A representative concept for a RAG-based system that extracts and classifies information from large volumes of enterprise documents.
A professional services firm's staff spent a significant share of their week manually reviewing and classifying incoming PDF documents to extract key data points.
Discovery included testing extraction accuracy against a real sample of the firm's actual documents before committing to a full build, since document quality and structure varied significantly.
A retrieval-augmented pipeline that classifies incoming documents, extracts key fields, and routes low-confidence extractions to a human review queue.
A review-queue interface designed for fast human verification -- extracted fields shown side-by-side with the source document region they came from.
Extraction accuracy was measured against real documents at each stage, with the human-review threshold tuned based on observed confidence levels rather than a fixed assumption.
Document access scoped by role, with processed data encrypted at rest.
Deployed as a containerized service with monitoring on extraction throughput and review-queue volume.