What is AI quality assurance?
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AI quality assurance is a comprehensive testing and verification process that evaluates AI systems for correctness, safety, fairness, robustness, and compliance with regulatory standards. It ensures systems work as intended and meet established quality criteria before deployment.
Why is QA testing important for AI systems?
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AI systems can exhibit unexpected behaviors, biases, and vulnerabilities that impact users and organizations. Rigorous QA testing identifies these issues before production deployment, reducing risks, ensuring compliance, and building user trust in AI systems.
What certification levels does Orbit-Q offer?
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We offer three CASA certification levels: Level 1 (Foundation) for basic testing, Level 2 (Professional) for production systems, and Level 3 (Enterprise) for mission-critical applications. Each level has specific requirements and recertification schedules.
How long does certification take?
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Timeline varies by certification level: Level 1 typically takes 2-4 weeks, Level 2 takes 6-12 weeks, and Level 3 takes 3-6 months. Timelines depend on system complexity and testing requirements.
What is continuous monitoring?
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Continuous monitoring tracks AI system performance in production, detecting drift, degradation, anomalies, and behavioral changes in real-time. This enables proactive issue identification and prevents quality issues from impacting users.
How does red team testing work?
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Red teams simulate adversarial attacks, including prompt injection, jailbreaks, context confusion, and output poisoning. These simulations identify vulnerabilities before malicious actors can exploit them, strengthening system security.
What standards does Orbit-Q align with?
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We align with CASA, ISO 42001, NIST AI Risk Management Framework, and BSI standards. Our certifications provide mutual recognition with international bodies, ensuring compliance across jurisdictions.
How is bias detected and quantified?
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We use systematic testing across demographic groups, protected attributes, and intersectional categories. We measure demographic parity, equalized odds, and disparate impact using established fairness metrics and custom analysis.
Can your services cover custom AI architectures?
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Yes, we work with any AI architecture including LLMs, transformers, traditional ML models, computer vision systems, and custom implementations. Our methodologies are flexible and scalable across different system types.
What is the cost of certification?
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Pricing depends on system complexity, certification level, and testing scope. We provide custom quotes after initial assessment. Most organizations invest 0.5-2% of development budget for comprehensive quality assurance.
How often must systems be recertified?
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Recertification frequency depends on the certification level and system type: Level 1 annually, Level 2 every 18 months, Level 3 every 12 months. Significant system changes may require interim assessments.
What documentation do you provide?
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We provide comprehensive test reports, vulnerability assessments, certification documents, audit trails, compliance matrices, executive summaries, and technical documentation suitable for regulatory submission and internal governance.