Moorcheh

Moorcheh provides a self‑hosted stack for ingesting documents, performing semantic search, and enabling retrieval‑augmented generation within a virtual private cloud. It bundles a full pipeline—ingestion, indexing, retrieval, and agentic intelligence—so that enterprises can run AI‑driven search without relying on external APIs or managing clusters. The system is positioned as a sovereign solution that can be deployed in under ten minutes, aiming to reduce infrastructure costs and latency.

The platform targets engineering teams and developers who need high‑throughput, low‑latency search over large corpora while keeping data under their own control. It integrates with popular tooling such as LangChain, LlamaIndex, Hugging Face models, and offers access via Python, npm, REST API, and GitHub. Reported performance includes thousands of queries per second and sub‑10 ms average retrieval latency.

What distinguishes Moorcheh is its emphasis on a turnkey, cost‑effective deployment that eliminates the need for third‑party services and DevOps overhead. It is marketed as an enterprise‑grade, information‑theoretic search engine that can be operated entirely within a customer’s VPC, with early adopters citing scalability, security, and reduced cloud spend.