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Smallest.ai Secures $13 Million to Develop Human-Like Voice AI

Smallest.ai, a startup founded in late 2024, has raised $13 million in Series A funding to develop ultra-fast voice AI designed to sound genuinely human.

  • Smallest.ai has raised $13 million in a Series A funding round, bringing its total funding to over $21 million.
  • The company is developing small, specialised voice models to make AI phone calls indistinguishable from human conversations.
  • Smallest.ai's model aims to mimic human information processing by listening, thinking, and speaking simultaneously, with virtually zero response lag.

Smallest.ai, a startup established in late 2024, has secured $13 million in a Series A funding round. This latest investment, led by Seligman Ventures with participation from Sierra Ventures and 3one4 Capital, increases the company's total funding to over $21 million.

The startup is developing small, specialised voice models with the goal of making AI phone calls indistinguishable from human conversations. Smallest.ai's founder and CEO, Sudarshan Kamath, stated that the company's model is designed to mimic how humans process information by listening, thinking, and speaking simultaneously, aiming for virtually zero response lag.

Unlike large language models that process an entire prompt before responding, Smallest.ai's approach focuses on real-time interaction. The model acts as an intelligence layer for natural customer conversations on specific topics. If a query falls outside its limited knowledge base, it can hand off to a large foundational model, briefly placing the customer on hold.

Smallest.ai focuses on voice-specific nuances, including handling diverse accents, supporting multiple languages, and operating in noisy environments. The company's existing customers include RingCentral and Truecaller, and it aims for its models to "break the Turing test," meaning users should not be able to distinguish between an AI and a human.

Why this matters: The development of more human-like voice AI could significantly change how individuals interact with automated customer support and other voice agents, potentially making these interactions feel more natural and efficient.

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