Answers questions about weekly sell-through %, stock turn, aging inventory breakdown, weeks of supply, and markdown risk triggers. 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 market trends and retail industry benchmark 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 the overall sell-through rate and aging inventory breakdown for SKU-001 in July 2026?" — Synthesizes internal BigQuery conversational analytics query and computes KPI summary.
Turn 2 (Market Context / Google Search):"What are industry standard inventory sell-through benchmarks for seasonal retail apparel?" — Grounds analysis against external retail benchmarks and industry context.
Turn 3 (Visual Artifact / Matplotlib):"Render a horizontal bar chart comparing store sell-through percentages against our 70% target." — Generates and renders a custom chart visualization artifact inline.
Turn 4 (Executive Canvas Presentation):"Create a 4-slide executive presentation summarizing the Sell-Through & Inventory Health analysis and recommendations above." — Automatically creates a 4-slide deck and showcases each slide via the bottom thumbnail rail.