Built a hybrid resume parser using regex and OpenAI fallback
OpenAI's GPT-based resume parsing was accurate but costly (~$0.10–0.20 per resume).
Developed a custom regex engine to parse experience, education, and skills from most resumes. Used OpenAI only when the fallback failed or the resume was missing key sections.
Cut parsing costs by ~85% i.e reduced token usage from 4523 to 678 tokens. Maintained accuracy, and provided users with a fast, automated jumpstart
Smart cost optimizations happen at the integration layer, not just the API layer.