NLP
Document Intelligence & Entity Extraction
An NLP service that classifies incoming documents and extracts structured entities into downstream systems.
Schema-validated
Extraction
Confidence-gated
Review
Stream + batch
Ingestion
Architecture
Hover or tap any stage to see its role in the pipeline.
Data flow
Documents arrive via API or batch drop, text and layout are extracted, the classifier routes to a schema, extraction fills that schema with confidence scores, validation rules run, and low-confidence records are queued for human review.
Problem statement
High volumes of unstructured documents were manually triaged and keyed into core systems, creating backlog and transcription errors.
Business objective
Automatically classify document type and extract the fields required by downstream processes with a confidence score per field.
Solution overview
A pipeline combining layout-aware text extraction, a classifier for document routing, and LLM-based structured extraction validated against a strict schema.
AI / ML approach
Text and layout extraction, document-type classification, schema-constrained extraction with per-field confidence, and human review routing below threshold.
Model / LLM used
Transformer classifier + schema-constrained LLM extraction
Technology stack
PythonPyTorchFastAPIKafkaSQLAWS
Key capabilities
- Document type routing
- Schema-validated field extraction
- Per-field confidence scoring
- Human-in-the-loop review queue
Evaluation approach
- Field-level precision and recall against annotated samples
- Confidence calibration versus review outcomes
- Error analysis by document type
Challenges
- Noisy scans
- Highly variable layouts
- Silent extraction errors
Solution decisions
- Layout-aware extraction rather than plain text dumping
- Confidence thresholds tuned per field criticality
- Every automated extraction stored with its source span
Business impact
Shifted document handling from full manual keying to exception-only review, with traceability from every extracted field back to the source document.