110% Protocol | FAANG Enterprise-Grade | Zero Human Hands

Lead Sniper is a fully autonomous lead generation system that combines the power of Manus Core parallel processing, Google Vertex AI, Vision Cortex multi-perspective analysis, and headless browser orchestration to generate high-quality distressed property leads 24/7.

• Manus Core Engine: Parallel MAP processing with auto-heal, auto-fix, and auto-optimize
• Vision Cortex Integration: Multi-perspective signal analysis for comprehensive lead scoring
• Vertex AI + AutoML: Gemini-powered analysis with predictive lead scoring
• Headless Scraper Orchestration: 100+ parallel browser instances for maximum throughput
• Triple-Check Validation: Three-stage validation ensuring 100% data accuracy
• Hybrid Local/Cloud Sync: Smart routing with automatic failover

• Zero Human Intervention: Fully autonomous E2E pipeline
• Scheduled Execution: Configurable daily runs (default: 5 AM)
• Smart Routing: Automatic local/cloud switching based on availability
• Self-Healing: Auto-recovery from failures and errors
• Live Sync: Real-time data synchronization across systems

lead-sniper/
├── src/
│   ├── core/
│   │   └── manus_core.py          # Core autonomous engine
│   ├── crawlers/                   # Web crawlers
│   ├── scrapers/
│   │   └── headless_orchestrator.py  # Parallel scraper system
│   ├── agents/                     # AI agents
│   ├── orchestrator/               # Task orchestration
│   ├── vertex_ai/
│   │   └── vertex_integration.py   # Vertex AI + AutoML
│   ├── vision_cortex/              # Multi-perspective analysis
│   ├── pipeline/
│   │   └── autonomous_pipeline.py  # Main pipeline
│   ├── validation/                 # Data validation
│   └── sync/
│       └── hybrid_sync.py          # Local/cloud sync
├── config/                         # Configuration files
├── results/
│   ├── raw/                        # Raw scraped data
│   ├── processed/                  # Processed leads
│   ├── leads/                      # Final lead outputs
│   └── reports/                    # Pipeline reports
├── scripts/                        # Utility scripts
├── tests/                          # Test suite
├── docs/                           # Documentation
├── infrastructure/
│   ├── docker/                     # Docker configs
│   ├── kubernetes/                 # K8s manifests
│   └── terraform/                  # IaC configs
├── frontend/                       # Web dashboard
├── backend/                        # API server
└── main.py                         # Main entry point

• Python 3.11+
• Node.js 18+
• Google Cloud SDK
• Docker (optional)

# Clone repository
git clone https://github.com/InfinityXOneSystems/lead-sniper.git
cd lead-sniper

# Install dependencies
pip install -r requirements.txt

# Set environment variables
export GCP_SA_KEY='your-service-account-key'
export GEMINI_API_KEY='your-gemini-api-key'

# Run pipeline
python main.py

# Build image
docker build -t lead-sniper .

# Run container
docker run -e GCP_SA_KEY=$GCP_SA_KEY lead-sniper

python main.py

python main.py --schedule

python main.py --daemon

python main.py --test

python main.py --config config/custom.json --max-scrapers 200 --schedule-time 06:00

PipelineConfig(
    max_parallel_scrapers=100,      # Parallel browser instances
    max_parallel_analysis=50,       # Concurrent AI analysis
    batch_size=100,                 # Processing batch size
    auto_start=True,                # Auto-start on init
    auto_heal=True,                 # Self-healing enabled
    auto_retry=True,                # Auto-retry on failure
    max_retries=3,                  # Maximum retry attempts
    local_primary=True,             # Local execution primary
    cloud_fallback=True,            # Cloud fallback enabled
    smart_routing=True,             # Smart routing enabled
    triple_check_enabled=True,      # Triple validation
    min_confidence_threshold=0.7,   # Minimum confidence
    store_to_bigquery=True,         # BigQuery storage
    sync_to_firestore=True,         # Firestore sync
    save_to_local=True,             # Local file storage
    scheduled_execution=True,       # Scheduled runs
    schedule_time="05:00"           # Daily run time
)

• Vertex AI: Gemini Pro for intelligent analysis
• AutoML: Predictive lead scoring models
• BigQuery: Analytics and reporting
• Firestore: Real-time data sync
• Cloud Storage: Data persistence
• Cloud Run: Serverless execution

• Government foreclosure databases
• County clerk records
• Tax assessor data
• Auction platforms (Auction.com, etc.)
• Real estate platforms (Zillow, Redfin)
• Social media (Facebook Marketplace, etc.)

1. Initialization: Start all components
2. Scraping: Parallel headless scraping
3. Validation: Triple-check data validation
4. Analysis: Vision Cortex + Vertex AI
5. Scoring: AutoML prediction
6. Enrichment: Data enrichment
7. Storage: Multi-destination storage
8. Reporting: Generate reports

• Foreclosures
• Tax Liens
• Bank-Owned (REO)
• Short Sales
• Probate Properties
• Code Violations
• Auction Properties

The system tracks:

• Leads scraped/validated/analyzed/stored
• Execution time per stage
• Error rates and auto-heals
• Worker health and availability
• Sync status (local/cloud)

• Service account authentication
• Encrypted credentials storage
• Stealth mode for scrapers
• Rate limiting and throttling
• Audit logging

Proprietary - InfinityXOneSystems

This is an internal system. Contact the development team for contribution guidelines.

For issues and support, contact the InfinityXOneSystems team.

Built with ❤️ by InfinityXOneSystems

Powered by Manus Core | Vision Cortex | Vertex AI