# AletheionGuard - AI Auditing Platform

> Epistemic auditor for LLM outputs - quantifies uncertainty to detect hallucinations

## Overview

AletheionGuard is an advanced epistemic auditing system that quantifies uncertainty in Large Language Model (LLM) outputs using a pyramidal architecture to separate aleatoric (data uncertainty) from epistemic (model uncertainty).

## Core Technology

### Pyramidal Architecture
- **Q1 (Aleatoric Uncertainty)**: Measures irreducible data noise and ambiguity
- **Q2 (Epistemic Uncertainty)**: Quantifies model ignorance and knowledge gaps
- **Height Metric**: h = 1 - sqrt(Q1² + Q2²) - Represents proximity to truth

### Verdict System
- **ACCEPT**: Low uncertainty (Q1 < 0.35, Q2 < 0.35, u < 0.30)
- **MAYBE**: Moderate uncertainty requiring review
- **REFUSED**: High uncertainty (Q2 ≥ 0.35 or u ≥ 0.60)

## Key Features

1. **Real-time Auditing**: Audit LLM responses in milliseconds
2. **Calibration Metrics**: ECE (Expected Calibration Error) and Brier Score
3. **API Integration**: RESTful API for seamless integration
4. **Batch Processing**: Efficient batch auditing for large datasets
5. **Model Agnostic**: Works with any LLM (GPT, Claude, Llama, etc.)

## Pricing Plans

### Free Tier
- 1,000 requests/month
- Basic API access
- Standard support
- Community features

### Pro ($29/month)
- 50,000 requests/month
- Priority API access
- Email support
- Advanced analytics

### Enterprise (Custom)
- Unlimited requests
- Dedicated infrastructure
- 24/7 support
- Custom integrations
- SLA guarantees

## Technical Specifications

### API Endpoints

**POST /api/audit**
```json
{
  "text": "The LLM response to audit",
  "context": "Optional context or prompt",
  "model_source": "gpt-4"
}
```

**Response:**
```json
{
  "q1": 0.245,
  "q2": 0.189,
  "height": 0.691,
  "ece": 0.048,
  "brier": 0.032,
  "verdict": "ACCEPT",
  "confidence_interval": [0.641, 0.741],
  "explanation": "Low uncertainty across both dimensions..."
}
```

### Performance Metrics
- **Latency**: < 100ms per request
- **Accuracy**: 94.7% on benchmark datasets
- **Calibration**: ECE < 0.05 (well-calibrated)
- **Uptime**: 99.9% SLA

## Use Cases

1. **Hallucination Detection**: Identify when LLMs generate false information
2. **Quality Assurance**: Automated QA for AI-generated content
3. **Medical AI**: Safety-critical validation for healthcare applications
4. **Legal Tech**: Verify AI legal research and document analysis
5. **Financial Services**: Risk assessment for AI trading algorithms
6. **Customer Support**: Quality control for AI chatbots
7. **Content Moderation**: Verify AI content classification
8. **Educational Tools**: Validate AI tutoring responses

## Scientific Foundation

### Research Papers
- Original architecture based on epistemic-aleatoric uncertainty decomposition
- Calibration methods from modern uncertainty quantification literature
- Evaluation on standard benchmarks (TriviaQA, NaturalQuestions, MMLU)

### Model Training
- Sentence-BERT embeddings (all-MiniLM-L6-v2)
- Neural network gates for Q1 and Q2 estimation
- Trained on diverse LLM outputs with ground truth labels
- Continuous calibration and improvement

## Integration Examples

### Python
```python
from aletheion_guard import EpistemicAuditor

auditor = EpistemicAuditor()
audit = auditor.evaluate(
    text="Paris is the capital of France",
    context="What is the capital of France?"
)
print(f"Verdict: {audit.verdict}")
print(f"Height: {audit.height:.3f}")
```

### JavaScript/Node.js
```javascript
const response = await fetch('https://aletheionguard.onrender.com/v1/audit', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer YOUR_API_KEY'
  },
  body: JSON.stringify({
    text: 'Paris is the capital of France',
    context: 'What is the capital of France?'
  })
});
const audit = await response.json();
```

### cURL
```bash
curl -X POST https://aletheionguard.onrender.com/v1/audit \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{"text": "Paris is the capital of France"}'
```

## Company Information

### Organization
- **Name**: Aletheion Research Collective
- **Founded**: 2024
- **Location**: Global (Remote-first)
- **Mission**: Ensure AI systems produce trustworthy, calibrated outputs

### Contact
- **Website**: https://aletheionguard.com
- **Email**: contact@aletheionagi.com
- **Support**: support@aletheionagi.com
- **Twitter**: @aletheionguard
- **GitHub**: https://github.com/AletheionAGI

### Open Source
- **License**: AGPL-3.0
- **Repository**: https://github.com/AletheionAGI/AletheionGuard
- **PyPI Package**: aletheion-guard
- **NPM Package**: Coming soon

## Documentation

### Quick Start
- Installation: `pip install aletheion-guard`
- API Docs: https://aletheionguard.com/docs
- Tutorials: https://aletheionguard.com/docs/tutorials
- Examples: https://github.com/AletheionAGI/AletheionGuard/tree/main/examples

### Developer Resources
- API Reference: https://aletheionguard.com/docs/api
- SDK Documentation: https://aletheionguard.com/docs/sdk
- Webhooks: https://aletheionguard.com/docs/webhooks
- Rate Limits: https://aletheionguard.com/docs/rate-limits

### Support Resources
- FAQ: https://aletheionguard.com/docs/faq
- Community Forum: https://github.com/AletheionAGI/AletheionGuard/discussions
- Status Page: https://status.aletheionguard.com
- Changelog: https://aletheionguard.com/changelog

## Compliance & Security

### Security
- SOC 2 Type II (in progress)
- GDPR compliant
- End-to-end encryption
- Zero data retention by default
- Regular security audits

### Privacy
- No training on customer data
- Anonymized analytics only
- Data processing agreements available
- EU/US data residency options

## Benchmarks

### Performance (vs. Other Methods)
- Baseline (Max Softmax Prob): 82.3% accuracy
- Entropy-based: 85.7% accuracy
- AletheionGuard: **94.7% accuracy**

### Calibration
- Expected Calibration Error: 0.048 (industry: ~0.12)
- Brier Score: 0.032 (industry: ~0.08)
- Perfectly calibrated on most domains

## Roadmap

### Q1 2025
- [ ] Multi-language support
- [ ] Real-time streaming audits
- [ ] Custom threshold configuration
- [ ] Enhanced analytics dashboard

### Q2 2025
- [ ] Domain-specific models (medical, legal, financial)
- [ ] Fine-tuning API
- [ ] Advanced visualizations
- [ ] Mobile SDKs (iOS/Android)

### Q3 2025
- [ ] On-premise deployment option
- [ ] Federated learning support
- [ ] Audit trail and compliance reporting
- [ ] Integration marketplace

## Keywords

artificial intelligence, machine learning, large language models, LLM, uncertainty quantification, epistemic uncertainty, aleatoric uncertainty, hallucination detection, AI safety, calibration, neural networks, natural language processing, NLP, deep learning, model evaluation, quality assurance, AI auditing, trustworthy AI, responsible AI, explainable AI, XAI

---

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---

**Last Updated**: 2025-11-12
**Version**: 1.1.2
**Format**: llms.txt v1.0
**License**: AGPL-3.0-or-later