Pramod Rathoure
I design and build custom n8n workflows that cut out repetitive work, connect your favorite tools, and keep things running smoothly—think less busywork, smarter integrations, and faster, error-free results. My goal is to free up your time so you and your team can focus on what really matters. Let’s chat—I’d love to help you turn messy processes into smooth, automated workflows.
Templates by Pramod Rathoure
Create a company policy chatbot with RAG, Pinecone vector database, and OpenAI
A RAG Chatbot with n8n and Pinecone Vector Database Retrieval-Augmented Generation (RAG) allows Large Language Models (LLMs) to provide context-aware answers by retrieving information from an external vector database. In this post, we’ll walk through a complete n8n workflow that builds a chatbot capable of answering company policy questions using Pinecone Vector Database and OpenAI models. Our setup has two main parts: Data Loading to RAG – documents (company policies) are ingested from Google Drive, processed, embedded, and stored in Pinecone. Data Retrieval using RAG – user queries are routed through an AI Agent that uses Pinecone to retrieve relevant information and generate precise answers. --- Data Loading to RAG This workflow section handles document ingestion. Whenever a new policy file is uploaded to Google Drive, it is automatically processed and indexed in Pinecone. Nodes involved: Google Drive Trigger Watches a specific folder in Google Drive. Any new or updated file triggers the workflow. Google Drive (Download) Fetches the file (e.g., a PDF policy document) from Google Drive for processing. Recursive Character Text Splitter Splits long documents into smaller chunks (with a defined overlap). This ensures embeddings remain context-rich and retrieval works effectively. Default Data Loader Reads the binary document (PDF in this setup) and extracts the text. OpenAI Embeddings Generates high-dimensional vector representations of each text chunk using OpenAI’s embedding models. Pinecone Vector Store (Insert Mode) Stores the embeddings into a Pinecone index (n8ntest), under a chosen namespace. This step makes the policy data searchable by semantic similarity. 👉 Example flow: When HR uploads a new Work From Home Policy PDF to Google Drive, it is automatically split, embedded, and indexed in Pinecone. --- Data Retrieval using RAG Once documents are loaded into Pinecone, the chatbot is ready to handle user queries. This section of the workflow connects the chat interface, AI Agent, and retrieval pipeline. Nodes involved: When Chat Message Received Acts as the webhook entry point when a user sends a question to the chatbot. AI Agent The core reasoning engine. It is configured with a system message instructing it to only use Pinecone-backed knowledge when answering. Simple Memory Keeps track of the conversation context, so the bot can handle multi-turn queries. Vector Store QnA Tool Queries Pinecone for the most relevant chunks related to the user’s question. In this workflow, it is configured to fetch company policy documents. Pinecone Vector Store (Query Mode) Acts as the connection to Pinecone, fetching embeddings that best match the query. OpenAI Chat Model Refines the retrieved chunks into a natural and concise answer. The model ensures answers remain grounded in the source material. Calculator Tool Optional helper if the query involves numerical reasoning (e.g., leave calculations or benefit amounts). 👉 Example flow: A user asks “How many work-from-home days are allowed per month?”. The AI Agent queries Pinecone through the Vector Store QnA tool, retrieves the relevant section of the HR policy, and returns a concise answer grounded in the actual document. --- Wrapping Up By combining n8n automation, Pinecone for vector storage, and OpenAI for embeddings + LLM reasoning, we’ve created a self-updating RAG chatbot. Data Loading pipeline ensures that every new company policy document uploaded to Google Drive is immediately available for semantic search. Data Retrieval pipeline allows employees to ask natural language questions and get document-backed answers. This setup can easily be adapted for other domains — compliance manuals, tax regulations, legal contracts, or even product documentation.
By Pramod RathoureRestaurant lead generation from Google Maps with Apify, Airtable & AI newsletter
This workflow contains community nodes that are only compatible with the self-hosted version of n8n. 🚀 Automating Google Maps Lead Generation with n8n + Apify Finding quality leads can be time-consuming. What if you could scrape restaurant data from Google Maps, filter the best ones, and email a Morning Brew–style newsletter automatically? That’s exactly what this n8n workflow does. 🔎 What This Workflow Does Takes a location input (Bangkok or Bareilly in this case) Runs a Google Maps scraper via Apify Actor Extracts restaurant essentials (name, category, rating, reviews, address, phone, Google Maps link) Sorts & filters results (only high-review, highly-rated places) Saves data to Airtable for lead management Uses AI to generate a newsletter in a Morning Brew–style HTML email Emails the newsletter automatically to your chosen recipients 🛠️ Workflow Breakdown Form Trigger User selects location from a dropdown (Bangkok or Bareilly) Submits form to kickstart the process Google Maps Scraper Powered by Apify Collects up to 1,000 restaurants* with details: Name Category Price Range Rating Reviews Address Phone Google Maps URL Skips closed places and pulls detailed contact data Extract & Transform Data n8n Set node extracts only the essentials Formats them into a clean text block (Restaurant_Data) Sort & Filter Sorted by: Review Count (descending) Rating (descending) Filter: Only restaurants with 500+ reviews* Airtable Lead Storage Each record is saved to Google Map Leads - Restaurants Airtable table Fields include: Title Category Price Range Rating Review Count Address Phone Location AI-Powered Newsletter n8n’s LangChain + OpenAI node generates an HTML newsletter Tone: Breezy, witty, like Morning Brew Content: Sorted restaurant picks with ratings, reviews, and contact links Output is JSON with "Subject" and "Body" Automatic Email Gmail node sends the newsletter directly to your inbox Example recipient: prath002@gmail.com 🎯 Why This Workflow Rocks End-to-End Automation: From scraping → filtering → emailing, no manual effort Lead Enrichment: Only keeps high-quality restaurants with strong social proof Scalable: Works for any city you plug into the form Engaging Output: AI crafts the results into a ready-to-send newsletter 🔮 Next Steps Add more cities to the dropdown for multi-location scraping Customize the email template for branding Integrate with CRM tools for automated outreach 👉 With just a few clicks, you can go from raw Google Maps data → polished newsletter → fresh leads in Airtable. That’s the power of n8n + Apify + AI.
By Pramod RathoureAutomate employee reimbursement workflow with Gmail, Google Drive & AI validation
Reimbursements used to be a headache. Employees submitted receipts through emails, managers got stuck in approval chains, and finance teams spent hours checking for duplicates, updating sheets, and sending follow-up emails. So, we automated it. Using n8n, we built a smart Employee Reimbursement Workflow that does everything… in just a few clicks. Here’s how it works.] When an employee uploads a receipt, the workflow first checks for duplicates. If the file is new, it’s uploaded to Google Drive instantly. Next, a unique tracking ID is generated—no manual typing, no mistakes. Then, all the details are logged in Google Sheets in real time, ready for records. And finally, the Finance team gets an email notification with everything they need to process the payment—no chasing, no missing info. The impact? We’ve cut processing time by over 70%, reduced errors to nearly zero, and made the entire process stress-free for employees and finance alike. This isn’t just automation—it’s giving people their time back.
By Pramod RathoureAutomate sponsored deal email responses with Gmail and GPT-4
Automate Sponsored Deal Emails with n8n + AI 🚀 Managing inbound emails can be exhausting — especially when your inbox is flooded with sponsored deal requests that don’t always fit your brand. Manually reading, filtering, and politely declining each one eats up valuable time. That’s why I built a smart n8n workflow that automatically: Detects sponsored deal inquiries, Decides if they match my criteria, Drafts a professional, courteous reply, Or simply ignores irrelevant messages. The best part? It runs on autopilot. Let’s break down every single node in this workflow. 🔔 1. Gmail Trigger – “Your Inbox Watchdog” Polls your Gmail inbox every minute. Detects new emails instantly. Hands over each email to the next step for analysis. ✂️ 2. Edit Fields (Set Node) – “Extract the Essentials” Pulls out the most important parts of the email: From → Sender’s email address Subject → The email subject line Email Body → The main text content Keeps data structured and ready for AI to process. 🧠 3. AI Agent – “The Smart Classifier” Powered by LangChain + OpenAI. Reads the email content carefully. Outputs two things: isSponsoredEmail → true/false reason → why it decided that way Example: json { "isSponsoredEmail": true, "reason": "The email mentions a paid collaboration opportunity." } Structured Output Parser – “Keep It Clean” Ensures the AI response is always structured properly. Forces the output into this format: json { "isSponsoredEmail": true/false, "reason": "string" } Prevents messy AI replies from breaking the workflow. 🔀 5. Node – “Decision Maker” Branches logic into two paths: If sponsored → moves on to draft a polite response. If not sponsored → sends it to “No Operation” (do nothing). ✍️ 6. AI Agent (Reply Writer) – “Crafts the Perfect Response” If it’s a sponsored email, this node drafts a professional reply. Reply includes: Thanking the sender for reaching out. Explaining sponsorship criteria: Alignment with brand values Relevance for your audience Fit with internal planning cycles Politely declining if the timing isn’t right. Leaving the door open for future opportunities. Tone: professional, warm, and courteous. 📤 7. Gmail Node – “Hit Send!” Takes the AI-generated reply. Sends it directly back to the original sender. No manual typing, no waiting — just instant professionalism. 🚫 8. No Operation – “Do Nothing, Gracefully” If the email isn’t a sponsored deal, the workflow stops here. No unnecessary actions are taken. Keeps your system clean and efficient. 📝 Sticky Notes (Workflow Documentation) To make the workflow easier to understand inside n8n, sticky notes were added: Email Trigger → Explains inbox polling. Extract → Notes what fields are being pulled. Process and Validate → Describes how AI decides sponsorship. Prepare Email Body → Documents reply drafting process. Reply → Clarifies auto-reply step. Do Nothing → Notes what happens if it’s not sponsored. 🌟 Why This Workflow Rocks Saves Hours: No more manual email filtering. Consistent Replies: Always professional and brand-friendly. Scalable: Works for 5 or 500 inbound emails. Customizable: Easily adapt prompts and conditions to fit your brand. ⚡ Try It Yourself This workflow is built in n8n, an open-source automation tool. You can import the JSON file into your own n8n instance and customize: Your Gmail credentials Your company’s sponsorship guidelines The tone of the AI-generated replies 👉 With just a few tweaks, you’ll have a smart email assistant running on autopilot! 💡 Pro Tip Even if you don’t deal with sponsored deals, you can repurpose this workflow for: Job applications Customer support inquiries Lead qualification The same logic applies — just adjust the AI prompts and reply template.
By Pramod Rathoure