Moorcheh.ai delivers next-gen serverless vector search based on Information-Theoretic principles, empowering developers to build radically efficient and hyper-accurate AI chatbots and RAG systems.
Moorcheh.ai provides a serverless API for vector search and Retrieval-Augmented Generation (RAG) that allows developers to build exceptionally accurate and computationally efficient AI chatbots and assistants.
@rachitmagon Great question. Short answer: we treat RAG data as evolving by design. Your knowledge base can add fields, delete items, or re-chunk without breaking queries or causing downtime. We built the only knowledge base that does not need re-indexing, it is always on and ready for search.
We put Moorcheh.ai by Edge AI Innovations to the test against top tools like ElasticSearch, Pinecone, pgvector by Postgres Professional, Redis, MongoDB, Milvus by Zilliz, Qdrant, Weaviate and ChromaDB. The results were clear: by measuring true semantic meaning, Moorcheh.ai by Edge AI Innovations consistently outperformed the competition in both relevance and completeness.
But don't just take our word for it. Our entire benchmarking process—including the third-party documents and questions—is open-source on GitHub. You can replicate our findings and inspect the code yourself. We believe performance claims should be backed by open, reproducible evidence. Watch our deep dive to see the data, and find the link to the GitHub repo in the comments.
Smoopit
Simplifying RAG implementation is much needed. How does your API handle schema evolution as knowledge bases grow and change? @majid_fekri
@rachitmagon Great question. Short answer: we treat RAG data as evolving by design. Your knowledge base can add fields, delete items, or re-chunk without breaking queries or causing downtime. We built the only knowledge base that does not need re-indexing, it is always on and ready for search.