Moss

Moss: Real-Time Semantic Search for AI Agents

Moss is a real-time semantic search engine built for conversational AI, voice agents, and copilots. Developed by InferEdge Inc., it eliminates latency bottlenecks by providing sub-10ms end-to-end retrieval without the need for external vector databases. The system is designed for 100% local execution, allowing it to run directly in the browser, at the edge, on-device, or in the cloud.

Key Features

• Ultra-Low Latency: Delivers end-to-end retrieval in under 10 milliseconds, performing up to 100x faster than traditional vector databases.
• Local Execution: Operates entirely locally with offline indexing and querying, removing network hops and infrastructure overhead.
• Flexible Deployment: Runs directly in browsers, edge environments, devices, or the cloud.
• Broad Integrations: Works with modern AI stacks including Voice AI (LiveKit, Pipecat, VAPI, ElevenLabs), LLM frameworks (LangChain, DSPy), and Frontend AI (Vercel AI SDK, Next.js).
• Developer Friendly: Supports Python and TypeScript, allowing developers to add retrieval to their AI stack in just a few lines of code.

Use Cases

• Voice AI & Copilots: Provides real-time context retrieval for conversational agents, ensuring instant responses without lag or network overhead.
• Docs & Knowledge Search: Powers internal and customer-facing document retrieval systems.
• On-Device & Edge Apps: Enables local, offline-first search capabilities for applications running directly on devices.

Getting Started

Website: https://www.moss.dev/

Moss provides developers with a production-ready, high-speed retrieval solution that replaces traditional vector databases, ensuring real-time performance for latency-sensitive AI applications.