Model Evaluation Suite

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Know before you deploy.

Comprehensive testing and validation for AI models with automated red-teaming, bias audits, and continuous monitoring.

Purpose

The evaluation process assesses how closely a model’s output matches the expected result. This is crucial for understanding performance and identifying opportunities to improve before real-world deployment.

Outcome

The resulting evaluation provides clear insight into model accuracy and effectiveness, guiding users to refine and improve models for better real‑world performance.

On-Premise
CAPABILITIES

Comprehensive Model Validation

Scenario-Based Testing

Comprehensive test harnesses aligned with your specific domain and regulatory requirements.

Continuous Monitoring

Real-time scorecards integrated into CI/CD pipelines for automated quality assurance.

Automated Red-Teaming

Proactive security testing to identify vulnerabilities and potential attack vectors before deployment.

Bias Detection & Fairness

Comprehensive bias audits and fairness testing across different demographic groups and use cases.

Performance Analytics

Detailed performance metrics, regression detection, and comparative analysis across model versions.

Universal Compatibility

Test any LLM—proprietary, open source, or commercial—with the same comprehensive evaluation suite.

Deploy AI with confidence

Join organizations that deploy AI responsibly with comprehensive model evaluation and monitoring.

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ACCURACY TEST & MODEL EVALUATION

Accuracy Test & Model Evaluation

Metrics Used
  • BLEU Score: Measures how closely the model’s output matches the reference text by focusing on n‑gram precision.
  • ROUGE Score: Evaluates overlap between the model output and the reference text, emphasizing recall.
  • METEOR Score: Considers synonyms and word order for a more nuanced evaluation than BLEU or ROUGE.
  • BERTScore: Uses deep learning to compare semantic similarity between the model output and the reference text.
Process
  1. The model’s output is compared against a reference text (the expected answer).
  2. Each metric computes a score indicating alignment between output and reference.
  3. Scores highlight strengths and weaknesses to guide targeted improvements.