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LostmindAI PropTech Backend v1

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🎯 Vision

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🚀 Quick Start

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1. Install Dependencies

pip install -r requirements.txt

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2. Configure AI Integration

# Copy and configure environment
cp .env.example .env
# Set AI_COMPUTE_URL to your TurboRepo ai-compute service
# Default: http://localhost:8000

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3. Sanitise Your Data (IMPORTANT - Do this first!)

python sanitize_data.py

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4. Create Database Schema

python excel_to_sql.py

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5. Test AI Integration

python scripts/test_integration.py

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🚨 CRITICAL SECURITY WARNING

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🔐 MANDATORY First Step - Data Sanitization

• ✅ 16,598 actual General Ledger transactions
• ✅ 2,084 real Accounts Payable records
• ✅ Actual vendor names and contact information
• ✅ Real property names and addresses
• ✅ Genuine financial amounts and calculations

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🚨 BEFORE ANY DEVELOPMENT OR SHARING:

# STEP 1: ALWAYS RUN THIS FIRST
python sanitize_data.py

# STEP 2: VERIFY no sensitive data remains
# Review the generated SM_Accrual_Review_SANITISED.xlsm file

# STEP 3: Only then proceed with development
python excel_to_sql.py

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🔒 Data Privacy Rules:

1. NEVER commit the original SM_Accrual_Review_TEMPLATE.xlsm to any public repository
2. NEVER share unsanitised data outside your secure environment
3. ALWAYS use Gemma (local model) for Australian data compliance
4. ALWAYS run sanitization before any external collaboration

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✅ After Sanitization, Data Becomes:

• Vendor names → VENDOR_0001_PTY_LTD
• Property names → NSW_OFF_001_Sydney_CBD
• Amounts → Randomised ±10% (maintaining statistical distribution)
• Employee data → Completely removed
• Structure & formulas → Fully preserved

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📁 Project Structure

PropTech Finance Tools/
├── Core Files
│   ├── sanitize_data.py          # Data privacy tool
│   ├── excel_to_sql.py           # Database converter
│   └── SM_Accrual_Review_TEMPLATE.xlsm  # Original tool
│
├── AI Integration ✅
│   ├── ai_integration/
│   │   ├── unified_client.py     # Modern AI service client
│   │   └── current_architecture.py  # Migration docs
│   └── .env.example              # Environment configuration
│
├── Microservices ✅
│   └── microservices/
│       └── accrual_calculator/
│           └── main.py           # AI-enhanced calculator
│
├── Scripts & Tools ✅
│   ├── scripts/
│   │   ├── test_integration.py   # AI integration testing
│   │   ├── migrate_ai_imports.py # Migration tools
│   │   └── security_scan.py      # Security validation
│   └── requirements.txt          # Modern dependencies
│
├── Documentation
│   ├── docs/
│   │   └── AI_INTEGRATION_MIGRATION.md  # Migration guide
│   ├── PROJECT_MAP.md            # Project navigation
│   ├── ARCHITECTURE.md           # System design
│   └── CLAUDE.md                 # AI context
│
└── Coming Soon
    └── /web_interface/           # User interface

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🏗️ System Architecture

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Current Capabilities ✅

• ✅ Excel structure analysis (20 worksheets, 16,000+ transactions)
• ✅ Data sanitisation for privacy
• ✅ SQL schema generation
• ✅ Modern AI Integration (UnifiedAIClient architecture)
• ✅ Microservices Foundation (AI-enhanced AccrualCalculator)
• ✅ Service-oriented Architecture (TurboRepo ai-compute integration)
• ✅ Australian Compliance (PropTech-specific AI features)
• ✅ Comprehensive Testing & Security (Integration tests, security scans)

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AI Architecture (Completed)

PropTech Backend → UnifiedAIClient → TurboRepo ai-compute → AI Models
    (Async)          (HTTP/JSON)        (Service)        (Multi-provider)

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In Development

• 🔄 Additional microservices (variance_analyzer, journal_generator)
• 🔄 Web interface for user interaction
• 🔄 Advanced AI financial analysis features

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💡 Key Features

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From Your Knight Frank Tool

• 16,598 General Ledger transactions processed
• 2,084 Accounts Payable records managed
• 3,504 Chart of Accounts mappings
• VBA Macros for automation
• SQL Integration with MRI system

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AI-Powered Capabilities ✅

• UnifiedAIClient: Modern service-oriented AI integration
• Australian Compliance: PropTech-specific AI with data sovereignty
• AI-Enhanced AccrualCalculator: Intelligent variance explanation and validation
• Comprehensive Error Handling: Graceful degradation and retry logic
• Async Architecture: High-performance concurrent AI operations
• Security-First: Zero hardcoded secrets, service-to-service auth ready

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Infrastructure Capabilities ✅

• Microservices Architecture: Scalable, maintainable components
• SQL Database: Structured data storage
• Comprehensive Testing: Integration tests, security validation
• Migration Tools: Automated modernization scripts

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🔐 Data Privacy & Security

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Sanitisation Process

• Vendor names → Generic codes (VENDOR_0001)
• Property names → NSW property codes (NSW_OFF_001_Sydney CBD)
• Employee data → Removed
• Amounts → Randomised ±10%
• Formulas & structure → Preserved

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Compliance Considerations

• Gemma Model: Local processing for Australian data requirements
• Data Isolation: Separate development and production environments
• Audit Trail: Complete tracking of all changes

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🎯 Use Cases

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AI-Enhanced Monthly Accruals ✅

# NOW AVAILABLE - AI-powered accrual processing
from microservices.accrual_calculator.main import AccrualCalculator

calculator = AccrualCalculator()

# Traditional calculation with AI validation
accruals = calculator.calculate_expense_accruals("2024-12", "NSW_OFF_001")

# AI-powered variance explanation
variance_data = {/* "account": "4000", "variance": 5000, "percentage": 15.5 */}
explanation = calculator.explain_variance(variance_data, "2024-12")
print(explanation)  # AI generates detailed explanation

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Modern AI Integration ✅

# NOW AVAILABLE - Direct AI service integration
from ai_integration.unified_client import UnifiedAIClient

async def analyze_financial_data():
    async with UnifiedAIClient() as client:
        # Australian-compliant financial analysis
        result = await client.analyze_financial_data(
            data={"transactions": transaction_data},
            analysis_type="variance"
        )

        # Accrual validation with confidence scoring
        validation = await client.validate_accruals(
            accrual_data=accruals_list,
            period="2024-12"
        )

        return result, validation

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Property Portfolio Analysis

# Coming soon - enhanced with AI insights
from microservices.variance_analyzer import VarianceAnalyzer

analyzer = VarianceAnalyzer()
variances = analyzer.compare_periods("2024-11", "2024-12")
ai_insights = analyzer.generate_ai_insights(variances)  # AI-powered analysis

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🗺️ Roadmap

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Phase 1: Foundation ✅

• Project structure
• Data sanitisation
• SQL schema design
• Data migration script

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Phase 2: Core Engine (Next 2 weeks)

• Excel parser with intelligent processing
• Database population
• Basic API structure
• First microservice (accrual_calculator)

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Phase 3: AI Integration (Weeks 3-4)

• Gemma local setup (Australian compliance)
• Claude API integration
• Gemini document processing
• Model routing logic

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Phase 4: User Interface (Weeks 5-6)

• Web dashboard
• Excel export functionality
• API documentation
• User authentication

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Phase 5: Production (Weeks 7-8)

• Docker containerisation
• Deployment scripts
• Performance testing
• Security audit

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🏢 Industry Context

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Your Experience

• JLL: Property accounting fundamentals
• CBRE: Enterprise-scale processes
• Knight Frank: NSW Finance Manager (created this tool)
• Mirvac: MRI system expertise

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Target Market

• Property management companies (400+ properties)
• Commercial real estate firms
• REITs and property funds
• Corporate real estate departments

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Value Proposition

• 80% time reduction in month-end processing
• 99.9% accuracy in calculations
• Complete audit trail for compliance
• Scalable to any portfolio size

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📊 Technical Specifications

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Performance Targets

• Process 500+ properties in < 5 minutes
• Handle 20,000+ transactions per run
• Support 100+ concurrent users
• 99.9% uptime SLA

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Technology Stack

• Backend: Python (FastAPI coming)
• Database: PostgreSQL/SQLite
• AI Models: Gemma, Claude, Gemini, GPT, Grok
• Frontend: React/Next.js (planned)
• Infrastructure: Docker, Kubernetes (planned)

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🤝 Contributing

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Development Workflow

1. Create feature branch
2. Implement with tests
3. Update documentation
4. Submit for review

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Coding Standards

• Australian English spelling in all code and comments
• Comprehensive docstrings
• Type hints for all functions
• Unit tests for critical logic

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📞 Next Steps

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Immediate Actions

1. Run sanitisation: python sanitize_data.py
2. Review sanitised data: Ensure no sensitive info remains
3. Create database: python excel_to_sql.py
4. Plan first microservice: Accrual calculator

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Questions to Consider

1. Which accounting process should we automate first?
2. Should we prioritise monthly or year-end workflows?
3. Which properties/portfolios to use for testing?
4. Integration requirements with existing systems?

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📝 Notes

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About the Original Tool

• Scalable: From 400 to 4000+ properties
• Intelligent: AI-powered decision making
• Compliant: Australian data sovereignty
• Modern: Cloud-native architecture

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Security Reminder

• Never commit unsanitised data to version control
• Review all outputs before sharing
• Use Gemma for sensitive Australian data
• Maintain audit logs for all operations

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🚀 Let’s Build This!