Accuracy and usefulness
See whether outputs are correct, complete, and usable for the job, not only fluent.
Sinorax AI Evaluate
Test and measure AI model performance.
Sinorax AI Evaluate is the testing and measurement layer in our end-to-end enablement platform, compare models with automated metrics and structured review, then decide from evidence as part of We Test. We Train. We Validate.
What you can measure
Criteria that match the decision you need to make.
Set the quality, safety, reasoning, and operational standards that matter to your project, then compare models on the same work.
- Accuracy
- Usefulness
- Safety
- Policy
- Reasoning
- Instruction following
- Model comparison
- Structured review
Safety and policy
Surface unsafe recommendations, leakage, and instruction-following failures before they reach users.
Reasoning quality
Review explanations, trade-offs, and domain judgment with qualified specialists when the stakes are high.
Evaluation engine
Compare models against the same evidence.
Turn every evaluation into a performance baseline that guides model selection, iteration, and improvement.
Model comparison
Compare candidate models against the same tasks, prompts, and performance breakdowns to see meaningful differences clearly.
Customizable benchmarking criteria
Set evaluation criteria around the quality, safety, reasoning, and operational standards that matter to your project.
Structured evaluation
Combine automated evaluation metrics with clear review criteria for a consistent and transparent assessment process.
Continuous improvement
Turn every evaluation into a performance baseline that guides model selection, iteration, and improvement.
Strategic evaluation
Performance breakdowns that lead to better next decisions.
Sinorax AI Evaluate makes model suitability testing visible across use cases, helping teams identify where a model performs, where it fails, and which improvements are worth prioritizing.
- Automated evaluation metrics for repeatable measurement.
- Structured review for nuanced, contextual assessment.
- Transparent criteria across every evaluation cycle.
A repeatable cycle
From sample to decision, then back again.
Define the job, select the sample, match the expertise, collect structured judgments, and keep the same set as a baseline when the model or prompt changes.
- Use real outputs or realistic prompts, including costly edge cases.
- Match reviewers to the domain with qualified specialists aligned to the task.
- Record disagreement so uncertain cases stay visible.
Start with evidence
See how your AI performs under structured review.
Bring a representative sample or describe the system you want to compare. We will help define the criteria, the review profile, and the evidence worth collecting, as part of your end-to-end AI enablement plan.
Start a project