Batch Document Processing
Not one document — forty thousand of them, overnight, with a report on what needs attention in the morning.
Batch document processing is the handling of documents in bulk — thousands to millions of files submitted as a job rather than one at a time — with the orchestration that scale requires: queuing, parallel execution across workers, progress tracking, retry of transient failures, and a completion report that separates clean results from exceptions. It is the mode of choice for migrations (digitizing an archive), periodic loads (month-end statements, annual policy renewals), and any back-office flow where documents accumulate and are processed on a schedule rather than on arrival.
Batch changes the engineering priorities relative to real-time processing. Latency per document stops mattering; throughput, cost per document, and completeness dominate. That opens optimizations unavailable to interactive flows: grouping similar documents so models and caches stay warm, routing bulk volume to cheaper compute or provider batch pricing tiers, scheduling around off-peak capacity, and amortizing fixed costs across the run. It also demands operational rigor that single-document processing hides — a job that dies at document 38,000 must resume, not restart; duplicate submissions must be idempotent; and a 2% failure rate, invisible anecdotally, is 800 documents that someone must see listed, categorized by failure reason, and routed to resolution.
The exception report is the quiet heart of a batch system. A run is not "done" when the happy path completes; it is done when every input is accounted for — succeeded, failed with a known cause, or queued for human review — and the failure categories feed back into pipeline improvement. Mature operations track batch health over time (failure rates by document type and source, throughput trends, review-queue volume), treating each run as both production output and a measurement of how well the document AI fits the population it serves.
Not how fast one document processes, but how many process per hour — the metric that governs cost at volume.
Every channel, one front door — email, scan, upload, API, fax — normalized into a stream the system can process.
The architecture for moments that can't wait — capture, read, and respond while the user is still there.
Proof Perimeter runs document AI inside your own perimeter — with a provenance record on every field.
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