KITT AI Freight Optimizer | Production-ready AI Agents Hackathon @ AI By the Bay

KITT AI Freight Optimizer Created by: xenn0010 & Yeabsira Teshome Freight loading inefficiency costs the logistics industry over. KITT tackles this massive problem with a physics-based AI world model inspired by cutting-edge research in autonomous agents. What It Does KITT predicts the optimal way to load trucks and containers — accounting for weight distribution, item geometry, weather, traffic, and route constraints. KITT stood out for its ambition — a rare application of world models in an industry ready for AI-driven transformation. How It Works - Leverages Redpanda for low-latency event streaming and agent coordination. - Predicts optimal positioning of every item inside a truck or container - Uses weather and traffic forecasts to adjust configurations dynamically - Aims to autonomously power the entire freight-loading workflow, end-to-end - Tech Stack - Backend: FastAPI, WebSockets, Python 3.10+ - Streaming: Redpanda - Databases: SQLite, Neo4j - AI/ML: DeepPack3D, Claude 3.5 Haiku, scikit-learn - Voice: Pipecat AI - MCP: FastMCP for AI tool integration - APIs: OpenWeatherMap, TomTom Traffic, OpenRouteService. More: https://ai.bythebay.io/hackathon/kitt-ai-freight-optimizer