Moss

Moss is developing a real-time semantic search runtime specifically designed for conversational and multimodal AI applications. Their system empowers voice agents, copilots, and chat interfaces to efficiently retrieve, reason, and respond to queries in under 10 milliseconds. This rapid response time is crucial for creating AI interactions that feel genuinely natural and intuitive.

The challenge with many conversational or voice AI products is the noticeable lag during interactions. This delay, where the agent pauses, disrupts the perceived intelligence of the system. In most cases, the primary bottleneck is the retrieval process. Each query must traverse networks and databases, introducing both delay and increased costs. Moss addresses this issue by ensuring retrieval occurs close to the agent's operational environment, effectively eliminating this performance gap.

Moss operates natively across various platforms, including browsers, mobile devices, and servers, utilizing an optimized vector index built with Rust and WebAssembly. This allows development teams to create AI products that are not only instantaneous but also highly contextual and adaptive. These enhanced experiences lead to significant business benefits for Moss's clients, including improved user retention, increased conversion rates, and the potential to develop entirely new product categories that are enabled by real-time understanding. Moss is currently being utilized in production pilots across voice AI and developer platforms, achieving retrieval times of under 10 milliseconds and realizing token savings of 70–90% compared to conventional pipelines.