Kitchen Memory Vault - AI-Powered Restaurant Intelligence System A production-ready RAG pipeline with agentic capabilities that gives restaurant kitchens an intelligent memory layer. Staff can log notes, search semantically, and get LLM-grounded answers about specials, inventory, customer preferences, and operations. Key Innovation: Session-based context caching reduces API calls by 80% and improves response time by 50%, making it practical for real-world high-frequency use. Built with MongoDB Atlas Vector Search for semantic retrieval, Voyage AI for embeddings, and Llama-4 for reasoning. Features include hybrid routing (fast-path + ReAct agent), role-based context seeding, and CLI/API interfaces. Solves: Information loss during shift handoffs, scattered kitchen knowledge, and slow manual search through notes.
More from Agentic Memory & Context Engineering Hackathon
Our tool helps patients with dementia by surfacing real-time context on the person they are talking to.
- ContextScope Eval is an open-source evaluation and observability framework for agent-to-agent context sharing. It measures how effectively autonomous agents…
Ever struggled to find that perfect article you read last week? That tutorial video you can't quite remember the name of? WebBrain transforms your browser…
+1The Airbnb Travel Booking Assistant is a personalized AI concierge that curates stays based on each traveler’s unique style and past experiences. Instead of…
KitchenPal is an AI-powered cooking assistant that leverages multiple independent Retrieval-Augmented Generation (RAG) workflows to provide personalized…
Incident Copilot is a seeded-RAG assistant for on-call engineers. It uses MongoDB Atlas Vector Search, Voyage embeddings with reranking, and Google Gemini to…
+1