HumationHuman + Automation
Back to Catalog

Automated lead research – from LinkedIn to ready-to-send report

LukaszBLukaszB
1558 views
2/3/2026
TrelloDue DateAutomationUpdate CardAPI
Official Page

AI Prospect Researcher – Automated Lead Intelligence Workflow

This workflow is built for professionals and teams who want to scale their B2B outreach with context-rich, personalized communication. It automates the full prospect research process — from pulling lead data and scraping LinkedIn profiles, to gathering real-time company insights and generating high-quality outreach reports with GPT-4.

Using a combination of Apify, Perplexity AI, and OpenAI, this system creates a structured Google Doc for each lead, along with a logged summary in Google Sheets. Whether you’re preparing for sales calls, writing cold emails, or enriching your CRM — this tool delivers ready-to-use intelligence in minutes, without manual research.

The process is modular, production-ready, and suitable for agencies, SDR teams, or founders managing outbound on their own.

How it works

Once triggered, the workflow takes in a list of leads from Google Sheets. For each lead, it uses Apify to scrape both the LinkedIn profile and company page (no login or cookies required). Then, Perplexity AI fetches contextual insights and competitor data. GPT-4 validates the research and synthesizes a structured summary of the individual and their company. Finally, a complete outreach report is generated and saved in Google Docs, while key data is logged in Sheets for tracking or follow-up automation.

This is a powerful, production-grade automation for anyone serious about personalizing outreach without spending hours per lead.

Automated Lead Research: From LinkedIn to Ready-to-Send Report

This n8n workflow automates the process of extracting lead information, enriching it with AI, and generating a structured report. It's designed to streamline lead generation by taking raw data, processing it through an AI model, and then organizing the results into a Google Sheet and a Google Doc.

What it does

This workflow performs the following steps:

  1. Manual Trigger: Initiates the workflow upon manual execution.
  2. Google Sheets (Input): Reads lead data from a specified Google Sheet. This sheet is expected to contain initial lead information.
  3. Loop Over Items: Processes each row (lead) from the Google Sheet individually.
  4. Edit Fields (Set): Prepares the data for the AI model by setting specific fields, likely combining or formatting existing data.
  5. Basic LLM Chain: Acts as a wrapper for the Language Model, orchestrating the interaction with the OpenAI Chat Model and the Structured Output Parser.
  6. OpenAI Chat Model: Communicates with the OpenAI API to process the lead data. This node is responsible for the AI-powered data enrichment, such as extracting specific information, summarizing profiles, or generating insights based on the input.
  7. Structured Output Parser: Parses the response from the OpenAI Chat Model into a structured format, ensuring consistency in the output data.
  8. Wait: Introduces a delay in the workflow, likely to manage API rate limits or to allow for external processing time.
  9. HTTP Request: Makes an HTTP request, potentially to another API or service, using the enriched lead data. The exact purpose is not specified in the JSON but could be for further data validation, posting to a CRM, or triggering another external process.
  10. If: Implements conditional logic. Based on the outcome of the previous steps (e.g., success of the HTTP request or a specific AI output), the workflow branches.
  11. Google Sheets (Output - Success): If the 'If' condition is true, this node appends the successfully processed and enriched lead data to a designated Google Sheet.
  12. Google Docs (Output - Success): If the 'If' condition is true, this node creates or updates a Google Doc with the enriched lead report.
  13. Google Sheets (Output - Failure): If the 'If' condition is false, this node appends the unprocessed or failed lead data to a separate Google Sheet, likely for review or error handling.
  14. Merge: Combines the outputs from both the success and failure branches of the 'If' node, allowing the workflow to continue as a single stream after conditional processing.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance to import and execute the workflow.
  • Google Account: With access to Google Sheets and Google Docs for inputting raw data and outputting reports.
  • OpenAI API Key: For the OpenAI Chat Model to perform AI-powered data enrichment.
  • Credentials for Google Sheets and Google Docs: Configured within n8n.
  • Credentials for OpenAI: Configured within n8n.
  • Initial Google Sheet: Containing the raw lead data to be processed.

Setup/Usage

  1. Import the Workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your Google Sheets and Google Docs credentials in n8n.
    • Set up your OpenAI API key credentials in n8n.
  3. Update Google Sheet Nodes:
    • In the initial "Google Sheets" node (ID 18), configure it to read from your source Google Sheet containing lead data.
    • In the "Google Sheets" output nodes (ID 18, 18), configure them to write to your desired success and failure Google Sheets.
  4. Update Google Docs Node:
    • In the "Google Docs" node (ID 495), configure it to create or update your lead report document.
  5. Review and Customize:
    • Examine the "Edit Fields (Set)" node (ID 38) to ensure the data transformation aligns with your lead data structure.
    • Adjust the prompts and settings in the "OpenAI Chat Model" node (ID 1153) and "Structured Output Parser" node (ID 1179) to get the desired AI enrichment and output format.
    • Configure the "HTTP Request" node (ID 19) if you intend to integrate with other services.
    • Adjust the conditions in the "If" node (ID 20) to match your criteria for success or failure.
    • Modify the "Wait" node (ID 514) duration if needed to adhere to API rate limits.
  6. Execute Workflow: Click the "Execute workflow" button on the "Manual Trigger" node (ID 838) to run the workflow.

Related Templates

Track competitor SEO keywords with Decodo + GPT-4.1-mini + Google Sheets

This workflow automates competitor keyword research using OpenAI LLM and Decodo for intelligent web scraping. Who this is for SEO specialists, content strategists, and growth marketers who want to automate keyword research and competitive intelligence. Marketing analysts managing multiple clients or websites who need consistent SEO tracking without manual data pulls. Agencies or automation engineers using Google Sheets as an SEO data dashboard for keyword monitoring and reporting. What problem this workflow solves Tracking competitor keywords manually is slow and inconsistent. Most SEO tools provide limited API access or lack contextual keyword analysis. This workflow solves that by: Automatically scraping any competitor’s webpage with Decodo. Using OpenAI GPT-4.1-mini to interpret keyword intent, density, and semantic focus. Storing structured keyword insights directly in Google Sheets for ongoing tracking and trend analysis. What this workflow does Trigger — Manually start the workflow or schedule it to run periodically. Input Setup — Define the website URL and target country (e.g., https://dev.to, france). Data Scraping (Decodo) — Fetch competitor web content and metadata. Keyword Analysis (OpenAI GPT-4.1-mini) Extract primary and secondary keywords. Identify focus topics and semantic entities. Generate a keyword density summary and SEO strength score. Recommend optimization and internal linking opportunities. Data Structuring — Clean and convert GPT output into JSON format. Data Storage (Google Sheets) — Append structured keyword data to a Google Sheet for long-term tracking. Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n account with workflow editor access Decodo API credentials OpenAI API key Google Sheets account connected via OAuth2 Make sure to install the Decodo Community node. Create a Google Sheet Add columns for: primarykeywords, seostrengthscore, keyworddensity_summary, etc. Share with your n8n Google account. Connect Credentials Add credentials for: Decodo API credentials - You need to register, login and obtain the Basic Authentication Token via Decodo Dashboard OpenAI API (for GPT-4o-mini) Google Sheets OAuth2 Configure Input Fields Edit the “Set Input Fields” node to set your target site and region. Run the Workflow Click Execute Workflow in n8n. View structured results in your connected Google Sheet. How to customize this workflow Track Multiple Competitors → Use a Google Sheet or CSV list of URLs; loop through them using the Split In Batches node. Add Language Detection → Add a Gemini or GPT node before keyword analysis to detect content language and adjust prompts. Enhance the SEO Report → Expand the GPT prompt to include backlink insights, metadata optimization, or readability checks. Integrate Visualization → Connect your Google Sheet to Looker Studio for SEO performance dashboards. Schedule Auto-Runs → Use the Cron Node to run weekly or monthly for competitor keyword refreshes. Summary This workflow automates competitor keyword research using: Decodo for intelligent web scraping OpenAI GPT-4.1-mini for keyword and SEO analysis Google Sheets for live tracking and reporting It’s a complete AI-powered SEO intelligence pipeline ideal for teams that want actionable insights on keyword gaps, optimization opportunities, and content focus trends, without relying on expensive SEO SaaS tools.

Ranjan DailataBy Ranjan Dailata
161

Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax

Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions

Daniel NkenchoBy Daniel Nkencho
601

Automate invoice processing with OCR, GPT-4 & Salesforce opportunity creation

PDF Invoice Extractor (AI) End-to-end pipeline: Watch Drive ➜ Download PDF ➜ OCR text ➜ AI normalize to JSON ➜ Upsert Buyer (Account) ➜ Create Opportunity ➜ Map Products ➜ Create OLI via Composite API ➜ Archive to OneDrive. --- Node by node (what it does & key setup) 1) Google Drive Trigger Purpose: Fire when a new file appears in a specific Google Drive folder. Key settings: Event: fileCreated Folder ID: google drive folder id Polling: everyMinute Creds: googleDriveOAuth2Api Output: Metadata { id, name, ... } for the new file. --- 2) Download File From Google Purpose: Get the file binary for processing and archiving. Key settings: Operation: download File ID: ={{ $json.id }} Creds: googleDriveOAuth2Api Output: Binary (default key: data) and original metadata. --- 3) Extract from File Purpose: Extract text from PDF (OCR as needed) for AI parsing. Key settings: Operation: pdf OCR: enable for scanned PDFs (in options) Output: JSON with OCR text at {{ $json.text }}. --- 4) Message a model (AI JSON Extractor) Purpose: Convert OCR text into strict normalized JSON array (invoice schema). Key settings: Node: @n8n/n8n-nodes-langchain.openAi Model: gpt-4.1 (or gpt-4.1-mini) Message role: system (the strict prompt; references {{ $json.text }}) jsonOutput: true Creds: openAiApi Output (per item): $.message.content → the parsed JSON (ensure it’s an array). --- 5) Create or update an account (Salesforce) Purpose: Upsert Buyer as Account using an external ID. Key settings: Resource: account Operation: upsert External Id Field: taxid_c External Id Value: ={{ $json.message.content.buyer.tax_id }} Name: ={{ $json.message.content.buyer.name }} Creds: salesforceOAuth2Api Output: Account record (captures Id) for downstream Opportunity. --- 6) Create an opportunity (Salesforce) Purpose: Create Opportunity linked to the Buyer (Account). Key settings: Resource: opportunity Name: ={{ $('Message a model').item.json.message.content.invoice.code }} Close Date: ={{ $('Message a model').item.json.message.content.invoice.issue_date }} Stage: Closed Won Amount: ={{ $('Message a model').item.json.message.content.summary.grand_total }} AccountId: ={{ $json.id }} (from Upsert Account output) Creds: salesforceOAuth2Api Output: Opportunity Id for OLI creation. --- 7) Build SOQL (Code / JS) Purpose: Collect unique product codes from AI JSON and build a SOQL query for PricebookEntry by Pricebook2Id. Key settings: pricebook2Id (hardcoded in script): e.g., 01sxxxxxxxxxxxxxxx Source lines: $('Message a model').first().json.message.content.products Output: { soql, codes } --- 8) Query PricebookEntries (Salesforce) Purpose: Fetch PricebookEntry.Id for each Product2.ProductCode. Key settings: Resource: search Query: ={{ $json.soql }} Creds: salesforceOAuth2Api Output: Items with Id, Product2.ProductCode (used for mapping). --- 9) Code in JavaScript (Build OLI payloads) Purpose: Join lines with PBE results and Opportunity Id ➜ build OpportunityLineItem payloads. Inputs: OpportunityId: ={{ $('Create an opportunity').first().json.id }} Lines: ={{ $('Message a model').first().json.message.content.products }} PBE rows: from previous node items Output: { body: { allOrNone:false, records:[{ OpportunityLineItem... }] } } Notes: Converts discount_total ➜ per-unit if needed (currently commented for standard pricing). Throws on missing PBE mapping or empty lines. --- 10) Create Opportunity Line Items (HTTP Request) Purpose: Bulk create OLIs via Salesforce Composite API. Key settings: Method: POST URL: https://<your-instance>.my.salesforce.com/services/data/v65.0/composite/sobjects Auth: salesforceOAuth2Api (predefined credential) Body (JSON): ={{ $json.body }} Output: Composite API results (per-record statuses). --- 11) Update File to One Drive Purpose: Archive the original PDF in OneDrive. Key settings: Operation: upload File Name: ={{ $json.name }} Parent Folder ID: onedrive folder id Binary Data: true (from the Download node) Creds: microsoftOneDriveOAuth2Api Output: Uploaded file metadata. --- Data flow (wiring) Google Drive Trigger → Download File From Google Download File From Google → Extract from File → Update File to One Drive Extract from File → Message a model Message a model → Create or update an account Create or update an account → Create an opportunity Create an opportunity → Build SOQL Build SOQL → Query PricebookEntries Query PricebookEntries → Code in JavaScript Code in JavaScript → Create Opportunity Line Items --- Quick setup checklist 🔐 Credentials: Connect Google Drive, OneDrive, Salesforce, OpenAI. 📂 IDs: Drive Folder ID (watch) OneDrive Parent Folder ID (archive) Salesforce Pricebook2Id (in the JS SOQL builder) 🧠 AI Prompt: Use the strict system prompt; jsonOutput = true. 🧾 Field mappings: Buyer tax id/name → Account upsert fields Invoice code/date/amount → Opportunity fields Product name must equal your Product2.ProductCode in SF. ✅ Test: Drop a sample PDF → verify: AI returns array JSON only Account/Opportunity created OLI records created PDF archived to OneDrive --- Notes & best practices If PDFs are scans, enable OCR in Extract from File. If AI returns non-JSON, keep “Return only a JSON array” as the last line of the prompt and keep jsonOutput enabled. Consider adding validation on parsing.warnings to gate Salesforce writes. For discounts/taxes in OLI: Standard OLI fields don’t support per-line discount amounts directly; model them in UnitPrice or custom fields. Replace the Composite API URL with your org’s domain or use the Salesforce node’s Bulk Upsert for simplicity.

Le NguyenBy Le Nguyen
942