Smallest AI

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Smallest AI was founded by Sudarshan Kamath and Akshat Mandloi and has raised eight million dollars in seed funding led by Sierra Ventures, months after a pre seed led by 3one4 Capital, with the batch Crunchbase data listing San Francisco as headquarters and a non equity assistance event in October 2025. The company's thesis is precision over scale: many small, efficient models that each know exactly what matters, co optimized to run together rather than assembled from different vendors. In its first year it cut text to speech latency from roughly two seconds to one hundred milliseconds, dropped TTS cost from twenty cents to a penny per minute at scale, and moved from prototype to powering millions of enterprise calls each month. The stack has three layers. Lightning is the text to speech line, with V3 hitting about one hundred millisecond latency across fifteen plus languages including Indic and European coverage, instant voice cloning from ten seconds of audio, and streaming over HTTP, SSE, or WebSocket. Pulse is streaming speech to text with a sixty four millisecond time to first transcript. Atoms is the agent platform on top: no code Single Prompt and Conversational Flow agents with an Agentic Graph Builder, knowledge bases, webhooks, API calls from within conversations, phone numbers, outbound campaigns and audiences, a web voice and chat widget, post call metrics, conversation logs, versioning, and testing, all documented alongside Node.js and Python SDKs, a WebSocket SDK, mobile integrations, and an MCP section in the developer docs. The full agent pipeline stays under eight hundred milliseconds per turn, and the whole stack is available on AWS Marketplace under consolidated billing. Smallest AI fits teams building production voice agents for support, sales, and operations who want the model layer and agent layer from one vendor with published usage pricing, and developers who need fast multilingual TTS and STT as raw APIs. It competes with Vapi, Retell, ElevenLabs, Cartesia, and Deepgram across its layers.