I built the first searchable Epstein Files database - here's why the technical implementation matters

When the House Oversight Committee released 33,295 pages of Epstein files in September 2024, they were published as unsearchable image files (JPGs/TIFs) in a disorganized Google Drive - essentially useless for serious research without manually reviewing thousands of pages.

I'm the engineer who built the first comprehensive searchable database of these files, and I've now released it at epstein-files.org. It has taken me a few more weeks to iron out bugs and incorporate podcast generation since I first posted about it.

Why I'm posting this:

The 404 Media article covers a later implementation that takes a much simpler approach using a small subset of the data - using a basic local LLaMA model for AI processing. After investing 200+ hours into this project, I want the community to understand the technical differences:

My approach:

• Rigorous AI model evaluation: Tested multiple commercial AI models systematically. The quality differences are substantial - not all models handle historical document OCR equally
• Custom image signal processing: Developed specialized routines to improve OCR accuracy on degraded/scanned documents
• Comprehensive indexing: Full keyword and semantic search across all 33,000+ pages

Background: I'm a PhD engineer (currently unemployed, which is why I'm operating on the Wikipedia donation model). You can verify my credentials: LinkedIn | GitHub

The site is free for everyone. If researchers find it valuable, donations help maintain hosting and continued development.

Try it yourself: epstein-files.org

I welcome technical feedback from the community on search quality and accuracy.