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Backed by<br>Built for Production AI Systems<br>Your Voice AI breaks when retrieval is slow.<br>Fix it with Run a Latency Test →Talk to an Engineer<br>Used by teams running voice AI, copilots, and real time systems<br>where milliseconds directly impact user experience.
Live latency demo
Moss Founding AgentLive Test Environment
Try asking<br>"Run a retrieval query benchmark"<br>Run Test
Built for real time AI systems at scale
End to end retrieval latency<br>Up to 100x faster than vector databases
250K+ installs<br>Used by developers building production AI systems<br>Across voice, copilots, and real time applications
100% local execution<br>Offline indexing and querying<br>No external vector database required
Used in production by teams building real time AI systems
Rethinking retrieval<br>Why Moss is fundamentally different
Replace your vector database<br>No external retrieval layer. No network hops. Eliminate latency at the source.
Run search where your AI runs<br>Browser. Edge. Device. Cloud.<br>Deploy where performance matters most.
Retrieve in Enable real time conversational experiences. No lag. No infrastructure overhead.
Developer Experience<br>Ship real-time retrieval in minutes<br>Add Works with your existing LLM stack including LangChain and Vercel AI SDK.
PythonTypeScript
Copy
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<br>Your bottleneck is not your model. It is retrieval.<br>Query latency measured in milliseconds. Lower is better.
P50
P99
Moss<br>ChromaDB<br>Qdrant<br>Pinecone
3.1
5.4
351.8
538.5
597.6
771.4
432.6
934.2
02505007501000
Benchmark run on 100K documents. Includes embedding inference and end to end retrieval latency. View benchmark script
Integrations<br>Built for modern AI stacks<br>Drop Moss into your existing stack across voice, LLM frameworks, and frontend AI<br>Voice AI<br>LiveKit<br>Pipecat<br>ElevenLabs
LLM frameworks<br>LangChain<br>DSPy
Frontend AI<br>Vercel AI SDK<br>Next.js
Use Cases<br>Built for real time AI applications<br>For systems where retrieval is on the critical path and latency directly impacts user experience
Voice AI & CopilotsReal time context retrievalDocs & Knowledge SearchInternal and customer facing retrievalOn-Device & Edge AppsLocal, offline first search<br>Voice AI and Copilots<br>retrieval latency<br>// Agent receives user query<br>user: "What was our Q3 revenue?"<br>moss.search() → 3 results in 6ms<br>agent: "Q3 revenue was $4.2M, up 18% from Q2..."
View Voice AI architecture→
FAQ<br>Questions from teams building real time AI systems<br>Answers to common questions about latency, architecture, and production deployment<br>Why is Moss faster than vector databases?
Do I still need Pinecone or other vector databases?
Can Moss run fully on device or at the edge?
How does Moss reduce latency in production systems?
How does Moss handle data privacy and security?
What does Moss replace in my current stack?
How does Moss scale for high volume voice or AI workloads?
How do I get started and test latency?
Eliminate latency from your AI stack<br>Run a Latency Test →Talk to an Engineer<br>No credit card required<br>Deploy in minutes<br>Production ready