Project Overview

The quality control team spent significant time manually reviewing large volumes of documents to ensure compliance and consistency. Human oversight made the process slow and occasionally prone to missed anomalies, especially in high-throughput periods.

Problem Statement

Manual document review was inefficient and reactive. Minor inconsistencies or deviations often went unnoticed until they caused downstream issues, requiring rework and manual investigation. There was no automated system to proactively detect out-of-norm documents.

Solution

We developed an AI-driven anomaly detection model that automatically scans and evaluates incoming documents against defined norms and historical data patterns.
The system uses natural language processing (NLP) and pattern recognition to identify deviations, flag unusual entries, and route them to the appropriate reviewers for validation.
A dashboard highlights flagged cases and provides confidence scores to help prioritize review efforts.

Result

• 80% reduction in time spent on manual document review.

• Faster issue resolution through automated flagging and routing.

• Improved accuracy and compliance tracking.

• Proactive quality management, allowing teams to focus on decision-making rather than detection.