Harsha Nalluru
Founder
Moss
Moss provides an on‑device semantic search runtime that runs in the same process as conversational AI agents, delivering sub‑10 ms vector lookups without remote databases. The Rust‑based engine compiles to WebAssembly and syncs compressed indexes via its SDK, enabling offline‑first, privacy‑by‑design retrieval on browsers, edge, mobile, or cloud containers. Developers can integrate via TypeScript or Python APIs and use built‑in A/B testing to optimize embeddings.
Updated 6 months ago
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Funding rounds are not available yet.
Founders
Sri Raghu Malireddi
Founder
Product
Problem
Conversational AI agents and voice copilots must perform dozens of knowledge lookups per interaction, often incurring 100–500 ms latency per query when relying on remote vector databases. This delay adds noticeable dead time to user conversations and forces developers to manage separate retrieval infrastructure to keep data fresh.
Solution
Moss delivers a real‑time semantic search runtime that executes directly inside the agent’s process, eliminating network hops and reducing lookup latency to under 10 ms. Built in Rust and compiled to WebAssembly, the engine runs on browsers, edge nodes, mobile devices, or cloud containers without any server‑side clusters. Developers push their documents or live data through Moss’s SDK or portal, and the platform automatically indexes, syncs, and distributes a compact vector index to every deployment target. The runtime provides instant, always‑current context retrieval, enabling agents to recall and reason without pause while keeping data on‑device for privacy‑by‑design compliance. Drop‑in TypeScript and Python libraries expose simple query APIs, and built‑in A/B testing lets teams experiment with embeddings and index configurations before shipping.
Target Audience
Moss is aimed at developers and product teams building voice assistants, AI copilots, multimodal applications, and internal knowledge‑base search tools that require ultra‑low‑latency retrieval, as well as enterprises needing on‑device or edge AI capabilities with strong data‑privacy guarantees.
Features
- Rust‑based core compiled to WebAssembly for sub‑10 ms on‑device semantic search across browser, edge, and mobile runtimes
- TypeScript and Python SDKs for rapid indexing, incremental updates, and low‑latency querying
- Automatic index compression, synchronization, and distribution to any runtime location, eliminating external infrastructure
- Offline‑first operation with local storage; data never leaves the device unless explicitly synced, supporting strict privacy requirements
- Built‑in A/B testing framework for comparing embedding models and index parameters on the same corpus
- Zero‑DevOps deployment: no clusters, replicas, or managed vector databases needed
- Secure end‑to‑end encryption of indexed data and queries, compliant with regulated‑industry standards