Backed byY Combinator

REAL-TIME SEMANTIC SEARCH FOR

Connect your data once. Moss indexes, packages, and distributes it so semantic search runs where intelligence happens:
in-browser, on the edge, or in the cloud.

moss

Plug-and-play Semantic Search across every surface

Get real-time retrieval inside apps, browsers, and enterprise agents — with centralized management, analytics, and scale built in.

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WHY MOSS ?

Moss is a high-performance runtime for real-time semantic search. It delivers sub-10 ms retrieval, instant index updates, and zero infrastructure overhead. It runs wherever your intelligence lives - in-browser, on-device or in the cloud - so search feels native and effortless.

Model-Agnostic

MODEL-AGNOSTIC

Works with any AI model - no vendor lock-in.

Zero-Hop Latency

ZERO-HOP LATENCY

Answers from device memory in <10 ms - no internet delay.

Zero Infra Overhead

ZERO INFRA OVERHEAD

Fully managed hybrid cloud and on-device retrieval.

Offline-First, Cloud-Smart

OFFLINE-FIRST, CLOUD-SMART

Runs offline, cloud powered sync and analytics.

WHO IS IT FOR?

Moss is for developers building conversational, voice, and multimodal AI - experiences where every millisecond shapes how human the interaction feels.

COPILOT & AI AGENT
COPILOT & AI AGENT

For real-time, offline-capable assistance.

DOCS &  KNOWLEDGE
DOCS & KNOWLEDGE

Superfast search without sending data to others.

MOBILE & DESKTOP DEVS
MOBILE & DESKTOP DEVS

Tiny engine (<20kB) that fits anywhere.

DEV TOOL MAKERS
DEV TOOL MAKERS

Keeps code local, great for security audits.

INFRA & PLATFORM LEADS
INFRA & PLATFORM LEADS

Combine speed with optional analytics and rollouts.

COMMON USE CASES

Where teams are putting Moss to work today...

Copilot Memory

Copilot Memory

Recall user context instantly, even offline.

Docs Search

Docs Search

Fast, private search inside help centers.

Desktop Productivity

Desktop Productivity

Smart search in note apps or IDEs without sending data online.

AI-Native Apps

AI-Native Apps

Sub-10ms search on phones and AI-PCs — no lag even with bad network.

HOW WE DELIVER?

You bring your data. Moss powers the retrieval layer - indexing, packaging, and distributing it automatically, so semantic search runs close to where intelligence happens. It enables your application to:

How we deliver - Infrastructure diagram

Run instantly, anywhere

Moss brings real-time semantic search to any environment - inside browsers, apps, or your own infrastructure - with sub-10 ms retrieval and no setup overhead.

Stay personal and private

Each user’s data can be embedded, searched, and updated locally, so experiences feel faster and more personal without sending data to the cloud.

Scale effortlessly

A simple cloud dashboard manages analytics, policies, and updates - giving teams visibility and control without maintaining infrastructure.

Get smarter over time

Moss continuously improves search quality and syncs enhancements automatically. Built-in A/B testing for embeddings makes it easy to compare and tune indexes for the best retrieval results.

Mission background

OUR MISSION

We’re building the foundation for the next generation of AI-native software - systems that understand context, react in real time, and run wherever intelligence happens.
Our mission is to make intelligence ambient - empowering every interaction, in every environment, to feel instant, personal, and human.

PROVEN REAL-TIME PERFORMANCE

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ACTIVE DEPLOYMENTS

Already powering production AI agents and multimodal applications.

<20 kB

Lightweight Runtime

Embeddable inside any browser, app, or edge environment - easy to integrate.

<10 ms

Response

Sub-10 ms median retrieval - instant, human-like search experiences.

100%

OFFLINE CAPABILITY

Fully functional on-device, in hybrid mode, or in the cloud. No dependency on GPUs or network latency.

SUPERCHARGE YOUR APPS WITH MOSS

Users bounce when answers lag or miss the point! Let’s fix that...
We’ll plug in one of your data sources, index it, and show instant, on-topic answers in your conversational AI, voice and multimodal flows. We’ll also run a quick A/B on embedding indexes so you know what works best for your corpus.

Ready to see it on your data?