🍕 LIVE DEMO — John’s of Arthur Avenue · AI-Powered Restaurant Intelligence | Inflexis AI · Powered by AIXaaS™
Demo —Loading Restaurant Intelligence Platform…
📋 Presenter Script
0:00John's of Arthur Avenue. 50-location pizza franchise. Real PrISM POS data, supplier invoices, and call recordings loaded into AIXaaS.
0:14Scene 1: AI phone ordering. Watch the voice agent handle a live customer call, recognize a returning customer, take an order, and suggest an upsell.
0:36The agent identified Maria as a returning customer, remembered her usual order, suggested garlic knots, and routed to PrISM POS. 47-second call. Zero escalations.
1:00Scene 2: Supplier pricing intelligence. The food cost engine compares pricing across US Foods, Ferraro, and Sysco in real time.
1:18Mozzarella alone: Ferraro charges $3.89 per pound versus $3.42 at US Foods. At 50 locations that is $24,000 per year on one ingredient. Total projected savings: $127,400.
1:40Restaurant operators know their suppliers are overcharging. This tool shows them exactly where and by how much.
1:54Scene 3: demand forecasting. Combining PrISM POS sales data with weather, local events, and demographic intelligence.
2:10Saturday with a Yankees game at nearby stadium. Historical correlation: plus 34% pizza orders. Recommended prep: 840 pies, 12 staff on shift, extra mozzarella delivery Friday.
2:34Three AI capabilities for one franchise. Phone ordering saves labor. Cost comparison saves margin. Demand forecasting prevents waste and stockouts.
2:48John's of Arthur Avenue. Powered by Inflexis AI and AIXaaS. Every data point came from John's real operational data.
JA
John’s of Arthur AvenueInflexis AI · 50-Location Pizza Franchise · Powered by AIXaaS™
📚 Knowledge BaseConnecting…
✅ Demo complete — Explore mode active. Click any question below, use the pattern library, or type your own question.
🔍 Query BuilderClient Operations · 2,847 chunks
💡 Analysis Result
🔍 This is an AIXaaS demo loaded with John’s of Arthur Avenue operational data. I don’t have a scripted response for that specific question — try one of these:
Supplier pricing — "Compare mozzarella pricing across suppliers"
Cost trends — "What ingredients are rising in price?"
Demand forecasting — "Forecast this Saturday with Yankees game"