with jev, everyone is understanding the importance of calibrated confidence scores for discrete decision making we've taken that approach one-step further and created grounded confidence scores for general schema-guided document extraction: ✅ this includes primitive types like bool, int, and float. the numbers don't have to be bounded ✅ this also includes free-form text extraction ✅ each extracted value also carries a bounding box directly back into the source document calibrated confidence scores are extremely important for human review. By setting a threshold, you can let a human reviewer audit the lower confidence values while automating the extraction of higher confidence values. If you have needs for large scale doc extraction, come check it out: Sign up to LlamaParse here: