Decentralized learning (DL) leverages edge devices for collaborative model training while enhancing data privacy, as training data never leaves the device. In this paper, we present "BeyondFederated: Truly Decentralised Edge AI," a novel approach to overcoming the limitations of traditional Federated Learning (FL) by eliminating reliance on centralized servers. Traditional FL, despite its decentralization benefits, still poses significant privacy and security risks due to its central control points. Our system leverages edge AI with TensorFlow Lite and a peer-to-peer gossip network to ensure fully decentralized learning and data processing. We developed a proof-of-concept to demonstrate a decentralized alternative to Spotify, incorporating Web3 YouTube playback, and enabling efficient and accurate Scalable Nearest Neighbor (ScaNN) searches using vector embeddings incorporated into an actual TensorFlow model running on edge devices. Through various experiments, we evaluated the system's performance in real-time search capabilities, embedding model comparisons, and new data insertion handling. The results confirm that BeyondFederated maintains high efficiency, scalability, and privacy. This study underscores the potential for truly decentralized machine learning and sets the stage for more robust and user-centric decentralized AI applications.