Auto-Generate Lead Research Report After Booking

Cal.com trigger → Google Sheets LinkedIn lookup → Relevance AI scrape + Perplexity research → OpenAI profile & pain points → HTML report → Gmail delivery

Who

Sales reps, consultants, and founders who need pre-meeting prospect intelligence

You get

Receive a formatted HTML email report minutes after a booking, with LinkedIn profile, company info, recent posts, news context, pain points, and high-ROI automation suggestions

You need

n8n · gmail · google-sheets · perplexity · relevance-ai

Steps

1. Configure trigger and database connections

   After importing the template, connect the Cal.com Trigger (or replace with Calendly/SavvyCal) and set it to listen for BOOKING_CREATED events. Configure Google Sheets node(s) to query your actual customer database containing email and LinkedIn URL fields. Merge the multiple lookup nodes into one or point them to the same sheet. Change the Filter node's rightValue from [email protected] to your actual test email or remove the filter. [First-test safety] Keep the workflow inactive after import; before the first test, temporarily disconnect the Email Report node input, or set sendTo to a test mailbox and keep sending disabled/unactivated, so a real booking cannot auto-email or burn Relevance AI / Perplexity API quota.

2. Scrape and format LinkedIn data

   Configure the Scrape Profiles + Posts - Relevance AI HTTP Request node: replace the URL with your own Relevance AI project endpoint (the template uses https://api-bcbe5a.stack.tryrelevance.com/...), and provide your API key in the HTTP Header Auth credential. This node sends a POST request with jsonBody {"linkedin_url":"{{ $json.profileUrl }}","last_x_days":30}. Downstream Posts, Experiences, and Education Code nodes automatically transform the response into HTML fragments. Node request body: ```json ={"linkedin_url":"{{ $json.profileUrl }}","last_x_days":30} ``` Node code: ```js const input = $node["Scrape Profiles + Posts - Relevance AI"].json; // Getting posts from output, then fallback to top-level. const posts = (input.output && input.output.last_30_days_posts_transformed) || input.last_30_days_posts_transformed || []; // Build HTML for each LinkedIn post let postsHTML = ''; if (Array.isArray(posts)) { posts.forEach(post => { postsHTML += `<div class="linkedin-post" style="border:1px solid #ddd; padding:10px; margin-bottom:10px;"> <p>${post.user_post}</p> <p><em>Posted on: ${post.posted}</em></p> </div>`; }); } // Return only the generated HTML return [{ json: { postsHTML } }]; ``` Node code: ```js // Get the input data from the first item const input = $node["Scrape Profiles + Posts - Relevance AI"].json; // Navigate to the experiences array within the "output" object const experiences = input.linkedin_profile_details_data?.experiences || []; // Build the HTML table rows string let htmlRows = ''; experiences.forEach(exp => { htmlRows += `<tr> <td>${exp.company}</td> <td>${exp.title}</td> <td>${exp.date_range}</td> <td>${exp.location}</td> </tr>`; }); // Return a new object that only contains the experiencesTable field return [{ json: { experiencesTable: htmlRows } }]; ``` Node code: ```js // Get the input data from the first item const input = $node["Scrape Profiles + Posts - Relevance AI"].json; // Navigate to the educations array in the output object const educations = input.linkedin_profile_details_data?.educations || []; // Build the HTML table rows for education let educationRows = ''; educations.forEach(edu => { educationRows += `<tr> <td>${edu.school}</td> <td>${edu.degree}</td> <td>${edu.field_of_study}</td> </tr>`; }); // Return a new object that only contains the educationTable key return [{ json: { educationTable: educationRows } }]; ```

3. Run deep web research

   Configure the Perplexity node: select sonar-pro model and provide Perplexity API credentials. The system message sets the researcher role; the user message passes the company name and website URL extracted from LinkedIn. The downstream Citations Code node converts the Perplexity citations array into an HTML link list. Node code: ```js // Retrieve the JSON from the previous node const input = items[0].json; // Extract the citations array; default to an empty array if not present const citations = input.citations || []; // Build an HTML unordered list for the citations let citationsHTML = '<ul>'; if (Array.isArray(citations)) { citations.forEach(citation => { citationsHTML += `<li><a href="${citation}" target="_blank">${citation}</a></li>`; }); } citationsHTML += '</ul>'; // Return a new JSON object containing the generated HTML snippet return [{ json: { citationsHTML } }]; ```

   Execution prompt · Source text。Perplexity node messages.message[0].content (system role)

   Copy prompt

   =You are a researcher in a business development team. Your job is to find as much research as you can about the prospect company. You must ensure your research is for the correct company and is highly accurate. Your research must always include what the prospect company does.

   Execution prompt · Source text。Perplexity node messages.message[1].content (user role)

   Copy prompt

   =
   Find as much info as you can about {{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.company }}. }} This is their website URL: {{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.company_website }}

4. Generate person and company profile

   Configure the Person + Company Profile OpenAI node: select o1-mini model (or substitute an available GPT model) and connect OpenAI API credentials. This node receives LinkedIn bio, last 30 days of posts (transformed by the Posts node), and Perplexity research results, then generates an HTML fragment containing personal profile, interests, unique facts, and company profile.

   Execution prompt · Source text。Person + Company Profile OpenAI node messages.values[0].content field

   Copy prompt

   =You are part of the business development team at Built by Abdul which is an AI Automation Agency.
   
   When a new leads book a consultation with Built by Abdul, your team researches the leads and provides the research + extracted insights to the consultants so they can read it prior to the consult. 
   
   Your job is to create the following sections from the research:
   
   - Personal Profile: The personal profile is a one paragraph summary of the person (lead) extracted from researching their LinkedIn profile.
   - Interests: In this section you must identify a few of the leads interests from their most recent LinkedIn posts + their LinkedIn profile. 
   - Unique Facts: In this section you must extract at least 2 unique facts about the lead that tells them you've really put time to research who they are. These facts should be things that are unique to the lead and out of the ordinary.
   - Company Profile: The company profile is a one paragraph summary of the company the person represents, extracted from researching their company LinkedIn profile and available information on the web. 
   
   Your output must be in HTML, following this format: 
   
   Person Profile (Heading 2)
   [Details]
   
   Company Profile (Heading 2)
   [Details]
   
   Interests (Heading 2)
   [List of interests in dot point format]
   
   Unique Facts (Heading 2)
   [List of unique facts in dot point format]
   
   
   - Don't wrap the output in ```html``` since the output will go into the middle of another HTML document. Just make sure its in HTML language.
   
   Create this output for the following research: 
   
   LinkedIn Profile About:
   {{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.about }}
   
   Most Recent LinkedIn Posts: 
   {{ $('Scrape Profiles + Posts - Relevance AI').item.json.last_30_days_posts_transformed.toJsonString() }}
   
   Web Research: 
   {{ $('Perplexity').last().json.choices[0].message.content }}

5. Generate pain points and high-ROI suggestions

   Configure the Pain Points + Solutions OpenAI node: also uses o1-mini model. This node receives the profile summary from the previous step and Perplexity company research, then outputs an HTML fragment with a pain-point evidence table and 5 highest-ROI automation opportunities. Note: replace "Built by Abdul" and "Kamexa" in the prompt with your actual company and service names.

   Execution prompt · Source text。Pain Points + Solutions OpenAI node messages.values[0].content field

   Copy prompt

   =You are part of the business development team at Built by Abdul which is an AI Automation agency that provides the following services: 
   
   -AI-powered personalized lead generation systems
   -Automated ClickUp CRMs and project management workflows
   -AI Content agents that repurpose and schedule high-volume content
   -Client onboarding automations (proposals, forms, contracts, etc.)
   -Other AI workflows that save founders dozens of hours per month
   
   Built by Abdul's consultants are the first point of contact for a new lead. So when a new lead is interested in their solutions, they book a free call with their consultants. The consultants are then responsible for exploring the highest ROI opportunities for automation.
   
   Once a call is booked, your team does in depth research on the lead so that the consultants can read through it in preparation for the consultation. 
   
   As part of the business development team you are provided with the following research: 
   
   - LinkedIn Profile: This is the result of scraping the leads LinkedIn profile. This will give you the leads bio, experiences, education and company description.
   - LinkedIn Posts: This gives you the leads LinkedIn posts over the past 30 days. 
   - Company Google Search: This gives you the research found from searching the leads company on Google and their website. 
   - Reviews: This gives you the most recent bad reviews the leads company received on TrustPilot.
   
   
   Your job is to analyse the research and extract following information: 
   
   - Pain Points: By looking through the companies negative reviews and also considering their line of business and the leads position in the company, you must identify the biggest pain points the leads company could be facing. 
   - Solutions to Pain Points: In this section you must come up with solutions Built by Abdul can offer to solve each of the pain points completely. 
   - Highest ROI Opportunities: Considering the info you have about the lead and his company, you must come up with the 5 highest ROI opportunities that Built by Abdul can offer them. These automations have to be realisitic, yet extremely high ROI. Sort them in order of best opportunity at the top of the list.  
   
   The insights you extract must be in HTML format as they will be going in the middle of a HTML document. 
   
   Please use the following output format: 
   
   Opportunities (Heading 1)
   
   Pain Points and Solutions: (Heading 2)
   [Table with 3 columns: 
   1. Pain Point: Explains the pain point theyre facing. 
   2. Evidence: Explains why we think this is the case. 
   3. Solution: Explains the solution to the pain point by Kamexa.
   Each row is a new pain point.]
   
   Opportunities: (Heading 2)
   [5 Highest ROI automation opportunities for the leads company sorted with the best solution at the top. This must be in dotpoint format:
   - Opportunity#1 Name: Details
   - Opportunity#2 Name: Details
   ...
   
   - Don't wrap the output in ```html``` since the output will go into the middle of another HTML document.
   
   Someone just booked a call. Here's the research your team found. Create the output as per the above requirements: 
   
   LinkedIn Profile Scraped Summary in HTML format: 
   {{ $('Person + Company Profile').last().json.message.content }}
   
   Web Research Results for Leads Company: 
   {{ $('Perplexity').last().json.choices[0].message.content }}

6. Assemble HTML report

   The Create HTML Report node uses the HTML template engine to combine all upstream outputs (profile, pain points, LinkedIn details, education, experience, posts, research, citations) into a complete HTML document. The template contains CSS styling and n8n expression syntax referencing each upstream node's json output. Verify that all upstream node names match the $node['NodeName'] references in the template. Email HTML template: ```html <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Consultant Research Report</title> <style> body { font-family: Arial, sans-serif; margin: 20px; line-height: 1.6; color: #333; } h1, h2, h3 { color: #222; } .section { margin-bottom: 40px; } .profile-details p { margin: 5px 0; } table { width: 100%; border-collapse: collapse; margin-top: 10px; } th, td { border: 1px solid #ddd; padding: 8px; vertical-align: top; } th { background-color: #f2f2f2; } .card { border: 1px solid #ddd; padding: 10px; margin-bottom: 10px; border-radius: 4px; } a { color: #1a0dab; text-decoration: none; } a:hover { text-decoration: underline; } /* New styles for the header images container */ .header-images { display: flex; margin-bottom: 20px; } .header-images img { width: 50%; height: auto; max-width: 300px; } </style> </head> <body> <!-- Header Images --> <div class="header-images"> <img src="{{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.profile_image_url }}" alt="Profile Picture"> <img src="{{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.company_logo_url }}" alt="Company Logo"> </div> <!-- Analysis & Key Facts --> <div class="section" id="analysis"> <h1>Analysis & Key Facts</h1> {{ $('Person + Company Profile').last().json.message.content }} {{ $('Pain Points + Solutions').last().json.message.content }} </div> <!-- Detailed Research Report --> <div class="section" id="research-report"> <h1>Research Report</h1> <!-- LinkedIn Profile Details --> <div id="linkedin-profile" class="profile-details"> <h2>LinkedIn Profile Details</h2> <p><strong>Name:</strong> {{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.full_name }}</p> <p><strong>Headline:</strong> {{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.headline }}</p> <p><strong>Location:</strong> {{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.location }}</p> <p><strong>About:</strong> {{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.about }}</p> <p><strong>City:</strong> {{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.city }}</p> <p><strong>Country:</strong> {{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.country }}</p> <p><strong>Job Title:</strong> {{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.job_title }}</p> <p><strong>Company:</strong> {{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.company }}</p> <p><strong>Company Description:</strong> {{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.company_description }}</p> <p> <strong>Company Website:</strong> <a href="{{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.company_website }}"> {{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.company_website }} </a> </p> <p><strong>Industry:</strong> {{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.company_industry }}</p> <p> <strong>LinkedIn URL:</strong> <a href="{{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.linkedin_url }}"> {{ $('Scrape Profiles + Posts - Relevance AI').item.json.linkedin_profile_details_data.linkedin_url }} </a> </p> </div> <!-- Education --> <div id="education"> <h2>Education</h2> <table> <thead> <tr> <th>School</th> <th>Degree</th> <th>Field of Study</th> </tr> </thead> <tbody> {{ $node["Education"].json.educationTable }} </tbody> </table> </div> <!-- Experience --> <div id="experience"> <h2>Experience</h2> <table> <thead> <tr> <th>Company</th> <th>Title</th> <th>Date Range</th> <th>Location</th> </tr> </thead> <tbody> {{ $node["Experiences"].json.experiencesTable }} </tbody> </table> </div> <!-- Recent LinkedIn Posts --> <div id="linkedin-posts"> <h2>Recent LinkedIn Posts</h2> {{ $node["Posts"].json.postsHTML }} </div> <!-- Google Research Analysis --> <div id="google-research"> <h2>Google Research Analysis</h2> <p>{{ $node["Perplexity"].json.choices[0].message.content.removeMarkdown().removeMarkdown() }}</p> </div> <!-- Citations --> <div id="citations"> <h2>Citations</h2> {{ $node["Citations"].json.citationsHTML }} </div> </div> </body> </html> ```

7. Send email report

   Configure the Email Report Gmail node: connect Gmail OAuth2 credentials, change sendTo from [email protected] to your receiving email. The subject uses an n8n expression to dynamically insert the prospect's full name, and the message references the upstream HTML node's output. Manually test once before enabling auto-send. [First-test safety] Before the first test, set Email Report sendTo to a test inbox, or temporarily disconnect Email Report from Create HTML Report, or gate sends to the test address only.