AI Simulation Report: Insurance AI Agent
This is a real Arato simulation of an auto insurance company’s AI assistant. In insurance, a wrong answer isn’t a UX complaint, it’s a claim promised that was never approved, a payout released to the wrong person, or a compliance step skipped. See exactly what gets surfaced, and what most teams miss before deployment.
No Setup. No integration. Just real findings on how your AI really works.
Simulation Summary & Risk Score
Key Insight: The Simulation Summary gives you the full picture in one view – pass rate, main risks, response time and length, so you know exactly where your AI is excelling and where it’s falling short, before you dig into any specific conversation.
→ Instant risk assessment: Get a high-level, data-driven snapshot of your AI assistant’s readiness, before it touches a real customer’s claim.
→ Critical severity tiers: Every finding is tagged by severity based on what matters most to your business, so the failures that put customers or compliance at risk surface as Critical.
→ Operational metrics: Track performance indicators like Response Time and Response Length alongside every finding.
Detailed Behavioral Analysis
Key Insight: Breaking findings into pillars reveals patterns across customer profiles, showing your team exactly where to focus fixes for the highest impact, for example, the same denial-reason inconsistencies and deductible errors kept surfacing across multiple different customer profiles.
→ Automated Red Teaming: See how your AI assistant handles both routine requests (like explaining a policy’s coverage) and adversarial ones (like a customer pressuring the assistant into approving a claim it has no authority to approve).
→ Critical pillars breakdown: Evaluate performance across all five dimensions, Accuracy, Compliance, Ground Truth, Clarity, and User Experience, then see which ones this chatbot actually struggles with.
→ Visual radar chart: Instantly see where your AI underperforms, in this case, Compliance and Ground Truth, the two dimensions tied directly to regulatory exposure.
Ready to map your AI system’s health?
User Journeys & Sessions Analysis
Key Insight: Every finding comes with the evidence behind it, the full conversation, a screen recording showing you exactly where things went wrong, and the session ID, so your team can go straight to the source instead of taking the score at face value.
→ Visual conversation evidence: Review the exact exchange where the chatbot promised a denied claim would be “reprocessed and approved,” a decision it had no authority to make.
→ Timeline-mapped findings: See exactly which turn in the conversation each finding is tied to, the turn where the chatbot’s denial reason contradicted the customer’s file, and the turn where it kept handling the dispute instead of escalating.
→ Root-cause analysis: Trace exactly why the chatbot believed it had the authority to promise a claim approval, a gap between what the assistant can see and what it’s actually allowed to commit to, without digging through backend logs.