Engineering an edge-native Computer Vision mesh and predictive demand engine to synchronize kitchen production with real-time guest consumption telemetry.
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Global resort group managing 100+ high-volume buffet and banquet outlets requiring real-time inventory oversight.
Computer Vision Lead + 2 Backend Engineers + IoT Architect embedded within Culinary Operations.
Eliminating over-production waste without compromising guest satisfaction or 'full-plate' visual standards.
YOLOv8 edge inference, Kafka event-bus, and demand-prediction models synced with POS and IoT sensors.
The client’s high-volume catering operations relied on static 'prep-sheets' based on historical occupancy. This disconnected kitchen output from actual consumption velocity, resulting in a consistent 18-22% food waste rate in buffet environments.
The risk was financial and environmental: escalating raw material costs and sustainability mandates required a shift from 'Guesstimation' to 'Telemetry-Driven Prep.' Kitchens needed to know exactly what was being eaten—and how fast—to stall production of low-demand items.
Kitchen staff relied on intermittent visual checks of buffet lines, leading to late reactions.
Continuous per-tray fill level tracking with automated alerts for production slowing.
Prep lists fixed 24 hours in advance based on guest headcounts only.
Autonomous adjustment of prep volume based on live consumption and arrival velocity.
Manual waste bucket weighing, prone to human error and lack of item-level detail.
Precise, item-level waste tracking via vision sensors with automated regulatory reporting.
Processing video streams at the edge to identify tray depletion rates without overloading cloud bandwidth.
Embedded PII scrubbing masks guest faces at the camera level, satisfying strict GDPR/SOC2 hospitality privacy standards.
Intelligent 'Fire' directives prioritize high-demand items while auto-suppressing prep for stagnating buffet stations.
Pre-built templates for high-velocity object detection and fill-level analysis in retail/F&B.
Production-ready streaming models that correlate seasonal event data with live consumption.
Real-time monitoring for kitchen efficiency, station latency, and ingredient-level waste distribution.
Automated procurement right-sizing to maintain 15% lower waste while optimizing food spend.
Telemetry-driven prep synchronized kitchen output with guest appetite, eliminating end-of-service surplus.
Optimized protein prep and autonomous yield management directly improved Gross Profit margins.
Edge-native processing ensured that 'Fire' alerts reached kitchen displays in under 400ms.
Client Testimonial
Coretus didn't just install cameras—they engineered a waste-reduction mesh that reconciling our culinary art with engineering reality. We suppressed waste by 15% immediately, and our margins have never been more traceable.
Director of Culinary Operations