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.
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
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