What actually happens when OpenVals validates an AI system?

Vishwanath Akuthota
8 hours ago
2 min read

You connect an AI model.
You run a benchmark.
You get a score.
Sounds simple.

But AI validation should go much deeper than that.

OpenVals is being built to examine the AI system layer by layer.

1. PROVIDER INTELLIGENCEOpenVals first understands the environment.

Is the provider configured?Is it reachable?Is authentication working?Which models are available?Is the environment benchmark-ready?

2. DATASET VALIDATIONBefore trusting the model, OpenVals checks the evaluation data.

Schema. Missing fields. Duplicates. Quality. Dataset health.

Because unreliable evaluation data can produce unreliable conclusions.

3. CONTROLLED MODEL EXECUTIONThe same evaluation conditions are applied across models so results can be meaningfully compared.

4. MULTI-DIMENSIONAL EVALUATIONOpenVals evaluates more than accuracy.

It examines dimensions such as factuality, semantic alignment, reliability, consistency, safety, hallucination risk, latency and other production-relevant characteristics.

5. HALLUCINATION & FACTUALITY ANALYSISDid the AI provide unsupported information?

Did it contradict known facts?

Did numerical information change?

OpenVals converts these behaviours into measurable evidence.

6. RELIABILITY TESTINGOne correct answer isn’t enough.

The same AI can be evaluated repeatedly to understand whether its behaviour remains consistent.

7. TRUST INTELLIGENCEThe results are translated into decision-oriented indicators such as:

Trust Score — overall measured trust characteristicsDRS — Decision Reliability ScoreHPI — Hallucination Probability Index

8. EXECUTIVE EVIDENCEFinally, technical measurements are converted into reports and recommendations that engineering teams and decision-makers can understand.

So instead of asking:

“Did the model pass the benchmark?”

OpenVals helps organizations ask a much better question:

“What evidence do we have that this AI is ready for our use case?” OpenVals validates: