Project Overview
The Litigation Case Intelligence System is a comprehensive AI-powered platform designed to automatically classify and extract insights from large volumes of legal case data. It leverages advanced Natural Language Processing (NLP) and machine learning techniques to assign case categories, identify key entities, and streamline legal data management — all while maintaining full local execution and data privacy.
Key Features
- • Automated case classification using XGBoost
- • Named Entity Recognition (NER) for people, organizations, and locations
- • Confidence-based auto-labeling workflow
- • Closed-loop retraining with gold-standard data
- • Scalable ingestion of 6.7M+ legal records
- • 20 canonical legal categories with structured mapping
- • Comprehensive 100+ page documentation suite
- • Local-only execution (no external API dependencies)
Technologies
- • Python (scikit-learn, XGBoost, spaCy, sentence-transformers)
- • Pandas & NumPy for data engineering
- • TF-IDF and embeddings-based text representation
- • PostgreSQL / Local CSV storage
- • Matplotlib & Seaborn for visualization
- • Shell automation for reproducible execution
- • YAML configuration and logging architecture
System Architecture
Data Layer
Ingests and processes over 6.7 million case records from multiple structured and semi-structured CSV/Excel sources. Handles custom delimiters, encoding issues, and inconsistent field schemas. Performs label canonicalization and strategic sampling for efficient training.
AI Engine
XGBoost model trained to classify cases into 20 canonical categories. Sentence-transformer embeddings for semantic understanding of case descriptions. NER module detects people, organizations, and locations within case texts.
Insights Layer
Confidence-based routing for auto-labeling, manual review, and disagreement analysis. Detailed metrics and visualization outputs for model validation and decision support.
Data Layer Details
• Ingests and processes over 6.7 million case records from multiple structured and semi-structured CSV/Excel sources
• Handles custom delimiters, encoding issues, and inconsistent field schemas
• Performs label canonicalization and strategic sampling for efficient training
AI Engine Details
• XGBoost model trained to classify cases into 20 canonical categories
• Sentence-transformer embeddings for semantic understanding of case descriptions
• NER module detects people, organizations, and locations within case texts
Insights Layer Details
• Confidence-based routing for auto-labeling, manual review, and disagreement analysis
• Detailed metrics and visualization outputs for model validation and decision support
Core Features
Case Classification
An optimized XGBoost classifier achieves 89.49% accuracy across 20 canonical legal categories, accurately labeling 80% of cases automatically.
Entity Recognition (NER)
Extracts structured entities — including names, organizations, and locations — into machine-readable JSON outputs for downstream analytics.
Confidence Routing System
Automatically flags high-confidence predictions (≥0.8) for auto-labeling, medium-confidence for human review, and low-confidence for manual validation.
Closed-Loop Retraining
Supports iterative retraining through a gold-standard feedback pipeline that continuously improves performance as new verified data is introduced.
Technical Implementation
Model Architecture
- Primary Model: XGBoost Gradient Boosting (199 rounds, depth 6, 89.49% accuracy)
- Baseline Model: Logistic Regression (85.07% accuracy)
- NER Engine: spaCy-based entity tagger integrated with structured JSON output
- Data Representation: TF-IDF + 384-dimensional semantic embeddings
Performance Metrics
| Metric | Logistic Regression | XGBoost | Improvement |
|---|---|---|---|
| Accuracy | 85.07% | 89.49% | +4.42% |
| Cross-Validation | 85.7% ± 1.3% | 89.03% ± 0.52% | Stable |
| Auto-Labelable Cases | - | 5,946 (80.4%) | +80% workflow automation |
Use Cases & Applications
Law Firms & Legal Analysts
Automated case categorization for large-scale litigation datasets and discovery pipelines.
Insurance & Compliance
Rapid classification of claim narratives to detect injury types, liability categories, and related entities.
Data Labeling Operations
Confidence-based auto-labeling and retraining workflow reduces manual review effort by over 70%.
Results & Impact
Quantitative Results
- 89.49% model accuracy (XGBoost)
- 83.7% model agreement (cross-model validation)
- 6.7M rows processed efficiently on local machine
- 80.4% cases auto-labelable (≥0.80 confidence)
- 10.3% low-confidence cases flagged for manual review
Business Impact
- Drastically reduces manual labeling effort and costs
- Enables faster case triage and data analysis
- Maintains full data privacy (no external cloud use)
- Provides scalable foundation for future secondary keyword classification
Future Enhancements
Planned Features
- • Secondary keyword prediction (e.g., "Med-Mal – Cancer")
- • Taxonomy expansion to support subcategories
- • Entity relationship mapping between case participants
- • Lightweight web dashboard for case review and visualization
Technical Improvements
- • Bayesian inference layer for secondary keyword prediction
- • Automated model retraining pipeline
- • Integration with PostgreSQL for large-scale deployment
- • Additional subtype-level labeled data for enhanced accuracy