ZenMux is an enterprise LLM platform designed to unify access to multiple AI models through a single API. It emphasizes intelligent routing, allowing requests to be sent to the most suitable model based on the task, which can improve performance and efficiency. A key positioning point is its AI model insurance, aimed at reducing hallucination risk in production workflows. The platform is built for teams that want a simpler way to integrate, manage, and scale LLM usage without dealing with fragmented provider connections. ZenMux is focused on reliability, control, and safer enterprise deployment.

RAG for Cybersecurity combines the power of large language models with vector-based retrieval to transform how security teams access and analyze cybersecurity information. Users begin by ingesting documents such as MITRE ATT&CK matrices, CVE entries, and security advisories. The framework then generates embeddings for each document and stores them in a vector database. When a user submits a query, RAG retrieves the most relevant document chunks, passes them to the LLM, and returns precise, context-rich responses. This approach ensures answers are grounded in authoritative sources, reducing hallucinations while improving accuracy. With customizable data pipelines and support for multiple embeddings and LLM providers, teams can tailor the system to their unique threat intelligence needs.