We built WeRHungry, a real-time operating layer for busy commercial kitchens. An OpenCV/VLM pipeline converts a kitchen scan into a colored GLB digital twin identifying stations, equipment, workers, inventory, and safe aisles. During service, workers double-press a wristband to see their next task, required tools and ingredients, and a safe route. One press accepts; another double press passes and requests a replacement.
Firebase powers authentication, live state, task events, GLB storage, and hosting. Firestore Vector Search retrieves similar past incidents, allowing agents to combine historical outcomes with current queues, staffing, equipment, and inventory to predict completion times and explain delays.
Specialized Perception, Forecasting, Memory, Reasoning, Scheduling, Critic, and Coordinator agents collaborate through a Guild-compatible control plane. Pioneer ranks causes and actions while learning from manager corrections, and Replay tests critical application workflows. The operating method is universal, but each restaurant evolves its own demand, preparation, capacity, and staffing policies.
