A practical guide to extraction confidence scores

Confidence scores in document extraction can help organizations route high-confidence values automatically and send lower-confidence results for human review, with thresholds selected and validated against representative labeled datasets to meet specified precision targets. Using 370 ExtractBench documents containing 841,895 ground-truth fields, the evaluation found that LlamaParse Agentic Plus reached 97% precision at a confidence cutoff of about 0.7717, automatically accepting 559,666 correct values while sending 211,401 values for review, equivalent to 66.48% recall. The comparison reported that at the same precision target, Reducto Deep Extract achieved 33.23% recall and Extend Review Agent had no qualifying cutoff, while Agentic Plus provided scores for all returned values and substantially finer score granularity than the alternatives. The analysis emphasizes that useful confidence scoring depends on accuracy ranking, score coverage, and granularity, and recommends separate thresholds for fields with different error costs, explicit handling of unscored or missing values, and periodic validation as production document characteristics change. Across all acceptance levels, Agentic Plus recorded the lowest reported AUGRC, a measure of unreviewed-error risk, at 15.0 per thousand evaluated rows, alongside a measured cost of 8.53 cents per page.

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