We reduced RAG retrieval cost 10× with a hippocampus-inspired memory substrate
A new AI memory engine inspired by the hippocampus has been developed, significantly reducing retrieval costs. This system utilizes sparse coding to improve efficiency and accuracy compared to traditional methods. The results demonstrate a notable increase in performance while maintaining lower token costs.
- ▪The hippocampus-inspired memory engine achieves 90.91% accuracy with a token cost of approximately 12.
- ▪It outperforms the MiniLM-filtered model, which has an accuracy of 77.27% and a higher token cost.
- ▪The architecture allows for retrieval without embedding costs at query time, enhancing efficiency.
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BlogAI Memory12 min readWe Built a Memory Engine. The Brain Told Us How.We are two people building an AI memory layer in Dubai. A few months ago we got deep into reading about how the hippocampus actually works: sparse distributed codes, place cells, and the way a small number of neurons fire precisely while the rest stay silent.The kind of reading that starts at 2am and ends with you questioning why every retrieval system in AI works nothing like this.Almost every RAG pipeline in production today follows the same pattern: dense vectors, nearest-neighbor search, pull in as much context as possible, and hope the LLM figures out what is relevant.
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