This content originally appeared on DEV Community and was authored by Ranjan Dailata
This is a submission for the AI Agents Challenge powered by n8n and Bright Data
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Introduction
This workflow is designed to perform deep company research by combining Bright Data scraping, Google Search enrichment, and AI-driven interpretation with Google Gemini.
At its core, the workflow focuses on building a comprehensive Deep Research Report by integrating three critical data streams:
- CrunchBase → Funding, acquisitions, size, and market positioning.
- Glassdoor Company Info → Company overview, general facts, and employer branding signals.
- Glassdoor Company Reviews → Employee sentiment, leadership feedback, and culture insights.
The extracted data is normalized, enriched, and finally synthesized into a human-readable Markdown report by Google Gemini. This ensures that raw data (e.g., JSON dumps from Bright Data) is transformed into strategic insights with clear narratives.
The workflow is particularly useful for:
- Competitive Intelligence → Compare multiple companies on funding, growth, and employee sentiment.
- Investor/VC Due Diligence → Validate funding data alongside employee perspectives.
- Market Research → Understand brand perception and workforce satisfaction in target industries.
- Recruitment Insights → Position employer branding by combining company facts with real employee experiences.
By merging CrunchBase’s hard business metrics with Glassdoor’s cultural insights, the workflow produces a well-rounded research report that supports both quantitative and qualitative analysis.
Use Cases & Real-World Applications
1. Competitive Intelligence
- Gather data on competitors from CrunchBase and Glassdoor.
- Analyze company size, funding, employee sentiment, and growth trajectory.
- Build benchmark reports for strategic decision-making.
2. Recruitment & Employer Branding
- Extract Glassdoor reviews to understand employee sentiment.
- Present AI-enhanced summaries of culture, leadership, and employee satisfaction.
- Aid recruiters in positioning companies more effectively when pitching roles.
3. Investor/VC Due Diligence
- Pull CrunchBase funding data and combine it with Glassdoor reviews.
- Generate AI-curated summaries of risks, strengths, and employee perspectives.
- Accelerate investment decision-making with reliable research reports.
4. Sales Intelligence / Account Research
- Enable B2B sales teams to perform deep prospect analysis.
- Extract data from public search, Glassdoor, and CrunchBase before outreach.
- Provide sellers with AI-driven one-pagers on target accounts.
Workflow Overview
The workflow follows these main steps:
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Chat Trigger
- Starts when a user sends a company name via chat.
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Set Input Fields
- Captures the company name and prepares it for downstream nodes.
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AI Agent (Google Gemini)
- Constructs Bright Data search queries.
- Identifies and retrieves relevant URLs (Glassdoor, CrunchBase, reviews).
- Uses Structured Output Parser to normalize URLs into JSON format.
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Bright Data Extraction
- Glassdoor Company Info → General company overview.
- Glassdoor Reviews → Employee sentiment and reviews.
- CrunchBase Data → Funding, size, acquisitions, market insights.
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Human-Readable Content Extraction (Glassdoor Reviews)
- Uses Google Gemini to convert raw reviews into natural Markdown summaries.
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Merge Responses
- Combines CrunchBase, Glassdoor overview, and review summaries into a single dataset.
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Deep Research Builder
- AI Agent generates a comprehensive research report in Markdown format.
- Includes Glassdoor insights, CrunchBase data, and AI-curated analysis.
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Respond to Chat
- Sends the research report back to the requester in real time.
Key Components
AI Agent (Gemini-powered):
Acts as the orchestrator for data retrieval, URL discovery, and query building.Bright Data Scraping Tools:
Handle web scraping and structured data extraction from CrunchBase and Glassdoor.Structured Output Parser:
Ensures the AI output is formatted as JSON for reliability.-
Gemini Chat Models:
- Used at three levels:
- Query building & orchestration (AI Agent).
- Human-readable review extraction.
- Deep research synthesis into Markdown.
n8n Merge Node:
Combines multi-source insights into a single structured object.
Output
The final output is a Markdown Deep Research Report containing:
- Company Overview (Glassdoor + CrunchBase data).
- Employee Sentiment Summary (Glassdoor reviews).
- Funding, Size, and Market Data (CrunchBase).
- AI-generated strategic insights (growth trends, risks, opportunities).
Deep Research Report Sample: Bright Data
1. Company Overview
Bright Data is a private company in the Internet & Web Services industry. Founded in 2014, it is headquartered in Netanya, Israel, with a presence in the US (indicated by country_code). The company employs between 201 to 500 employees and generates an estimated annual revenue of $100 to $500 million (USD).
- Website: www.brightdata.com
- Glassdoor Overview: https://www.glassdoor.com/Overview/Working-at-Bright-Data-EI_IE2267280.11,22.htm
2. Overall Employee Sentiment & Ratings
Bright Data holds an overall rating of 3.7 out of 5 stars based on 201 employee reviews. This indicates a moderately positive general sentiment among current and former employees.
3. Key Employee Experience Metrics
Detailed ratings for specific aspects of the employee experience are as follows:
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Career Opportunities: 3.5 / 5 stars
- Distribution of ratings:
- 1-star: 25 reviews
- 2-star: 9 reviews
- 3-star: 22 reviews
- 4-star: 27 reviews
- 5-star: 77 reviews
- Compensation & Benefits: 3.8 / 5 stars
- Culture & Values: 3.5 / 5 stars
- Senior Management: 3.5 / 5 stars
- Work/Life Balance: 3.8 / 5 stars
- Diversity & Inclusion Score: 3.8 / 5 stars (based on 141 counts)
4. Leadership and Business Outlook
Or Lenchner is identified as the CEO, with an approval rating of 75% (based on 93 ratings). This suggests a generally favorable view of the CEO among employees.
- Business Outlook: 72% of employees have a positive business outlook for Bright Data.
- Recommend to a Friend: 70% of employees would recommend working at Bright Data to a friend.
5. Recruitment & HR Insights
- Salaries: There are 221 reported salaries, indicating a good level of data availability for compensation benchmarking.
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Interviews: 58 interviews have been reported, with the following outcomes:
- Positive Experience: 45%
- Negative Experience: 33%
- Neutral Experience: 22%
- Benefits: 30 benefits have been reported by employees.
- Jobs: Currently, 20 job openings are listed.
6. Public Image & Presence
Bright Data maintains a public image through various events and internal activities. There are 40 photos available on Glassdoor, showcasing participation in events like the London Data Summit, Customer Success Conference, and E-commerce Berlin Expo, as well as team activities.
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Example Photos:
- London Data Summit 2023
- Customer Success Conference
- Ecommerce Berlin Expo
- Team at E-tail Ecommerce and Omnichannel Retail Conference 2023
- Recognition as a Top 50 Software Company EMEA
7. Conclusion
Bright Data appears to be a moderately well-regarded private company in the Internet & Web Services sector. Employees generally report a positive overall experience, with strong ratings for Compensation & Benefits, Work/Life Balance, and Diversity & Inclusion. While Career Opportunities, Culture & Values, and Senior Management have slightly lower but still respectable ratings, the CEO’s high approval and a positive business outlook suggest a stable and forward-looking environment. The company actively participates in industry events and has a robust recruitment presence.
This report can be:
- Delivered in-chat.
- Stored in Google Sheets, Notion, or a database.
- Exported as a PDF for reporting purposes.
Major Challenges and Solutions
Challenge 1: Fragmented Company Data Across Multiple Platforms
Problem: Company data was spread across Glassdoor, CrunchBase, and general Google Search results, each with unique structures and reliability levels.
Solution: Implemented Bright Data scrapers and standardized them with a Structured Output Parser in n8n, ensuring normalized JSON formats that could be merged seamlessly.
Challenge 2: Noise in Glassdoor Reviews
Problem: Raw employee reviews on Glassdoor often contained excessive noise, slang, and irrelevant commentary.
Solution: Applied Google Gemini summarization to extract human-readable insights from employee reviews, focusing on sentiment, recurring themes, and leadership perception.
Challenge 3: Identifying the Right URLs Dynamically
Problem: For each company, Glassdoor and CrunchBase URLs differ and may include duplicate or outdated results from search engines.
Solution: Used an AI Agent (Gemini) to construct smart search queries and parse Google/Bright Data results, filtering for the most relevant URLs with higher accuracy.
Challenge 4: Data Volume and API Rate Limits
Problem: Scraping multiple sources simultaneously risked hitting Bright Data or platform rate limits.
Solution: Introduced query batching and throttling inside n8n workflows, allowing efficient scaling without violating API restrictions.
Challenge 5: Generating a Cohesive Research Report
Problem: The extracted data was too fragmented, making it difficult for end-users to gain actionable insights.
Solution: Designed a Deep Research Builder Agent (Gemini) that merged data streams (Glassdoor insights, CrunchBase funding info, and employee sentiment) into a single Markdown-formatted research report.
Challenge 6: Maintaining Reliability in Multi-Step AI Orchestration
Problem: Errors in one step (e.g., failed Bright Data scrape) could break the entire workflow.
Solution: Implemented error handling and fallback prompts in Gemini Agents, ensuring that partial data could still generate a useful report instead of failing completely.
Download the Workflow
This content originally appeared on DEV Community and was authored by Ranjan Dailata