LinkedIn Profile Deep Research with Insights + Bright Data Agent Analysis + n8n with Google Gemini

n8n and Bright Challenge: Unstoppable Workflow

This is a submission for the AI Agents Challenge powered by n8n and Bright Data

Download Workflow LinkedIn Profile Deep Research with Insights + Bright Data Agent Analysis with Google Gemini

What I Built:

The LinkedIn Deep Research + Gemini-Powered Workflow is an end-to-end automation pipeline designed to extract, analyze, and enrich professional data from LinkedIn profiles.

It integrates Bright Data Web Unlocker for reliable scraping, Google Gemini for advanced natural language reasoning and personality analysis, and Google Sheets for structured output. The workflow is orchestrated using n8n to ensure modular, scalable automation.

The system transforms a single LinkedIn URL into a comprehensive candidate dossier — including structured resume data, psychometric insights, and enriched web research.

The Problem

Recruiters, researchers, and analysts often struggle with:
Extracting structured, reliable data from LinkedIn profiles.
Understanding not just skills & experience, but also personality traits and work styles.
Enriching LinkedIn data with external insights from across the web.
Automating the process end-to-end without manual copy-paste or data cleaning.

This slows down talent intelligence, workforce research, and sales lead generation, where speed and insight depth are crucial.

The Solution

I built an automated workflow that turns a LinkedIn URL into a comprehensive candidate profile, enriched with AI-powered insights and personality analysis.

It uses:

Bright Data Web Unlocker → to reliably scrape LinkedIn content.
Google Gemini → to parse resumes, extract insights, and analyze the Big Five personality traits.
AI Agent with Google Search → to enrich profiles with external mentions, projects, and thought leadership.
Google Sheets → to store structured results for recruiters, analysts, or teams.

The result: A single pipeline that transforms raw LinkedIn URLs into structured, enriched candidate dossiers.

1. Introduction

This workflow automates deep research and analysis of LinkedIn profiles by combining:

Bright Data’s Web Unlocker for reliable scraping of LinkedIn profile content.
Google Gemini (PaLM API) for advanced resume parsing, insights extraction, and personality trait analysis.
n8n orchestration to structure the scraped data, transform it into JSON resumes, analyze psychological traits, and enrich insights with external search data.
Google Sheets integration to store structured outputs for further recruitment, research, or analytics use.

The pipeline ensures end-to-end automation: from extracting a LinkedIn profile → transforming content into structured JSON → analyzing skills & experience → performing Big Five personality analysis → validating JSON → enriching with Google searches → storing insights.

Combining a web scraping agent, an AI model, and an orchestration platform creates a powerful system for deep research and analysis of professional profiles. This goes far beyond simple data collection and allows for the generation of true insights.

The Role of Each Component

1. Bright Data Agent for Data Collection

A “Bright Data Agent” is a specialized web scraper designed to navigate and extract data from a specific website, in this case, LinkedIn. Unlike a manual process, these agents are built to handle technical challenges like CAPTCHAs and anti-bot measures at scale. For deep research, the agent is configured to scrape more than just a person’s name and title. It can be set up to collect:

Career History: A full account of past roles, companies, and the duration of each position.
Skills and Endorsements: A comprehensive list of skills, which often includes less-obvious abilities beyond job titles.
Education: Details on academic background, degrees, and areas of study.
Recommendations: Qualitative information about a person’s work ethic and professional standing.
Activity and Posts: The content and engagement metrics of a user’s recent posts and articles, which reveal their professional interests and thought leadership.

This rich, raw data is the foundation for the “deep research” part of the process.

2. Google Gemini for Generating Insights

The scraped data is raw and unstructured, but this is where Google Gemini’s advanced natural language processing (NLP) capabilities become invaluable. The data is fed into a Gemini model, which can be prompted to perform a series of sophisticated analyses:

Career Path Analysis: Gemini can analyze a person’s work history to identify trends in their career. For example, it can recognize a progression from an individual contributor to a leadership role or a specialization in a specific technology stack.
Skill Gap and Transferability Analysis: By looking at a person’s full career history and skills, Gemini can identify transferable skills and suggest potential career moves or skill gaps that need to be filled.
Sentiment and Tone Analysis: If a person’s posts and articles are scraped, Gemini can analyze the sentiment and tone of their public communication. This can provide insights into their professional demeanor and communication style.
Summary and Profile Generation: Gemini can take all the scraped data and generate a concise, insightful summary of a person’s professional journey, highlighting key accomplishments, expertise, and a potential “why.”

3. The Overall Workflow

This entire process is typically automated using a workflow automation tool like n8n. The steps would be:

A trigger event initiates the workflow (e.g., a new lead is added to a CRM).
The Bright Data agent is called via its API to scrape the full LinkedIn profile based on the provided URL.
The scraped JSON data is passed to a Gemini API call.
Gemini performs the deep analysis and returns a structured output with the generated insights.
This enriched data is then used to update a CRM, notify a team member, or populate a dashboard, turning raw data into actionable intelligence.

This automated pipeline is a significant step up from manual research, allowing businesses to perform deep, scalable analysis for tasks like targeted recruitment, competitive analysis, and strategic market intelligence.

2. Use-Cases & Real-World Applications

Recruitment & Talent Intelligence

Recruiters can extract structured resumes from LinkedIn profiles (work experience, skills, education).
Big Five personality insights provide a behavioral dimension for candidate fitment.
Results are stored in Google Sheets or pushed to ATS/CRM systems.

Competitive Intelligence

Map the profiles of industry experts, executives, or thought leaders.
Enrich profiles with search engine data for publications, talks, or affiliations.

Research & Organizational Studies

Academic researchers can use this workflow to study professional demographics, skill evolution, or personality correlations.
HR teams can benchmark workforce skills + personality traits.

Sales & Lead Generation

Extract decision-makers’ LinkedIn data and enrich with public-facing insights.
Segment leads by skills, interests, and personality traits for tailored outreach.

3. Workflow Overview

The workflow operates in four stages:

Input & Profile Scraping
- Extract LinkedIn URL from chat input.
- Use Bright Data Web Unlocker to scrape raw LinkedIn content.
Data Structuring & Parsing
- Parse profile into a JSON Resume schema (personal info, work, education, skills, projects, etc.).
- Sanitize raw JSON outputs.
Deep Insights & Analysis
- Run Big Five personality analysis (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism).
- Perform reasoning-based AI analysis for strengths, risks, and work style.
- Use AI Agent to combine LinkedIn data with Google Search enrichment.
Storage & Output
- Store enriched outputs in Google Sheets for sourcing pipelines, dashboards, or ATS integrations.
- Allow batch processing of multiple profiles via Looping & Aggregation nodes.

4. Node-by-Node Documentation

Input & Preprocessing

Profile Structuring & Resume Parsing

Personality & Behavioral Analysis

AI Agent Enrichment

AI Agent
- Multi-tool agent that executes:
- LinkedIn Data Extraction (via Bright Data).
- Google Search Analysis for external enrichment.
Bright Data Search Engine Data Extractor
- Scrapes SERPs for mentions of the candidate across the web.
Bright Data URL-based Web Data Extractor
- Extracts content directly from discovered URLs.
Google Gemini Chat Model for the AI Agent
- Provides reasoning and structured summary from agent actions.
Aggregate the Result & Merge
- Consolidates structured profile + personality + external enrichment.
Loop Over Items
- Supports batch processing for multiple profiles.

Storage & Output

Append or Update Row in Google Sheets
- Stores structured outputs into Google Sheets.
- Deduplication handled by matching on output column.
- Google Sheets acts as the central knowledge repository.

5. Real-World Applications

Talent Intelligence Workflow

Recruiter inputs a LinkedIn URL.
Workflow scrapes & parses profile → structures into JSON Resume → runs Big Five analysis → enriches with Google search data → saves to Google Sheets.
Output = comprehensive candidate dossier (skills + experience + psychology + online presence).

Research & Workforce Analytics

Batch LinkedIn URLs processed through loop.
Outputs JSON data into sheets for aggregate workforce skill & personality studies.

Enterprise ATS Integration

Extend Google Sheets → ATS/CRM sync.
Candidates are enriched with skills, projects, psychometrics, improving culture-fit matching.

6. Extensions & Next Steps

Pagination Support – Scrape multi-section LinkedIn pages (recommendations, endorsements).
Advanced Psychometric Models – Add MBTI, DISC, or leadership style analysis.
Cross-Platform Expansion – Parse GitHub, Twitter, and Medium in the same workflow.
Dashboard Integration – Visualize skills, personality distributions, and career paths in BI tools.
Direct ATS Integration – Push JSON Resumes directly into Greenhouse, Lever, Salesforce.