Our Methodology
TrustVector evaluates AI systems through a rigorous, transparent, and evidence-based framework. Every score is backed by verifiable sources and documented methodologies.
Core Principles
Evidence-Based
Every score requires documented evidence from official sources, research papers, or verified testing results.
Transparent
All evaluation criteria, methodologies, and confidence levels are publicly documented and verifiable.
Community-Driven
Open-source evaluations reviewed by the community. Anyone can contribute improvements or new evaluations.
Continuously Updated
Evaluations are regularly updated as new versions, features, and research become available.
Five Trust Dimensions
1. Performance & Reliability
Measures task accuracy, output consistency, latency, uptime, and overall system reliability.
- Task completion accuracy (benchmarks like HumanEval, MMLU, SWE-bench)
- Output consistency and determinism
- Response latency (p50, p95)
- Uptime SLA and availability
- Context window and multimodal support
2. Security
Evaluates resistance to attacks, data protection, and security posture of the AI system.
- Jailbreak resistance and prompt injection defense
- Data leakage prevention
- Adversarial robustness
- Content filtering and safety guardrails
- Access controls and authentication
3. Privacy & Compliance
Assesses data handling practices, regulatory compliance, and privacy protections.
- Data retention policies and user control
- GDPR, HIPAA, and SOC 2 compliance
- Data sovereignty and geographic controls
- Encryption at rest and in transit
- Training data usage policies
4. Trust & Transparency
Evaluates documentation quality, model transparency, and organizational trustworthiness.
- Model documentation completeness
- Training data transparency
- Safety testing and bias evaluation disclosure
- Decision explainability
- Version management and changelogs
5. Operational Excellence
Measures ease of use, deployment flexibility, cost efficiency, and operational maturity.
- Deployment flexibility (API, self-hosted, cloud platforms)
- API reliability and rate limits
- Cost efficiency and pricing model
- Monitoring and observability tools
- Documentation and support quality
Scoring System
Score Ranges (0-100)
Confidence Levels
Contribute to TrustVector
Help improve AI transparency by contributing evaluations, suggesting improvements, or reporting issues.