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Datapilot V1

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Datapilot V1

Datapilot V1

The AI Company & Topic Researcher is a sophisticated web scraping and analysis tool designed for competitive intelligence, market research, and technical deep dives. It automates the entire research pipeline from data collection to AI-powered strategic analysis.

Pricing

from $0.01 / 1,000 results

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Developer

Satyam Gupta

Satyam Gupta

Maintained by Community

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5

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10 months ago

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🧠 AI Company & Topic Researcher


📋 Table of Contents


🎯 Overview

The AI Company & Topic Researcher is a sophisticated web scraping and analysis tool designed for competitive intelligence, market research, and technical deep dives. It automates the entire research pipeline from data collection to AI-powered strategic analysis.

What Makes It Unique?

  • Dual Research Modes: Switch between company analysis and general topic research
  • Multi-Source Aggregation: Combines official websites, news, reviews, social mentions, and competitive data
  • AI-Powered Insights: Uses Groq's Llama 3.3 70B model for deep strategic analysis
  • Structured Output: Generates SWOT analysis, pricing strategies, market positioning, and actionable recommendations
  • Zero Configuration: Works out-of-the-box with sensible defaults

🚀 Key Features

🔍 Intelligent Web Scraping

  • Smart URL Discovery: Automatically finds and categorizes important pages (pricing, features, about, blog, etc.)
  • Static & Dynamic Content: Handles both traditional HTML and JavaScript-rendered content
  • Multi-Page Extraction: Scrapes homepage, pricing, features, documentation, blog posts, and more
  • Anti-Detection: Uses realistic browser fingerprints and human-like behavior patterns

📊 Comprehensive Data Collection

Company Analysis Mode

✅ Official website content and metadata
✅ Pricing tiers and monetization models
✅ Product features and capabilities
✅ News articles and press releases
✅ Customer reviews from G2, Trustpilot, Capterra, Product Hunt
✅ Social media mentions (LinkedIn, Twitter/X, Reddit)
✅ Competitor identification and analysis
✅ Blog posts and content marketing insights

General Topic Research Mode

✅ Top authoritative sources discovery
✅ Technical documentation and guides
✅ Recent developments and news
✅ Community discussions and sentiment
✅ Implementation examples and use cases

🤖 AI-Powered Analysis

The actor uses Llama 3.3 70B Versatile via Groq API to generate:

For Companies:

  • Company Overview (description, industry, target market, value proposition)
  • Product Analysis (core products, key features, technology stack)
  • Pricing Strategy (pricing model, positioning, monetization)
  • Market Position (market strength, brand reputation, growth trajectory)
  • Competitive Landscape (main competitors, threats, differentiation)
  • Customer Sentiment (overall sentiment, praise/complaints, NPS)
  • SWOT Analysis (top 5 strengths, weaknesses, opportunities, threats)
  • Strategic Insights (growth potential, investment worthiness)
  • Risk Assessment (risk score 0-100, key risk factors)
  • Executive Summary (4-6 paragraph comprehensive overview)

For Topics:

  • Topic Overview (definition, key concepts, importance)
  • Technical Deep Dive (how it works, core components, architecture)
  • State of the Art (current trends, recent advancements, leading players)
  • Pros & Cons (advantages, disadvantages, implementation challenges)
  • Use Cases (real-world applications across sectors)
  • Future Outlook (predictions, research directions, disruption potential)
  • Learning Resources (prerequisites and next steps)

📥 Installation

Prerequisites

  • Python 3.11+
  • Apify Account (for running as an Actor)
  • Groq API Key (optional, fallback provided)
  1. Import the Actor to your Apify account
  2. Configure inputs via the Apify Console
  3. Run and view results in the dataset

Option 2: Local Development

# Clone the repository
git clone https://github.com/yourusername/ai-company-researcher.git
cd ai-company-researcher
# Install dependencies
pip install -r requirements.txt
# Install Playwright browsers
playwright install chromium
# Set environment variables (optional)
export GROQ_API_KEY="your_groq_api_key_here"
# Run locally
python main.py

🎮 Usage

Using Apify Console

  1. Navigate to the Actor in your Apify Console
  2. Fill in the input form:
    • Target Name: Enter company/product name or research topic
    • Research Mode: Select "Company Analysis" or "General Topic Research"
    • Groq API Key: (Optional) Enter your key for higher rate limits
  3. Click Start
  4. View results in the Dataset tab once complete

Using Apify API

// Node.js Example
const ApifyClient = require('apify-client');
const client = new ApifyClient({
token: 'YOUR_APIFY_TOKEN',
});
const run = await client.actor('your-actor-id').call({
company_name: 'Stripe',
mode: 'Company Analysis'
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);
# Python Example
from apify_client import ApifyClient
client = ApifyClient('YOUR_APIFY_TOKEN')
run = client.actor('your-actor-id').call(run_input={
'company_name': 'Stripe',
'mode': 'Company Analysis'
})
dataset = client.dataset(run['defaultDatasetId']).list_items().items
print(dataset)

Local Execution

import asyncio
from main import EnhancedCompanyResearchScraper
async def main():
scraper = EnhancedCompanyResearchScraper(
company_name="Notion",
groq_api_key="your_key_here" # Optional
)
results = await scraper.run_complete_research()
# Save outputs
scraper.save_to_json(results, "notion_research.json")
scraper.save_analysis_text(results, "notion_analysis.txt")
asyncio.run(main())

📝 Input Schema

Required Fields

FieldTypeDescriptionExample
company_namestringName of company, product, or topic"Stripe", "React Framework"
modeselectResearch type"Company Analysis" or "General Topic Research"

Optional Fields

FieldTypeDescriptionDefault
groq_api_keystringYour Groq API keyUses fallback key

Input Examples

Company Analysis:

{
"company_name": "Figma",
"mode": "Company Analysis"
}

Topic Research:

{
"company_name": "Graph RAG",
"mode": "General Topic Research"
}

📤 Output Schema

Output Structure

{
"metadata": {
"research_date": "2024-12-06T10:30:00Z",
"company_name": "Stripe",
"scraper_version": "2.0",
"analysis_model": "llama-3.3-70b-versatile"
},
"raw_data": {
"website_data": { "homepage": {}, "pricing": {} },
"pricing_tiers": [
{
"name": "Starter",
"price": "$29",
"billing_period": "month"
}
],
"features": [
{
"name": "Payment Processing",
"description": "Accept payments worldwide"
}
],
"news_articles": [],
"reviews": [],
"social_mentions": [],
"competitors": []
},
"ai_deep_analysis": {
"company_overview": {
"description": "Stripe is a financial infrastructure platform...",
"industry": "Financial Technology",
"target_market": "Online businesses and platforms",
"value_proposition": "Easy-to-integrate payment processing",
"business_model": "Transaction fees and subscriptions"
},
"product_analysis": {},
"pricing_strategy": {},
"market_position": {},
"competitive_landscape": {},
"customer_sentiment": {},
"strengths_weaknesses": {
"top_5_strengths": ["Best-in-class API", "Strong brand", "..."],
"top_5_weaknesses": ["Premium pricing", "Complex setup", "..."],
"opportunities": ["B2B expansion", "Crypto integration"],
"threats": ["Regulatory changes", "Increased competition"]
},
"strategic_insights": {},
"actionable_recommendations": [
"Consider Stripe for global payment processing",
"Evaluate pricing for low-volume businesses"
],
"executive_summary": "Stripe is a leading financial infrastructure...",
"risk_score": {
"overall_score": "35",
"risk_factors": ["Regulatory compliance", "Account stability"],
"confidence_level": "high"
}
},
"metrics": {
"pages_scraped": 8,
"pricing_tiers_found": 4,
"features_identified": 15,
"news_articles": 10,
"reviews_collected": 8,
"competitors_identified": 12
}
}

🔬 Research Modes

1. Company Analysis Mode

Best For:

  • Competitive intelligence
  • Investment research
  • Partnership evaluation
  • Product comparison

Data Collected:

  • Website content, pricing, features
  • News and announcements
  • Customer reviews and ratings
  • Social media presence
  • Competitor landscape

AI Analysis:

  • Business model breakdown
  • Market positioning
  • SWOT analysis
  • Customer sentiment
  • Risk assessment

2. General Topic Research Mode

Best For:

  • Technology trends
  • Academic research
  • Learning new concepts
  • Industry analysis

Data Collected:

  • Authoritative sources
  • Latest developments
  • Community discussions
  • Implementation examples

AI Analysis:

  • Technical deep dive
  • State-of-the-art trends
  • Pros and cons
  • Future outlook
  • Learning pathway

🛠️ Technical Stack

ComponentTechnologyPurpose
RuntimePython 3.11+Core execution environment
Web ScrapingPlaywrightHeadless browser automation
AI AnalysisGroq APILlama 3.3 70B inference
Actor FrameworkApify SDKScalable execution & storage
ParsingBeautifulSoup4, regexContent extraction

Dependencies

apify>=1.0.0
playwright>=1.40.0
groq>=0.4.0
requests>=2.31.0
rich>=13.0.0

📊 Examples

Example 1: Analyze a SaaS Company

{
"company_name": "Notion",
"mode": "Company Analysis"
}

Output Highlights:

  • 10 pages scraped
  • 3 pricing tiers identified
  • 25+ features extracted
  • 15 news articles collected
  • Risk Score: 25/100 (Low Risk)
  • Market Position: Strong growth trajectory

Example 2: Research a Technical Topic

{
"company_name": "Retrieval Augmented Generation",
"mode": "General Topic Research"
}

Output Highlights:

  • 3 authoritative sources analyzed
  • Technical architecture explained
  • Current trends identified
  • 5 use cases documented
  • Future outlook: High disruption potential

🐛 Troubleshooting

Common Issues

IssueSolution
"Company website not found"Verify company name is correct; use full name
"AI analysis failed"Check Groq API key validity; may be rate limited
"No pricing found"Pricing may be on request; check raw_data
Empty competitor listCompany may be too niche
Slow executionNormal; typically takes 2-5 minutes

Performance

  • Total Runtime: 2-5 minutes
  • Pages Scraped: 5-10 per company
  • Output Size: 50-200 KB JSON
  • Memory Usage: 500MB - 1GB

💡 Best Practices

  1. Use Specific Names: "Stripe Payment Processing" > "Payment Company"
  2. Check Domain Accuracy: Ensure the company has a website
  3. Use Custom API Key: For higher rate limits
  4. Review Outputs: AI analysis should be verified for critical decisions

📸 Visual Overview

Screenshot 2025-12-06 144028 Screenshot 2025-12-06 144005

📜 License

This project is licensed under the MIT License.


🎖️ Built For Apify Hackathon 2025

This Actor was developed as part of the Apify Hackathon 2024, showcasing the power of combining web scraping with advanced AI analysis for market intelligence.