Model Evaluation
Trusted Evaluation Frameworks for AI Systems That Power Critical Decisions
In high-stakes environments, AI systems must be more than functional, they must be accountable, fair, robust, and aligned with business goals. Qualitest’s Model Evaluation & Safety services are designed to provide end-to-end visibility into your AI’s performance, and potential risk, before and after deployment.
From enterprise-grade LLMs to traditional machine learning models, we combine human expertise with automated rigor to help companies deploy AI systems they can trust.
Model Benchmarking
Evaluate models beyond metrics. Validate performance in context.
We perform structured benchmarking across your AI lifecycle, measuring not just how a model performs in isolation, but how it behaves under real-world conditions, edge cases, and adversarial pressure.
Key Capabilities:
- Full System & Business Process Validation
Assess the AI’s end-to-end alignment with operational workflows. - Precision, Recall, Relevance & Stability Metrics
Including BLEU, ROUGE, Perplexity, and Contextual Accuracy. - Adversarial Testing
Red teaming, prompt attacks, model tricking, and jailbreaking evaluations.
- Safety & Toxicity Checks
Monitor outputs for bias, fairness, hallucinations, and stereotyping. - Cross-Model Comparisons
Objective benchmarking against industry baselines and proprietary scoring systems. - Security Evaluations
Assess susceptibility to data poisoning, prompt injection, and malicious prompt manipulation.
Real-World Scenarios Simulated:
- Chain-of-thought reasoning
- Emotion-based and zero-shot prompting
- Historical, cultural, and geopolitical sensitivity simulations
Monitoring & Human Feedback
Maintain reliability post-deployment with dynamic monitoring and expert oversight.
Even the most accurate AI models require vigilance once in production. We enable continuous evaluation to detect drift, degradation, and behavioral shifts, integrating human feedback loops for nuanced understanding.
Our Approach
- Production Monitoring
Detect data drift, input anomalies, and performance degradation. - Bias & Fairness Audits
Evaluate demographic parity, stereotype probes, and cultural sensitivity. - User Experience Testing
Capture human feedback through structured A/B testing and survey-based assessments.
- Human-in-the-Loop (HITL) Systems
Subject matter experts provide judgment where automation alone is insufficient. - Crowd-sourced Testing & Red Teaming
Simulate real-world usage and edge scenarios to validate resilience.
All monitoring insights feed directly into model improvement cycles, closing the feedback loop from deployment to enhancement.
Get started with a free 30 minute consultation with an expert.