AI Simulation Report: E-commerce AI Agent
This is a real Arato simulation of an e-commerce company’s AI assistant. In e-commerce, a wrong answer isn’t a UX complaint, it’s often an order confirmed for stock that doesn’t exist, a refund issued with no verification, or a price calculated wrong. 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 order.
→ Critical severity tiers: Every finding is tagged by severity based on what matters most to your business, so the failures that put customers or revenue 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 customs-fee inconsistencies and discount-code errors kept surfacing across multiple different customer profiles.
→ Automated Red Teaming: See how your AI assistant handles both routine requests (like checking an order status) and adversarial ones (like a customer trying to stack two non-stackable discount codes).
→ 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, Ground Truth and Accuracy, the two dimensions behind confirming orders that don’t exist in inventory and processing refunds with no way to verify the claim.
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 confirmed an order for an item that’s actually out of stock, no inventory check, no caveat.
→ Timeline-mapped findings: See exactly which turn in the conversation each finding is tied to, the turn where the chatbot confirmed the order, and the turn where it sent written confirmation without ever surfacing the known problem.
→ Root-cause analysis: Trace exactly why the chatbot missed two separate chances, a status check and a request for written confirmation, to disclose a fulfillment problem it already knew about, without digging through backend logs.