Validate AI
Find out what your AI actually demonstrated.
System → evaluation protocol → test population → execution → human and expert assessment → inspection → evidence.
Typical term · Fixed project against a defined evaluation scope
For whom
Head of AI, ML engineering, AI product, QA, responsible AI, model risk.
When
A system exists and the organization needs to know whether it works for the job it is supposed to do — including where it fails.
You provide
- The system under test (or a distinct registered endpoint)
- Intended use and evaluation objectives
- Constraints (safety, rights, loss, mission) that can block even when the score is high
In the package
- Evaluation protocol and test population
- Human, expert and automated evaluation as required
- Execution, inspection and failure analysis
- Version comparison when a distinct system is presented
Activities inside the package
These are methods inside the package, not a menu of things to buy.
Model evaluation · RAG evaluation · Agent evaluation · Expert review · Red-teaming · Human evaluation · Benchmarking · Regression evaluation
You receive
Validate AI Evidence Package
Who performs
Qualified evaluators and domain specialists matched to the risk of the field.
Quality
A score is evidence. It is not a certificate. A high score does not automatically mean Ready. A targeted failure may make a defined use Not Ready.
Boundary
A score is evidence. It is not a certificate.
A high score does not automatically mean Ready. A targeted failure may make a defined use Not Ready.
What this is not
- Not a leaderboard
- Not a claim that the AI is universally safe
- Not authorization to deploy