A pipeline that converts inconsistent scanned records into validated structured data. Classical ML handles high-volume classification cheaply; LLMs are reserved for the ambiguous minority where they genuinely add value.
Scanned documents arrive in unpredictable layouts, mixed quality and inconsistent terminology. Manual review is slow and error-prone, and a single-model approach either overfits or costs too much per page.
- —Substantial reduction in manual document handling
- —Consistent, schema-valid structured output
- —Ambiguous cases routed intelligently instead of guessed
Classical ML first
TF-IDF with character-level features and a LinearSVC classifier handles the bulk of routing at a fraction of LLM cost and latency.
Calibrated confidence
Confidence thresholds decide routing: high confidence auto-processes, low confidence escalates to LLM validation or human review.
Layout as a feature
Textract layout and table blocks feed extraction rather than being discarded, which stabilizes field-level accuracy across templates.
Schema-validated output
Every document exits as JSON validated against a schema, so downstream systems never ingest malformed records.
Handwriting and low-quality scans
Preprocessing and confidence-aware fallbacks prevent noisy OCR from silently corrupting structured output.
Terminology variance
Semantic matching handles synonyms and abbreviations that exact-match rules miss.
Silent failure
Validation layers and rule-based safeguards surface anomalies instead of passing them downstream.
- →Not every problem needs an LLM — hybrid pipelines win on cost and reliability
- →Confidence calibration is what makes automation trustworthy
- →Validation layers pay for themselves the first time a bad scan appears