Model Quality Monitoring
1. What We Guarantee
Our model quality assurance has two pillars:
- Authenticity assurance: Ensuring that the models served match their declared identities -- detecting reverse-engineered, re-labeled, or misrepresented models
- Availability assurance: Continuously monitoring upstream channel and model health (success rate, latency, failure trends), with automated alerting, isolation, and recovery
2. Operational Quality Metrics
2.1 Core Metrics
AIone monitors the following metrics in real time:
| Metric | Description | Alert Threshold |
|---|---|---|
| Success Rate | Proportion of successful requests | < 99% |
| P50 Latency | Response time for 50% of requests | Varies by model |
| P99 Latency | Response time for 99% of requests | > 3x baseline |
| Error Rate | Proportion of 5xx errors | > 1% |
| Timeout Rate | Proportion of timed-out requests | > 0.5% |
2.2 Automated Alerting
When metrics exceed thresholds, the system automatically triggers an escalation workflow:
- Immediate alert: Operations team is notified
- Auto-failover: Traffic is shifted from the unhealthy channel to a backup channel
- Manual intervention: Operations team investigates and resolves the issue
- Recovery verification: Traffic is gradually restored after the fix is confirmed
2.3 Multi-Channel Redundancy
Each model family is backed by multiple upstream channels:
- Claude: Multi-region AWS Bedrock accounts
- GPT: OpenAI API + Azure OpenAI dual-channel
- Gemini: GCP Vertex AI across multiple regions
When one channel experiences issues, traffic automatically fails over to a backup -- completely transparent to the caller.
3. Model Authenticity Assurance
3.1 Official Channel Access
All models are accessed through official cloud service APIs. We never use:
- Reverse-engineered APIs
- Unauthorized third-party proxies
- Re-labeled or impersonated models
3.2 Response Validation
The system runs periodic benchmark tests:
- Tests each model with a standardized prompt set
- Compares response quality against direct calls to the official API
- Verifies that model capabilities match expectations (e.g., Opus should significantly outperform Sonnet)
4. Transparency Commitments
4.1 Data Transparency
- Token consumption, latency, and model version for every API call are visible in the console
- The usage statistics page provides detailed breakdowns by model, date, and key
4.2 Status Transparency
- Public status page: 24/7 visibility into the health of all service components
- Changelog: All platform changes are publicly documented
4.3 Incident Transparency
- Full tracking through the ticket system
- Post-incident reports provided for major outages
5. SLA Commitments
| Metric | Target |
|---|---|
| Monthly Availability | 99.9% |
| Incident Response Time | Within 1 hour for critical issues |
| Incident Recovery Time | Within 30 minutes |
If SLA targets are not met, customers may request service credit compensation through the ticket system.