While AI agents are increasingly capable of solving customer support problems, most people can still tell immediately when they’re talking to a machine instead of a human.
Smallest.ai, a startup founded in late 2024, is betting the next leap in voice agents will not come from making large language models faster, but from using smaller, specialized models built for human conversation. Simply put, the company wants to make speaking to an AI agent indistinguishable from talking to a human.
To do so, it’s developing a small voice model designed to mimic how humans process information by listening, thinking, and speaking simultaneously.
“While I’m speaking to you, you’re already thinking, and you might interrupt me if I talk for too long,” Sudarshan Kamath (pictured left), founder and CEO of Smallest.ai, told TechCrunch, adding that this is exactly how the startup’s model is designed to work.
To fuel this mission, Smallest.ai has raised $13 million in a Series A round, led by Seligman Ventures with participation from Sierra Ventures and 3one4 Capital. The fresh capital brings the startup’s total funding to over $21 million.
“The way an LLM works is you give it an entire prompt, and then it starts thinking,” Kamath said. While that latency is acceptable in a text chat, in a voice conversation, even a short pause feels unnatural. “If you think about how we are talking, I’m not giving you like a large clipping of my audio, and then you start thinking.”
The startup’s model serves as a real-time intelligence layer that enables natural customer conversations on specific topics, with virtually zero response lag. But if the model encounters a subject outside its limited knowledge base, Smallest.ai hands off the query to a large foundational model, briefly placing the customer on hold to “research” the issue—just as a real human would do.
Kamath believes that all AI agents will soon rely on two models: a small voice model for real-time interaction, and an “offline” LLM that is called upon as needed to solve complex problems.
Unlike large foundational models, Smallest.ai focuses strictly on voice-specific nuances, such as handling diverse accents, supporting dozens of languages, and operating in noisy environments.
The startup’s existing customers include companies in the voice space, including RingCentral and Truecaller. Kamath said that any customer support company, including newer ones like Sierra and Decagon, is a potential customer for the startup.
When asked why a well-funded AI customer support company wouldn’t build its own voice model, Kamath said that for customer support startups, becoming “extremely good at doing voice is a distraction from their core business.”
Smallest.ai competes with voice AI leader ElevenLabs, as well as Cartesia and regional players like Sarvam that focus on local languages.
While some competitors apply voice AI to use cases, like audio dubbing and podcasting, Smallest.ai focuses strictly on real-time conversational voice agents for its enterprise customers.
“We want our models to break the Turing test,” Kamath said. “You should speak to our model and not know it’s AI or human. That’s the sole focus of the company.”
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Marina Temkin is a venture capital and startups reporter at TechCrunch. Prior to joining TechCrunch, she wrote about VC for PitchBook and Venture Capital Journal. Earlier in her career, Marina was a financial analyst and earned a CFA charterholder designation.
You can contact or verify outreach from Marina by emailing marina.temkin@techcrunch.com or via encrypted message at +1 347-683-3909 on Signal.
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