Miruvor: Neuromorphic AI Memory

Miruvor AI is replacing Vector embeddings and Similarity search with a Brain Inspired Neuromorphic way to have AI Memory that is extremely low latency, energy efficient and allows to learn from Memories post training. By storing Memories into a Live Neural network and retrieval by Pattern recognition and reactivation, we can get rid of the latency and energy cost of similarity search. This is how the brain parallelizes associative memory allowing it to think and remember in milliseconds. We’ve innovated an architecture and algorithms to use local plasticity rules to learn from new memories and have temporal context, as opposed to using a database query for AI Memory. This is what will translate onto Edge AI and Neuromorphic Chips powering Physical AI as well. I’d be happy to present my Research Paper , architecture diagrams, technical pipelines on the same, show how these memories are ingested and associated. I’d be happy to talk all about the implementation, relavence to Drones, Robotics and how this can help AI Agents and more. We can show our MVP, how the API works, how this can be integrated within Agentic Workflows and used by Agentic AI Startups as well! Self learning AI can start here. We’ve benchmarked this on the locomo Benchmark and achieved 88% accuracy and 50ms average retrieval speed.