ChittyTrace-CC for Cook County Illinois (Chicago) - Financial Document Analysis & Court Ready Exhibit Package Development
Specialized version for Cook County Circuit Court with local attorney optimizations
A comprehensive AI-powered financial forensics platform optimized specifically for Cook County attorneys and legal professionals.
Developed by ChittyCorp, LLC - Part of the Chitty ecosystem of AI-powered business solutions.
- Document Processing: Recursive scanning and indexing of all financial documents
- AI-Powered Analysis: Claude integration for intelligent document querying
- Interactive Timeline: Visual timeline with document linking
- Exhibit Generation: Cook County court-compliant exhibit packages
- Form Automation: Automated filling of legal and financial forms
- Command Execution: Complex analysis commands (fund tracing, pattern detection)
- Database Integration: Neon PostgreSQL with vector search capabilities
- Email Ingestion: Cloudflare Worker integration for email processing
- Database Integration: Neon PostgreSQL with vector search
- Financial statements (PDF, Excel, CSV)
- Bank statements
- Wire transfer records
- Property documents
- Legal filings
- Tax documents
- Email communications (.eml, .msg)
- Archive files (.zip, .tar, etc.)
- Office documents (.doc, .docx, .rtf)
- Clone the repository:
cd /flow_analyzer- Create virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate- Install dependencies:
pip install -r requirements.txt- Set up environment variables:
cp .env.example .env
# Edit .env with your API keys and configuration- Initialize database (if using Neon):
# The schema will be automatically applied on first runLaunch the interactive web interface:
streamlit run app.pyFeatures available in GUI:
- Document scanning and indexing
- Natural language queries
- Interactive timeline visualization
- Exhibit package generation
- Form filling interface
- Command execution center
- Data visualizations
- Document browser
Start the API server:
python claude_openai_extension.pyThe API runs on http://localhost:8000 and provides endpoints for:
/documents/scan- Scan and index documents/documents/query- Query documents with natural language/timeline/extract- Extract timeline events/exhibits/generate- Generate court exhibit packages/forms/fill- Fill form templates/commands/execute- Execute analysis commands
from flow_analyzer import FlowAnalyzer
# Initialize analyzer
analyzer = FlowAnalyzer(api_key="your_anthropic_key")
# Scan documents
documents = analyzer.scan_all_documents()
# Query documents
result = analyzer.query("Trace funds from USAA to Colombia property")
# Generate exhibit package
package = analyzer.generate_exhibit_package(
documents=selected_docs,
case_info={
"case_number": "2024-CH-00001",
"caption": "Your Case Name v. Defendant",
"affiant": "Your Name"
},
purpose="Property funding documentation"
)The system automatically formats exhibits according to Cook County Circuit Court requirements:
- Page Size: 8.5" x 11"
- Margins: 1 inch on all sides
- Font: Times New Roman or Arial, 12pt
- Line Spacing: Double-spaced
- Exhibit Stickers: Bottom right corner with:
- Exhibit Number
- Case Number
- Date
- Authentication: Notarized affidavit required
- Package Components:
- Cover sheet
- Table of contents
- Certificate of service
- Sequential exhibit numbering
The system uses PostgreSQL with vector extensions for semantic search:
documents- Scanned documents with content and embeddingstimeline_events- Extracted financial eventscourt_exhibits- Exhibit preparation and trackinganalysis_queries- Query history and responsesexhibit_packages- Complete exhibit packages
The system integrates with Neon PostgreSQL databases for:
- Document storage and indexing
- Timeline event tracking
- Cross-referencing transactions
- Vector-based semantic search
- Exhibit package management
Configure Cloudflare Worker for email ingestion:
- Set
CLOUDFLARE_WORKER_URLin.env - Set
CLOUDFLARE_WORKER_TOKENfor authentication - The system will automatically ingest emails from:
- nick@chitty.cc
- Other configured email addresses
result = analyzer.execute_command("detect_patterns", {
"pattern_type": "structured_transactions",
"threshold": {"amount": 10000, "frequency": "daily"}
})chart = analyzer.execute_command("generate_fund_flow_chart", {
"start_date": "2023-01-01",
"end_date": "2023-12-31",
"accounts": ["USAA", "Fidelity", "Mercury"]
})analysis = analyzer.execute_command("analyze_property_chain", {
"property_address": "123 Main St, Chicago, IL"
})- API endpoints are protected with bearer token authentication
- Database connections use SSL
- Sensitive data is never logged
- File uploads are validated and sandboxed
- Import errors: Ensure all dependencies are installed
- API key errors: Check
.envconfiguration - Database connection: Verify DATABASE_URL format
- Large files: Files over 100MB are skipped by default
Check logs/ directory for detailed error messages and debugging information.
This project is developed and maintained by ChittyCorp, LLC, a technology company specializing in AI-powered solutions for legal, financial, and business intelligence applications.
- Website: chittycorp.com
- Contact: support@chittycorp.com
- Foundation: ChittyFoundation
For issues, questions, or commercial licensing inquiries:
- Email: support@chittycorp.com
- Create an issue on GitHub
- Visit: chittycorp.com
This project is licensed under the MIT License - see the LICENSE file for details.
Copyright (c) 2025 ChittyCorp, LLC