Get started
Why OpenVals
Problem
Most AI models perform well in demonstrations. Production environments require something different:
- Can the model be trusted?
- Is the response factually correct?
- How reliable is the model under repeated execution?
- What is the hallucination risk?
- Is the dataset itself trustworthy?
- Is the model ready for enterprise deployment?
OpenVals was built to answer these questions.
Why OpenVals?
A side-by-side comparison of capabilities between traditional benchmarks and OpenVals:
| Capability | Traditional Benchmarking | OpenVals |
|---|---|---|
| Accuracy | ✓ | ✓ |
| Latency | ✓ | ✓ |
| Semantic Similarity | ✓ | ✓ |
| Hallucination Detection | Limited | ✓ |
| Factuality Analysis | Limited | ✓ |
| Trust Scoring | ✗ | ✓ |
| Governance Readiness | ✗ | ✓ |
| Executive Reporting | ✗ | ✓ |
| Enterprise Validation | ✗ | ✓ |
Enterprise Use Cases
AI Model Selection
Compare GPT, Claude, Llama, Mistral, and private models before deployment.
Private AI Validation
Validate enterprise AI running on Ollama, vLLM, or self-hosted infrastructure.
AI Procurement
Benchmark vendor AI solutions before purchasing decisions.
AI Governance
Measure AI readiness against organizational trust and governance requirements.
AI Red Teaming Foundation
Identify hallucination risk, factual weaknesses, and trust gaps.
Executive Reporting
Generate trust dashboards and executive-level AI readiness reports.
What It Solves
Aligns evaluation with business objectives
Supports deployment decision-making
Quantifies trust, risk, and performance
Evaluates model performance
Benchmarks multiple models
Normalizes and compares results
Introduces trust before deployment
Validates numeric and semantic factuality
Measures hallucination risks
Assures dataset integrity and health