<10ms
Embedding Inference + Retrieval
150K+
Package Installs
100%
Offline Indexing + Querying
Trusted by 1000+ teams
How It Works
Three steps to production
01
Connect Once
Push your data source (docs, knowledge bases, or live data) via our SDK or portal.
02
Moss Handles the Rest
We index, sync, and distribute a compact index wherever your agent runs: browser, edge, device, or cloud.
03
Search Every Critical Path
Your agent retrieves context locally, in under 10ms. No hops. No lag. No infra to manage.
Integration
Get Started in Minutes
Install Moss and start querying in just a few lines of code
from inferedge_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
100x faster than the alternatives
Benchmarked on 100k documents. Latency includes embedding inference and end-to-end cloud roundtrip. View benchmark script
Integrations
Works with your stack
TypeScript, Python, voice, agents. Install and go.
Use Cases
What teams build with Moss
If you're building voice AI, copilots, or multimodal apps where retrieval is on your critical path, Moss is built for you.
Voice Agents and Copilots
Sub-10ms context retrieval for real-time conversation. Your agent recalls, reasons, and responds without the pause.
FAQ
Frequently asked questions
Everything you need to know about Moss and real-time semantic search for AI agents.