Enterprise Data Governance & Document Classification Platform

The Problem

Unstructured corporate data without classification or access controls

An enterprise client needed to bring intelligent structure to data chaos. Their organization — had vast repositories of unstructured files: emails, contracts, financial reports, HR documents, and internal communications. None of it was automatically classified, and access controls were either too broad or manually maintained.

The core challenges:

• No automated classification: files sat in shared drives with no programmatic way to determine if a document was “Finance,” “Legal,” “HR,” or “Confidential”
• GDPR exposure: without knowing what data existed where, compliance was impossible to guarantee
• Stale data accumulation: duplicate files, outdated versions, and abandoned documents consumed storage and increased risk
• Manual review bottleneck: compliance teams spent hundreds of hours per quarter reviewing access permissions against document sensitivity

Our Approach

ML-driven document intelligence pipeline

We built a document intelligence system that could ingest, analyze, and classify corporate files at scale. The pipeline combined multiple ML techniques to handle the variety and volume of enterprise data.

Smart Structurization Engine

The core of the system was a classification pipeline that processed documents through multiple stages:

1. Content extraction: parsed text from PDFs, DOCX, emails, and 20+ file formats using specialized extractors
2. Feature engineering: extracted structural features (document length, formatting patterns, metadata) alongside textual features (TF-IDF, named entities, key phrases)
3. Category prediction: ML models trained on labeled corporate data predicted business category with >92% accuracy
4. Confidentiality scoring: a separate model assessed sensitivity level (Public, Internal, Confidential, Secret) based on content patterns and entity types detected

Anomaly Detection Layer

Beyond classification, we built an anomaly detection module that identified:

• Stale data: files that hadn’t been accessed or modified beyond threshold periods, flagged for archival review
• Duplicates and near-duplicates: using document fingerprinting and similarity scoring to identify redundant files consuming storage
• Access anomalies: correlating document sensitivity classifications with actual access patterns to flag potential policy violations

Integration Architecture

The system was designed to plug into the client’s existing platform, processing documents via API endpoints and returning structured classification results with confidence scores. Results fed into dashboards for compliance teams to review edge cases and refine model performance.

Results

Automated compliance at enterprise scale

• Automated document classification across 5+ business categories with >92% accuracy
• GDPR compliance foundation: every document automatically tagged with sensitivity level
• Anomaly detection: proactive identification of stale data, duplicates, and access violations
• Eliminated manual classification: compliance teams shifted from reviewing every document to reviewing only edge cases flagged by the system
• Scalable architecture: designed to handle growing data volumes without linear cost increase

Architecture Trade-offs

Technology Stack

• ML/NLP: scikit-learn, Deeplearning4j, StanfordNLP, TF-IDF, Named Entity Recognition
• Anomaly Detection: Isolation-based methods, document fingerprinting
• Processing: Apache Spark, Apache Tika (content extraction across 70+ languages)
• Backend: Python, PostgreSQL, Elasticsearch
• Infrastructure: Docker, RESTful API
• Data Processing: Custom document extraction pipeline (PDF, DOCX, email formats)

Client Testimonial

“Outstanding!”

— CEO, Enterprise Data Client