OpenVals
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.
OpenVals
Trust Infrastructure for AI
What is OpenVals?
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?
Why OpenVals?
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?
| 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.
Core Platform Capabilities
AI Evaluation Engine
Evaluate AI systems using multiple dimensions:
- Accuracy
- Semantic Similarity
- Reliability
- Safety
- Consistency
- Variance
- Latency
- Factuality
- Hallucination Risk
Decision Reliability Score (DRS)
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?"
Factuality Engine
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
Hallucination Probability Index (HPI)
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
Dataset Intelligence
Trust the dataset before trusting the model.
Dataset Validation CLI includes:
- Schema validation
- Quality validation
- Duplicate detection
- Missing field detection
- Dataset Health Score (DHS)
60-Second Quick Start
Install:
pip install openvals
Benchmark:
openvals benchmark \
--dataset finance \
--models mistral,llama3
Output:
Model Accuracy DRS
--------------------------------
llama3 91.4 89.2
mistral 87.8 82.4
QWEN 70.7 69.7
validate-dataset examples
openvals validate-dataset finance
openvals validate-dataset ./customer_dataset.json
openvals validate-dataset ./customer_dataset.csv
openvals validate-dataset finance
Benchmark multiple models:
openvals benchmark \
--dataset finance \
--models mistral,llama3 \
--config finance
Parallel Execution Engine
OpenVals supports parallel model execution for faster benchmarking.
openvals benchmark \
--dataset finance \
--models mistral,llama3 \
--parallel \
--max-workers 2
Benefits:
- Reduced benchmark runtime
- Better scalability
- Future SaaS readiness
Show version:
openvals version
Example Output
===================================================
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
Screenshots
Trust Dashboard
Sample Evaluation Report
Dataset Validation
Multi-Model Benchmarking
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
Supported Benchmark Domains
Current datasets:
- Finance
- Healthcare
- Cybersecurity
- Legal
- Insurance(coming soon)
- Manufacturing(coming soon)
- Retail(coming soon)
- Enterprise Operations
- Software Engineering
- Math
- Reasoning
OpenVals Architecture
Enterprise Dataset ↓ Dataset Validation ↓ AI Evaluation Engine ↓ Trust Intelligence Layer ↓ Factuality Engine ↓ Hallucination Detection ↓ Decision Reliability Score ↓ Executive Reporting
OpenVals Ecosystem
OpenVals is part of a larger AI Trust & Assurance ecosystem.
OpenVals
AI Validation & Trust Intelligence
AI Compass
AI Maturity & Readiness Assessment
DrPinnacle
AI Strategy, Governance & Advisory
OpenVals Cloud (Coming Soon)
Continuous AI Validation Platform
Vision
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.
Evaluation vs Validation
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.
Contributing
Contributions are welcome.
- Fork the repository
- Create a feature branch
- Submit a pull request
License
Dr.Pinnacle Community Edition License (DPCL-CE) v1.0
Developed By
DrPinnacle -- AI Trust, Validation & Governance Initiative
OpenVals
Keywords
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
Metadata
Release files for openvals 0.5.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| openvals-0.5.5.tar.gz | 65.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| openvals-0.5.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 158.4 kB
Release files / openvals-0.5.5.tar.gz
| Download URL | openvals-0.5.5.tar.gz |
|---|---|
| Size | 65.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
8f5884ac85600c49e8d2bf712fe80208a79a4345cba7d3ab5c9d41881f694bb0
|
|
BLAKE2b-256 checksum How to use checksums |
06c512e74f389d50e63af01f26f3baa51940e37a07f92064cbbefaa67420071e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 3, 2026.
Transparency logRelease files / openvals-0.5.5-py3-none-any.whl
| Download URL | openvals-0.5.5-py3-none-any.whl |
|---|---|
| Size | 93.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
14f8d2c39cb0eae1882c8d01e8e4f03f0e029bee37e967be040dfb656a59766c
|
|
BLAKE2b-256 checksum How to use checksums |
3439dbd6f142398f28d08082c59bb5a89be35830b5b324b69d0dcfe2a52fd52a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 3, 2026.
Transparency log