Smallest AI Raises $13 Mn to Build Real-Time Voice AI

Voice Startup Smallest AI has raised $13 million in a Series A funding round led by Seligman Ventures, with participation from Sierra Ventures and 3one4 Capital, taking its total funding to more than $21 million.

The round also saw participation from Better Capital, Upsparks Capital, Schema Ventures, Tiny VC, DeVC, Mission Street Capital and a group of angel investors.

The fresh capital will be used to develop the company’s real-time speech-to-speech AI platform, which it plans to deploy across industries including financial services, healthcare and contact centres.

“Voice AI is creating a real impact on life and work. We believe the next generation of enterprise voice will be powered by Smallest AI,” said Ashish Kakran, Managing Partner at Seligman Ventures.

Founded in 2023 by Sudarshan Kamath and Akshat Mandloi, Smallest AI builds voice AI models and infrastructure for enterprises.

Its products include Lightning for text-to-speech, Pulse for speech-to-text, Electron, a small language model, and Hydra, a speech-to-speech model. The company is headquartered in San Francisco.

Smallest AI is focused on addressing latency and conversation quality in voice AI systems by building an architecture that processes speech as it arrives, instead of relying on conventional cascaded pipelines.

“The industry has focused on making models larger when the real challenge is architectural,” said Kamath, CEO of Smallest AI.

“Humans don't wait for someone to finish speaking before they begin thinking. We listen, think, and respond simultaneously. Voice AI needs to work the same way. That’s why we built Smallest AI around a real-time architecture that processes speech as it arrives, enabling faster, more natural conversations without sacrificing intelligence.”

Kamath added that the company is “rethinking the stack instead of simply scaling models” to reduce latency in voice interactions.

According to the company, its next phase of development is centred on real-time speech-to-speech systems that can support longer and more complex conversations for enterprise use cases.