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really cool launch.
Brilliant use of Decision models.
@NaceAI built a document reader that proofreads itself. After it turns a PDF or scan into text, a small model called Drex compares each block with the original page image and sends only the mismatches back for correction, all for $7 per 1,000 pages.
Standard OCR flattens tables and drops ticked boxes, while frontier models help but get expensive at volume.
Drex instead answers narrow questions, such as whether an OCR line matches the image, in about 50 milliseconds for about $2 per million answers.
Because only doubted text gets rewritten, median correction time per page fell 28% to 38%, and failed fixes revert to the original reading.
The system scores 0.834 on the Parse Index, beating GPT-6 Astra (low) at 0.813 while costing $7 per 1,000 pages against $70.57.
Introducing Drex for Document Processing 📑
Decision-powered parsing that catches errors before they reach your agents.
→ #1 on our parsing leaderboard
→ Beats GPT-6 Astra, Claude Opus 4.7 & every parser tested
→ 38% faster corrections on RealDocBench
Try it: https://t.co/eTUlO9j0wU
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