Document AI with human-in-the-loop QA

Extraction pipelines fail gracefully when confidence scores route work to review queues—and when auditors can replay decisions.

Straight-through processing is the goal; honest uncertainty is the reality. Pipelines that auto-post low-confidence extractions create silent ERP corruption—expensive to unwind.

Confidence is a routing signal

Per-field scores drive behavior: auto-accept above threshold, highlight for spot-check in the middle band, full manual review below. Thresholds are calibrated per document type on held-out sets, not global defaults.

Review UX is part of the model

Reviewers need side-by-side source snippets, keyboard-first corrections, and reason codes. Feedback loops retrain classifiers and fine-tune prompts—closing accuracy gaps without blaming users.

• Immutable job history with model version and prompt hash.
• Replay exports for compliance reviews.
• Idempotent pushes into ERP/DMS with dead-letter queues.

HIPAA- and SOX-aware deployments add retention policies and break-glass access logging. Security reviewers see controls, not black boxes.

Document AI projects fail when accuracy is measured on clean PDFs while production sees phone photos, faxes, and rotated scans. Pipeline design must embrace messiness and human judgment.

Pipeline stages matter

Ingest → classify document type → detect layout → extract fields → validate business rules → route to review or ERP. Each stage emits confidence and timing metrics so bottlenecks are obvious.

Calibration beats global thresholds

A single 0.85 threshold across invoice types will either over-auto-accept utilities or under-auto-accept dense tables. Calibrate per class with precision/recall targets agreed with finance or ops stakeholders.

Throughput for reviewers

• Queue prioritization by SLA and dollar impact.
• Keyboard shortcuts and bulk actions.
• Side-by-side OCR overlay on source pixels.
• Reason codes feeding model improvement backlog.

Governance and retention

Define how long raw images and extracted JSON live, who can export, and how models are retrained on production corrections. Regulated clients need immutable audit trails—not spreadsheets of "who fixed what."

Triaxo document AI engagements ship with operator training and weekly accuracy reviews for the first month, so teams trust the system before straight-through rates climb.