Hunter and Postgres integration

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.

Who’s this for 💼 This template is designed for teams and developers who need to generate PDF documents automatically from HTML templates. It’s suitable for use cases such as invoices, confirmations, reports, certificates, or any custom document that needs to be created dynamically based on incoming data. What this workflow does ⚙️ This workflow automates the full lifecycle of document generation, from request validation to delivery and storage. It is triggered by a POST webhook that receives structured JSON data describing the requested document and client information. Before generating the document, the workflow validates the client’s email address using Hunter Email Verification to prevent invalid or mistyped emails. If the email is valid, the workflow loads the appropriate HTML template from a Postgres database, fills it with the incoming data, and converts it into a PDF using PDF Generator API. Once the PDF is generated, it is sent to the client via Gmail, uploaded to Supabase Storage, and the transaction is recorded in the database for tracking and auditing purposes. How it works 🛠️ Receives a document generation request via a POST webhook. Validates the client’s email address using Hunter. Generates a PDF document from an HTML template using PDF Generator API. Sends the PDF via Gmail and uploads it to Supabase Storage. Stores a document generation record in the database. How to set up 🖇️ Before activating the workflow, make sure all required services and connections are prepared and available in your n8n environment. Create a POST webhook endpoint that accepts structured JSON input. Add Hunter API credentials for email verification. Add PDF Generator API credentials for HTML to PDF conversion. Prepare a Postgres database with tables for HTML templates and document generation records. Set up Gmail or SMTP credentials for email delivery. Configure Supabase Storage for storing generated PDF files. Requirements ✅ PDF Generator API account Hunter account Postgres database Gmail or SMTP-compatible email provider Supabase project with Storage enabled How to customize the workflow 🤖 This workflow can be adapted to different document generation scenarios by extending or modifying its existing steps: Add extra validation steps before document generation if required. Extend delivery options by sending the generated PDF to additional services or webhooks. Enhance security by adding document encryption or access control. Add support for additional document types by storing more HTML templates in the database. Modify the database schema or queries to store additional metadata related to generated documents. Adjust the data mapping logic in the Code node to match your input structure.