INITE Model Cards
Transparent documentation for all production AI models. Following Google's Model Card framework for standardized ML model documentation.
SocialAI NLP Engine
Community Management & Multi-language Support
Model Details
| Version | v3.2.1 |
| Architecture | Fine-tuned LLM + Custom RAG |
| Languages | 16 languages (native) |
| Response Time | 2.3s average |
| Uptime | 99.8% |
Performance Metrics
| Intent Accuracy | 94.2% |
| Response Relevance | 91.8% |
| User Satisfaction | 4.6/5.0 |
| Escalation Rate | 8.3% |
| False Positive Rate | 2.1% |
Intended Use
- ✓ Community management in Telegram/Discord
- ✓ FAQ automation and knowledge base queries
- ✓ Multi-language user support
- ✓ Sentiment analysis and crisis detection
Limitations
- ⚠ Not for medical/legal/financial advice
- ⚠ May struggle with highly technical jargon
- ⚠ Context window: 8,000 tokens
- ⚠ Real-time data not available (no internet)
Bias Testing Results (Q4 2024)
✓ Demographic parity verified across age groups | ✓ Equal performance across all 16 languages | ✓ No gender bias detected in response tone | ✓ Cultural sensitivity validated
EstateAI Lead Engine
Real Estate Lead Qualification & Sales Automation
Model Details
| Version | v2.4.0 |
| Architecture | Multi-modal (Text + Image) |
| Specialization | Real Estate Domain |
| CRM Integration | 15+ systems |
| Avg Deal Size Impact | +22-25% |
Performance Metrics
| Lead Qualification Accuracy | 89.4% |
| Conversion Improvement | 1.6-2.0x |
| Time Saved/Month | 40+ hours |
| Property Match Accuracy | 87.2% |
| Response Rate | 100% (24/7) |
Intended Use
- ✓ Lead qualification and scoring
- ✓ Property recommendation matching
- ✓ Legal/tax/visa FAQ automation
- ✓ Viewing and appointment scheduling
Limitations
- ⚠ Cannot provide binding legal advice
- ⚠ Property valuations are estimates only
- ⚠ Requires CRM integration for full features
- ⚠ Regional regulations may vary
RentAI Booking Engine
Vehicle Rental Automation & Revenue Optimization
Model Details
| Version | v2.1.3 |
| Architecture | Intent Classification + Booking Logic |
| Vehicle Types | Cars, Yachts, Motorcycles, Equipment |
| Payment Integration | Stripe, PayPal, Local gateways |
Performance Metrics
| Booking Conversion | 1.5-2.0x improvement |
| Avg Check Increase | +15% |
| Time Saved/Month | 30-50 hours |
| Query Resolution | 92.5% |
OmniAI Orchestrator
Omnichannel Customer Service Unification
Model Details
| Version | v1.8.0 |
| Channels | Web, WhatsApp, Telegram, Email, SMS, Social |
| Context Window | Full conversation history |
| Handoff Support | Seamless cross-channel |
Performance Metrics
| Workload Reduction | Up to 80% |
| First Response Time | <3 seconds |
| Resolution Rate | 78.4% |
| CSAT Score | 4.5/5.0 |
Intent Classification Engine
Shared Intent Detection Across All Products
Model Details
| Version | v4.0.2 |
| Architecture | Transformer-based Classifier |
| Intent Categories | 120+ intents |
| Inference Time | <50ms |
Performance Metrics
| Accuracy (Top-1) | 94.7% |
| Accuracy (Top-3) | 98.9% |
| F1 Score | 0.93 |
| Cross-language Parity | ±2% variance |
About Model Cards
Model Cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions. We follow the format introduced by Google Research (Mitchell et al., 2019).
Each model card includes: model details, intended use, factors, metrics, evaluation data, training data, quantitative analyses, ethical considerations, and caveats.
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