Answers questions about channel-level return rates, return reasons by product category, reverse logistics disposition recovery, restock turnaround performance, and policy abuse alerts. Orchestrates two sub-agents: **Data Insights**, which queries BigQuery via the Conversational Analytics API and BigQuery's built-in forecasting/contribution/anomaly-detection tools, and **Market Context**, which answers external questions via Google Search grounding.
Duration5:45 (Normal Pacing)
Resolution1080p Full HD (1920×1080)
UI Scaling1.25x High-DPI Scaled Text
Model Runtimegemini-3.5-flash (Vertex AI)
📋 Multi-Turn Conversation Flow
Turn 1 (Data Insights / BigQuery):"What is our overall return rate by channel and top return reason codes for Apparel?" — Synthesizes internal BigQuery conversational analytics query and computes KPI summary.
Turn 2 (Market Context / Google Search):"What are current retail benchmarks for e-commerce vs in-store return rates in 2026?" — Grounds analysis against external retail benchmarks and industry context.
Turn 3 (Visual Artifact / Matplotlib):"Render a bar chart comparing monthly return rates across sales channels for July 2026." — Generates and renders a custom chart visualization artifact inline.
Turn 4 (Executive Canvas Presentation):"Create a 4-slide executive presentation summarizing the Returns & Reverse Logistics analysis and recommendations above." — Automatically creates a 4-slide deck and showcases each slide via the bottom thumbnail rail.