ChatTrigger + AI Agent). Fetches raw transaction data from Google Sheets. Segments and pivots revenue by classification for: Current period Last month Last year Aggregates totals and applies custom titles for comparison. Merges all summaries into a final unified JSON report. Customization Options Replace DeepSeek with OpenAI or other LLMs. Change the date fields or cycle comparisons (e.g., quarterly, weekly). Add more AI analysis steps such as sentiment scoring or forecasting. Modify the pivot logic to suit specific KPI tags or labels. Troubleshooting Tips If Google Sheets fetch fails: ensure the document is shared with your n8n Google credential. If parsing errors: verify that all dates follow the expected format. Sub-workflow must be active and configured to accept the correct inputs (6 dates). SEO Keywords (ẩn hoặc mô tả ngầm): Google Sheets report, AI financial report, compare revenue by month, expense analysis automation, chatbot n8n report generator, n8n Google Sheet integration