AI Trust Intelligence Platform for LLMs, SLMs, Private AI, and Enterprise AI Systems
Evaluate • Benchmark • Trust Intelligence
OpenVals is an enterprise-grade AI evaluation and trust platform designed to help organizations measure, compare, validate, and deploy AI systems with confidence.
Unlike traditional AI benchmarks that focus only on accuracy, OpenVals evaluates performance, trustworthiness, factuality, reliability, safety, hallucination risk, governance readiness, and deployment confidence.
Trust Infrastructure for AI
OpenVals is an AI Trust Intelligence Platform that helps enterprises evaluate, validate, benchmark, and govern AI systems before production deployment.
OpenVals answers one question:
Can you trust your AI?
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.
| Capability | Traditional Benchmarking | OpenVals |
|---|---|---|
| Accuracy | ✓ | ✓ |
| Latency | ✓ | ✓ |
| Semantic Similarity | ✓ | ✓ |
| Hallucination Detection | Limited | ✓ |
| Factuality Analysis | Limited | ✓ |
| Trust Scoring | ✗ | ✓ |
| Governance Readiness | ✗ | ✓ |
| Executive Reporting | ✗ | ✓ |
| Enterprise Validation | ✗ | ✓ |
Compare GPT, Claude, Llama, Mistral, and private models before deployment.
Validate enterprise AI running on Ollama, vLLM, or self-hosted infrastructure.
Benchmark vendor AI solutions before purchasing decisions.
Measure AI readiness against organizational trust and governance requirements.
Identify hallucination risk, factual weaknesses, and trust gaps.
Generate trust dashboards and executive-level AI readiness reports.
Evaluate AI systems using multiple dimensions:
- Accuracy
- Semantic Similarity
- Reliability
- Safety
- Consistency
- Variance
- Latency
- Factuality
- Hallucination Risk
OpenVals introduces the Decision Reliability Score (DRS), a deployment-focused trust metric designed to determine whether an AI system is suitable for real-world production environments.
DRS combines:
- Accuracy
- Semantic Intelligence
- Reliability
- Safety
- Consistency
- Variance
- Latency
- Hallucination Risk
- Factuality
Traditional leaderboards answer:
"Which model scored highest?"
DRS answers:
"Which model can be trusted in production?"
OpenVals includes a dedicated factuality scoring engine capable of:
- Semantic factual alignment
- Numeric consistency validation
- Contradiction detection
- Factual risk classification
Output:
Factuality Score
Risk Level
Issues Detected
OpenVals introduces HPI (Hallucination Probability Index).
HPI estimates the probability that a model response contains hallucinated or unreliable content.
Risk Levels:
- Low
- Medium
- High
- Critical
Trust the dataset before trusting the model.
Dataset Validation CLI includes:
- Schema validation
- Quality validation
- Duplicate detection
- Missing field detection
- Dataset Health Score (DHS)
Install:
pip install openvalsBenchmark:
openvals benchmark \
--dataset finance \
--models mistral,llama3Output:
Model Accuracy DRS
--------------------------------
llama3 91.4 89.2
mistral 87.8 82.4
QWEN 70.7 69.7
openvals validate-dataset financeopenvals validate-dataset ./customer_dataset.jsonopenvals validate-dataset ./customer_dataset.csvopenvals validate-dataset financeopenvals benchmark \
--dataset finance \
--models mistral,llama3 \
--config financeOpenVals supports parallel model execution for faster benchmarking.
openvals benchmark \
--dataset finance \
--models mistral,llama3 \
--parallel \
--max-workers 2Benefits:
- Reduced benchmark runtime
- Better scalability
- Future SaaS readiness
Show version:
openvals version===================================================
OpenVals Trust Intelligence Report
===================================================
Model: llama3
Accuracy Score : 91.4
Semantic Score : 89.1
Factuality Score : 92.3
Safety Score : 95.2
Latency Score : 83.0
Hallucination Risk : LOW
Decision Reliability Score (DRS)
89.2 / 100
Deployment Status:
READY FOR PRODUCTION
Compare multiple models under identical conditions.
Supported:
- Ollama Models
- Local Models
- Private AI
- Enterprise AI
- Future API-based providers
Capabilities:
- Side-by-side comparison
- Normalized ranking
- DRS ranking
- Trust Intelligence reporting
Current datasets:
- Finance
- Healthcare
- Cybersecurity
- Legal
- Insurance(coming soon)
- Manufacturing(coming soon)
- Retail(coming soon)
- Enterprise Operations
- Software Engineering
- Math
- Reasoning
Enterprise Dataset ↓ Dataset Validation ↓ AI Evaluation Engine ↓ Trust Intelligence Layer ↓ Factuality Engine ↓ Hallucination Detection ↓ Decision Reliability Score ↓ Executive Reporting
OpenVals is part of a larger AI Trust & Assurance ecosystem.
AI Validation & Trust Intelligence
AI Maturity & Readiness Assessment
AI Strategy, Governance & Advisory
Continuous AI Validation Platform
OpenVals is building the Trust Intelligence Layer for AI.
The future of AI is not determined by which model is largest.
The future belongs to AI systems that can be measured, validated, governed, and trusted.
Most platforms evaluate AI.
OpenVals validates trust.
Evaluation answers:
"How well does the model perform?"
Validation answers:
"Can the model be trusted in production?"
OpenVals was built around this distinction.
Contributions are welcome.
- Fork the repository
- Create a feature branch
- Submit a pull request
Dr.Pinnacle Community Edition License (DPCL-CE) v1.0
DrPinnacle -- AI Trust, Validation & Governance Initiative
AI Evaluation Platform, AI Trust Platform, LLM Evaluation, AI Benchmarking, AI Governance, AI Validation, Factuality Scoring, Hallucination Detection, DRS Score, AI Trust Intelligence, Enterprise AI Validation, Private AI Evaluation, Ollama Benchmarking, AI Reliability Testing, OpenVals, Vishwanath Akuthota