Moss: Sub-10 ms semantic search runtime

Moss launches a semantic search engine with sub-10 ms retrieval latency, eliminating the need for vector databases and enabling real-time AI applications in browser, edge, device, or cloud environments. The tool is used by teams building voice AI, copilots, and real-time systems where milliseconds impact user experience.

Preparing your content Preparing your content Built for Production AI Systems Fix it with <10ms search. No vector databases. No latency bottlenecks. Runs directly in browser, edge, device, or cloud. Used by teams running voice AI, copilots, and real time systems where milliseconds directly impact user experience. <10ms End to end retrieval latency Up to 100x faster than vector databases 250K+ installs Used by developers building production AI systems Across voice, copilots, and real time applications 100% local execution Offline indexing and querying No external vector database required Used in production by teams building real time AI systems Rethinking retrieval No external retrieval layer. No network hops. Eliminate latency at the source. Browser. Edge. Device. Cloud. Deploy where performance matters most. Enable real time conversational experiences. No lag. No infrastructure overhead. Developer Experience Add <10 ms retrieval to your AI stack in a few lines of code Works with your existing LLM stack including LangChain and Vercel AI SDK. python from moss import MossClient client = MossClient PROJECT ID, PROJECT KEY docs = {"text": "How do I track my order?"} await client.add docs "my-index", docs Benchmarks Benchmark run on 100K documents. Includes embedding inference and end to end retrieval latency. View benchmark script https://github.com/usemoss/moss/tree/main/benchmarks Integrations Drop Moss into your existing stack across voice, LLM frameworks, and frontend AI Use Cases For systems where retrieval is on the critical path and latency directly impacts user experience <10 ms context retrieval for real time conversation. Your agent responds instantly without latency or network overhead. FAQ Answers to common questions about latency, architecture, and production deployment