Case Study 04 . AI Integration
DocuAI
An AI-powered document operations pipeline that transformed high-volume manual processing into an automated workflow.
Problem
Document processing backlog
Teams were manually reviewing thousands of files each day, creating delays and inconsistent handling.
Classification errors
Rule-only systems failed on edge cases, causing routing mistakes and repeat review effort.
No production visibility
The prior setup lacked confidence metrics and monitoring to evaluate model quality in real conditions.
Solution
Hybrid extraction pipeline
BR7 combined OCR, NLP post-processing, and model confidence checks for reliable field extraction.
Intelligent routing layer
Documents were auto-classified and routed with fallback handling for low-confidence cases.
Quality monitoring loop
We added continuous evaluation metrics to track drift and prioritize retraining opportunities.
Architecture
Ingestion
Batch and API-based document intake with preprocessing.
AI processing
Model inference pipeline with confidence scoring and validation rules.
Routing and orchestration
Automated downstream routing with human-in-the-loop fallback.
Monitoring
Quality dashboards, exception logging, and model performance tracking.
Measured results
97%
Classification accuracy
Measured on production-like validation sets.
10k+
PDFs processed daily
Sustained with stable throughput under peak ingestion.
71%
Manual review reduction
Human effort shifted toward only exception and quality cases.