RAIA - The AI Assurance Platform for AI Agents and ML Models

RAIA is an AI assurance platform that helps enterprises evaluate AI agents and machine learning models and make evidence-backed decisions about their readiness for deployment. AI systems can perform well in demonstrations yet behave unpredictably in real-world environments. Agents may make unsupported tool calls, exceed operational boundaries, fail to escalate appropriately, or produce inaccurate or biased outputs. Machine learning models may deliver strong overall performance while exhibiting fairness issues or making predictions that are difficult to explain. Engineering, risk, and compliance teams need evidence that these systems have been evaluated, their risks understood, and appropriate controls considered before deployment. RAIA addresses these challenges through structured evaluation, measurable assurance criteria, and actionable readiness recommendations. Register an AI agent or ML model, run an evaluation, and receive a readiness status of READY, CONDITIONAL, or NOT READY, supported by evaluation results, identified risks, and recommended remediation steps. RAIA informs deployment decisions while keeping people accountable for the final go or no-go decision. For AI agents, RAIA evaluates execution behaviour using traces collected through its lightweight SDK. Deterministic checks measure objectively verifiable properties such as task success, step efficiency, retries, and tool invocation validity. LLM-based evaluation assesses dimensions that require contextual judgment, including boundary compliance, escalation appropriateness, toxicity, bias, correctness, relevance, coherence, and response quality. Combining these approaches provides a broader view of agent behaviour than operational metrics alone. Critical safety and compliance breaches, such as boundary violations or unauthorized tool attempts, can trigger a NOT READY recommendation regardless of the overall score. For machine learning models, RAIA supports performance evaluation using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. It also supports fairness analysis using measures such as disparate impact, statistical parity, and equalized odds, alongside explainability techniques including SHAP. These capabilities help teams assess model performance, investigate potential fairness concerns, and understand the factors influencing predictions using their reference and inference datasets. Every evaluation produces a Readiness Report containing an evidence-backed scorecard, category-level results, threshold breaches, confidence information, and a Gaps and Recommendations section. Reports and evaluation history help engineering, risk, and compliance teams review findings, prioritize remediation, document assessment outcomes, and support internal governance processes. RAIA can help organizations generate evidence relevant to frameworks such as the EU AI Act, NIST AI Risk Management Framework, and internal model risk management policies. It does not replace an organization's legal, regulatory, or governance obligations. RAIA supports a straightforward workflow: register, assess, and review readiness. Teams can integrate agent traces through the SDK or evaluate ML models using their datasets. Evaluations can be initiated on demand, with scheduled evaluation capabilities available for supported workflows. RAIA is available as a multi-tenant SaaS platform with tenant isolation, JWT authentication, and configurable data retention. AWS Marketplace purchasing options include public pricing and private offers. Dedicated and on-premises container deployment options are available for organizations with additional security, deployment, or data residency requirements. RAIA brings agent and model assurance into one platform, helping organizations move beyond simply checking whether an AI system works to understanding how it performs, where risks exist, and what needs attention before making deployment decisions. Its goal is to make AI assurance evidence-based, actionable, and repeatable throughout the AI lifecycle.