# DSF Quantum Oracle API

**Hybrid Quantum-Classical Search & Filtering**

Accelerate search and filtering in large datasets using quantum-inspired algorithms. Validated on real quantum hardware with classical fallback.

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## 🚀 Why Oracle?

Classical search through large datasets requires linear examination. Oracle uses hybrid quantum-classical techniques to accelerate discovery of high-priority items in complex data spaces.

**Key Benefits:**
- Accelerated search in large datasets
- Multi-criteria filtering
- Real-time anomaly detection
- API-first integration

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## 📊 Use Cases

### Legal Technology
- Document retrieval in large case databases
- Precedent search across millions of cases
- Contract clause discovery
- Due diligence acceleration

### E-commerce & Marketing
- Customer segmentation at scale
- High-value customer identification
- Product recommendation filtering
- Campaign audience optimization

### Financial Services
- Portfolio screening
- Risk factor identification
- Fraud pattern detection
- Asset discovery

### Data Analytics
- Anomaly detection in time-series
- Feature selection from high-dimensional data
- Pattern matching at scale
- Outlier identification

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## 💼 Pricing Tiers

|        Tier      | Searches/Hour |      Support    |    Price   |
|------------------|---------------|-----------------|------------|
| **Community**    |      10       | Email           | Contact    |
| **Professional** |      50       | Email           | Contact    |
| **Enterprise**   |    Custom     | SLA + Dedicated | Contact    |

*Enterprise tier includes custom volume limits and execution strategies.*

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## 🔧 Quick Start

```python
from dsf_quantum_orc_sdk import QuantumOracle

oracle = QuantumOracle(
    api_key="your_api_key",
    license_key="your_license_key",
    tier="professional"
)


result = oracle.search(
    values=[0.82, 0.61, 0.74, 0.55, 0.92],  
    parameters=[0.5, 0.3, 0.2, 0.4, 0.6],
    threshold=0.75
)

print(f"Found Items: {result['found_indices']}")
print(f"Success Rate: {result['success_probability']}")
```

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## ⚙️ Input Requirements

**Normalization Required:**  
All input values must be normalized to [0-1] range

**Dimensionality Limits:**  
- Community: Up to 100 items
- Professional: Up to 1,000 items
- Enterprise: Up to 10,000 items (custom limits available)

**Performance Characteristics:**  
Results subject to quantum noise when executing on real hardware. Classical fallback ensures consistent availability.

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## 🎯 Integration Patterns

### Pattern 1: RAG Document Filtering
Pre-filter LLM document retrievals for faster, more relevant results

### Pattern 2: Batch Analytics
Overnight processing of large datasets with quantum-enhanced filtering

### Pattern 3: Real-Time Scoring
Stream processing with hybrid execution paths

### Pattern 4: Feature Selection
Pre-processing for ML pipelines to reduce dimensionality

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## 🔒 Security

- **Transport:** TLS 1.3 encryption
- **Storage:** AES-256 encryption at rest
- **Authentication:** Token-scoped API keys
- **Compliance:** SOC2-ready architecture (compliance program in progress)
- **Data Residency:** Configurable regional deployment
- **Technical Docs:** Available under NDA

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## 📊 Return Values

```python
{
    'found_indices': [0, 2, 4],           
    'success_probability': 0.87,          
    'execution_backend': 'quantum',       
    'item_scores': [0.85, 0.62, 0.91], 
    'quantum_noise_level': 0.12           
}
```

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## 📞 Get Started

**Request Technical Documentation:**  
[Technical Access Form](mailto:contacto@dsfuptech.cloud?subject=Oracle%20Technical%20Docs) - Full API specs under NDA

**Schedule Demo:**  
[Book 30-min demo](mailto:contacto@dsfuptech.cloud?subject=Oracle%20Demo) with your data

**Pilot Program:**  
60-day pilot for qualified organizations

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## 📚 Resources

- [RAG Integration Guide](mailto:contacto@dsfuptech.cloud?subject=RAG%20Guide) (requires NDA)
- [Performance Benchmarks](mailto:contacto@dsfuptech.cloud?subject=Benchmarks)
- [Case Studies: LegalTech](mailto:contacto@dsfuptech.cloud?subject=LegalTech%20Cases)

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## 🏢 Enterprise Features

- Custom search strategies
- Configurable execution backends
- On-premise deployment
- White-label options
- Custom SLAs

Production integration available upon completion of client validation and model governance workflows.

Contact: contacto@dsfuptech.cloud

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## 📋 Credits

**Technology Architect:** Jaime Alexander Jimenez

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