What You'll Learn
Build a reliable system that automatically extracts case studies from any company website by combining deterministic code with AI analysis. Get structured data on customer outcomes, company types, and confidence scores—perfect for ICP research or finding lookalike customers.
Why This Matters
- ICP Intelligence: Case studies reveal who companies consider their best customers
- Pre-Call Prep: Understand prospect's target market before discovery calls
- Lookalike Research: Use extracted companies as seeds for finding similar prospects
Key Steps Covered
- SERP Query Construction – Build boolean search queries combining "case study", "testimonials", "customer success" with site: operator
- AI Classification – Use Claude to categorize results as individual stories vs hub pages
- Hub Page Extraction – Scrape hub pages and extract individual case study links
- Data Structuring – Output consistent schema: company, type, link, outcome, confidence
Tools & Integrations
- AirOps: Workflow orchestration and AI prompting
- SerperDev: Google search API for finding case studies
- Anthropic Claude: LLM for classification and extraction
Common Questions
Q: How do you ensure reliable output from AI? A: By combining deterministic code for data collection with AI for classification, then enforcing strict output schemas. The code handles SERP results and scraping, while AI handles the ambiguous task of determining if a link is actually a case study.
Q: Can this handle companies with hundreds of case studies? A: Yes. The workflow processes hub pages that contain links to individual case studies, allowing you to capture extensive case study libraries. Adjust the result limit based on your needs.
