Every earnings call transcript is full of signals.

Management tone. Guidance confidence. How often "uncertainty" gets mentioned.

I built a workflow that can score transcripts automatically with AI.

Using OpenAI and a single Jupyter notebook, you can extract structured data from hundreds of transcripts:

→ Management confidence scores

→ Quarter-over-quarter tone shifts

→ Risk language density

→ Guidance specificity

The output is a clean CSV. One row per transcript.

You can screen an entire sector's earnings calls in an afternoon.

Some might say that processing thousands of documents with OpenAI is expensive. But there's a OpenAI API trick that cuts your costs in half and sidesteps the rate limits that crash most scripts.

It's perfect for exactly this kind of analysis.

Want to test it yourself? My notebook loads a dataset of 20,000 S&P 500 earnings call transcripts directly from Hugging Face. No envrionment set up required—just run it in your browser.

Full walkthrough + the cost-cutting trick in my latest post →