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    Moss

    Moss

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    Platform

    Real-time semantic search platform for conversational AI and voice agents.

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    About Moss

    Moss: Real-Time Semantic Search for AI Agents

    Moss is a real-time semantic search infrastructure designed specifically for conversational AI, voice agents, and copilots. The platform provides ultra-fast, sub-10ms context retrieval without the need to manage complex infrastructure, enabling AI agents to recall, reason, and respond without noticeable latency across browser, edge, device, and cloud environments.

    Key Features

    • Ultra-Low Latency: Delivers sub-10ms embedding inference and retrieval, operating up to 100x faster than traditional vector databases.
    • Zero Infrastructure Management: Automatically indexes, syncs, and distributes a compact index wherever the agent runs.
    • Offline Capabilities: Supports 100% offline indexing and querying for on-device and edge applications.
    • Broad Integration Ecosystem: Works seamlessly with LangChain, DSPy, Vercel AI SDK, LiveKit, VAPI, ElevenLabs, Next.js, and more.
    • Multi-Language Support: Provides SDKs for both Python and TypeScript, allowing developers to start querying in just a few lines of code.
    • Local Context Retrieval: Agents retrieve context locally without network hops or lag, ensuring real-time conversational flow.

    Use Cases

    • Voice agents and AI copilots requiring sub-10ms context retrieval for real-time conversations.
    • Documentation and knowledge search implementations utilizing semantic search on custom data.
    • On-device and edge applications needing offline-first, lightweight retrieval capabilities.

    Getting Started

    • Website: https://www.moss.dev/
    • Start Building: Create a free account to set up in 5 minutes and deploy to production.
    • Book a Demo: Schedule a demonstration or talk directly to the founders to explore enterprise solutions.

    Moss empowers development teams to build highly responsive conversational AI by eliminating retrieval lag. With its lightweight architecture and offline-first capabilities, it transforms semantic search into a seamless, real-time process for modern AI applications.