Document Review Workflows
The human half of automation, designed properly — queues, context, and corrections that count.
Document review workflows are the structured processes through which humans verify, correct, and adjudicate AI-processed documents: the routing rules that decide what needs eyes, the queues that organize the work, the interfaces where reviewing happens, the escalation paths for what a first-line reviewer can't resolve, and the capture of every correction as feedback. They are the operational complement to automation — the design of the 5–20% of traffic that machines route to people — and their quality determines both the economics (seconds versus minutes per item) and the safety (whether review actually catches what confidence scoring sent it to catch) of the whole system.
Design centers on minimizing cognitive load per decision. Reviewers see the extracted value, the model's confidence, and the highlighted source region side by side — verification as a glance, not a document hunt; work is batched by similarity (a run of the same document type builds rhythm and pattern memory); keyboard-first interaction and sensible defaults compress the mechanics; and the queue itself is prioritized by business urgency and SLA, not arrival order. Escalation tiers match expertise to difficulty: data-level fixes at the first line, domain judgment (coverage interpretation, fraud suspicion, coding decisions) at specialist tiers, with the routing reasons preserved so each reviewer knows why the item reached them.
The workflow's second product is data. Corrections captured with full context — field, before/after values, source region, reviewer, time spent — feed model retraining, threshold recalibration, and the accuracy measurement that automation expansion depends on. Guarding that data's quality means guarding the process: agreement sampling across reviewers, adjudication for disagreements, and vigilance against automation bias — reviewers rubber-stamping model output under throughput pressure, which quietly converts the safety layer into a formality. Measured well (correction rates, per-reviewer consistency, queue aging, downstream error escapes), review workflows become what they should be: not automation's residue, but its calibration instrument.
Who sees what, in what order — the operational engineering behind the human-in-the-loop station.
The model does the reading; a person checks its work — but only where the model isn't sure.
The interface details that separate a five-second verification from a five-minute one.
Proof Perimeter runs document AI inside your own perimeter — with a provenance record on every field.
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