The exception queue, not the extraction engine, is what makes document automation safe to rely on. Here's how to design one that catches what actually matters and clears fast enough that someone keeps using it.
Key takeaways
Low-confidence extracted fields, unmatched vendor, customer or SKU records, and totals or quantities that don't reconcile against a purchase order or an expected range are the core categories worth routing to a person.
The harder design question is what to leave out. Flagging a field just because the system hasn't seen that exact value before, when the pattern itself is common, turns the queue into noise fast.
Too strict, and the queue floods with fields that were actually fine, training staff to rubber-stamp it instead of reading it. Too loose, and a real misread slips through untouched.
Getting the threshold right takes a few weeks of tuning against actual outcomes on your own documents, not a number set once on day one and left alone.
Route a misread field to whoever normally handles data entry; they can look at the source document and correct it in seconds. Route a record-matching or judgment exception to someone with account or business context instead.
That segmentation is what keeps the queue fast to clear. Without it, everything lands in one inbox and starts to feel like a second job nobody signed up for.
Every flagged exception should log what was extracted, who resolved it, and what the corrected value was, retained in line with your own CRA record-keeping obligations and mindful of PIPEDA and provincial privacy rules for where document images are stored.
That record is what lets an owner or manager answer who checked this months later, not just today, which is the whole point of putting a review step in the process in the first place.
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