Document Intelligence Pipeline with OCR, Classification, and Human Review
An operations team needed to extract structured data from PDFs and images reliably. We built an OCR and classification pipeline with confidence scoring, human review for uncertain cases, and analytics that improved throughput without sacrificing accuracy.
Confidential engagement. NDA available upon request.
65%
Manual Effort Reduction
92%
Field Accuracy
3
Seconds per Doc
8
Weeks to Pilot
About the Client
Industry
Operations
Company Size
Operations team within a larger org
Background
A team processing large volumes of documents for onboarding and compliance. Manual entry was slow, inconsistent, and expensive.
What the Pipeline Needed to Handle
Document variability
Formats differed widely and images were often low quality or rotated.
Accuracy requirements
Certain fields required high confidence and auditability.
Workflow integration
The system had to plug into existing tools and support human review.
Performance and cost
Processing needed to be fast and cost effective at scale.
The Mission
Automate document extraction with high accuracy and safe fallbacks, reducing manual effort while preserving auditability through review workflows.
How We Approached It
01. Data and schema design
Week 1 to 2- Document taxonomy and field schema definition
- Ground truth labeling approach
- Baseline OCR evaluation
- Workflow mapping for human review
02. Pipeline build
Week 3 to 6- OCR and classification models
- Confidence scoring and routing rules
- Review UI integration and feedback capture
- Batch processing and retry strategy
03. Pilot and tuning
Week 7 to 8- Pilot rollout and monitoring
- Error analysis and retraining
- Cost tuning and throughput improvements
- Documentation and handoff
Vulnerabilities Discovered
0
CRITICAL
1
HIGH
3
MEDIUM
0
LOW
Low quality scans reduced OCR reliability
Skew, blur, and poor contrast required preprocessing and stronger fallback rules.
Skew, blur, and poor contrast required preprocessing and stronger fallback rules.
Ambiguous templates needed taxonomy improvements
Several templates were similar, requiring better classification signals to route correctly.
Several templates were similar, requiring better classification signals to route correctly.
Confidence thresholds needed tuning
Initial thresholds either over routed to review or allowed too many uncertain extractions.
Initial thresholds either over routed to review or allowed too many uncertain extractions.
Feedback loop was missing
Review corrections needed to be captured to improve the model over time.
Review corrections needed to be captured to improve the model over time.
How We Fixed It
Preprocessing and routing
Added preprocessing steps and confidence based routing to human review for uncertain fields.
Human in the loop workflow
Integrated review steps and captured corrections to improve model performance over time.
Monitoring and analytics
Implemented metrics for accuracy, throughput, and review rates to guide iteration.
Measurable Outcomes
The pipeline reduced manual entry while maintaining accuracy through review workflows and continuous improvement.
65%
Manual Effort Reduction
92%
Field Accuracy
3
Seconds per Doc
8
Weeks to Pilot
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