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🤖 Automated Company Profiling & Tagging Using Open-Source LLM Technologies

🏢 Client Overview

Our client, a B2B technology solutions provider, needed a scalable way to gather, categorize, and summarize data from thousands of prospect websites.
The goal: Equip the sales team with instant, organized company insights for effective outreach and segmentation.

🚨 The Challenge

Sales operations were stuck in a manual, time-intensive loop:

• 🔍 Visiting websites one by one
• 📋 Scraping and organizing info
• 🏷️ Manually tagging companies by industry, service, and size
• 📝 Writing readable summaries for the sales team

As the number of prospects grew, this method became unsustainable.
They needed an automated pipeline to turn unstructured site data into usable sales insights.

⚙️ Our Solution

Atlas Inspire built a custom AI-powered system to automate the entire profiling and tagging process — all using open-source LLM technologies.

Key Components:

• 🌐 Website Scraping: Automated content extraction via Selenium & BeautifulSoup
• ✨ Text Processing: Used NLTK & SpaCy for NLP, entity recognition, and keyword extraction
• 🧠 LLM-Based Tagging & Summarization:
  • Implemented Llama, Ollama, and LangChain to:
    • Assign industry and service tags
    • Generate readable summaries for each company
• 🔁 End-to-End Pipeline: A robust Python-based workflow for fully automated operation

📈 Technologies Used

• Llama
• Ollama
• LangChain
• Python
• Selenium
• BeautifulSoup
• NLTK
• SpaCy

✅ The Impact

• ⏱️ 90% reduction in manual effort
• 📄 High-quality, consistent company summaries delivered to sales team
• 🎯 Improved segmentation and CRM targeting
• 💸 Fully open-source solution — no recurring license fees

This solution helped the client scale prospecting without sacrificing personalization or data quality — turning raw web data into actionable sales intelligence.