AI & GenAI System Testing
Comprehensive testing and validation of AI and generative AI systems to ensure accuracy, reliability, safety and governance. We assess behaviour, outputs and risk of AI systems.
Overview
AI and GenAI systems require specialized testing approaches to validate model accuracy, output quality, bias detection, safety measures and governance frameworks. AssureSQ provides independent assessment of AI systems across multiple dimensions.
Testing Areas
- AI Model Accuracy Testing: Validation of model predictions, classification accuracy, regression performance and output correctness against ground truth data.
- LLM Validation: Testing of large language models for response quality, coherence, factual accuracy, context understanding and prompt adherence.
- Bias and Safety Testing: Detection of algorithmic bias, fairness assessment, safety guardrails validation and ethical AI compliance.
- Prompt Reliability Testing: Consistency testing across prompt variations, prompt injection resistance and prompt engineering validation.
- AI Risk Analysis: Assessment of AI system risks, failure modes, adversarial robustness and explainability requirements.
- Governance and Compliance: Review of AI governance frameworks, model documentation, data lineage and regulatory compliance.
Scoring Output
- AI Quality Score (0–100) — Overall AI system quality rating
- Model Accuracy Score — Performance and correctness metrics
- Bias and Safety Rating — Fairness and safety assessment
- Reliability Score — Consistency and robustness rating
- Risk Level — AI risk exposure and mitigation status
- Benchmark Comparison — Industry and peer comparison
Request an AI Testing Assessment
Get an AI quality score and improvement roadmap for your AI and GenAI systems.
Request Assessment Get Quality Score Back to Software & AI QualityCommon Challenges
Issues organizations face that drive the need for independent assessment
Output Inconsistency
AI and GenAI models produce different outputs for similar inputs, making quality assurance unpredictable and difficult.
Hallucination and Accuracy
Language models generate plausible but incorrect information that can mislead users and damage business credibility.
Bias and Fairness
AI systems may exhibit biases from training data that lead to unfair outcomes, legal exposure and reputational risk.
Safety and Alignment
GenAI applications may generate harmful, inappropriate or off-brand content without proper guardrails and testing.
Testing Methodology Gap
Traditional software testing methods do not adequately cover AI-specific quality dimensions like output quality, consistency and safety.
Regulatory Pressure
EU AI Act, India AI governance framework and industry regulations increasingly require demonstrated AI quality and safety testing.
How AssureSQ Helps
Independent testing, scoring and improvement guidance
AI Output Quality Testing
Structured evaluation of AI system outputs for accuracy, relevance, completeness and consistency across diverse input scenarios.
Hallucination Detection
Systematic testing to identify when models generate factually incorrect or fabricated information, with severity classification and coverage metrics.
Bias and Fairness Assessment
Testing across demographic dimensions, use cases and edge cases to identify and quantify biases in AI outputs and decision-making.
Safety and Guardrail Validation
Testing content safety filters, prompt injection defences, jailbreak resistance and alignment with acceptable use policies.
AI Quality Score
A structured quality score for AI systems covering accuracy, consistency, safety, fairness and operational reliability.