LLM + OCR for Submissions: Speed Without Losing Accuracy
You want speed, your auditors want accuracy. Here’s how to have both.
Architecture Overview
- OCR: extract structure and text from PDFs/scans/photos
- LLM: label fields, normalise values, score confidence
- Rules: validate (required/list/range/format) + cross sheet checks
- Ops: route low confidence to humans; log decisions
Confidence in Practice
| Bucket | Threshold | Treatment |
|---|---|---|
| High | ≥ 0.95 | Auto accept, log |
| Medium | 0.80–0.94 | Rule checks + quick human glance |
| Low | < 0.80 | Route to human, annotate missing info |
Common Failure Modes & Fixes
- Handwritten/photographed forms → ask brokers for digital, enhance pre processing
- Unseen templates → few shot examples; update patterns
- Inconsistent codes → import controlled lists and enforce
AI OCR & Submission Automation AI Triage
Related Reading
FAQs
Can we enforce our own field rules?
Yes. Required, format, lists and ranges are configurable; low confidence fields route to review with owner/severity.
Does this replace staff?
No. It removes rekeying so teams focus on selection, pricing and service quality.
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