The AI industry has long been dominated by companies pouring billions into ever-larger models, assuming that scale is the key to intelligence. Smallest.ai believes in a different approach—one that makes voice AI accessible and free at a population scale. Founded by Sudarshan Kamath and Akshat Mandloi, the company has developed small but powerful language models that they believe can rival and even outperform major competitors while keeping costs a fraction of industry norms.
A Unique Approach: Small AI, Big Impact
Smallest.ai was born from a fundamental question: If voice AI is so powerful, why aren't billions of people using it daily? The answer, as Kamath and Mandloi saw it, was that existing AI models were too large, expensive, and inefficient to scale effectively. Their solution was to rethink AI development from first principles, focusing on smaller models optimized for real-world use, and making this technology available for free at a population scale across America.
The founders' backgrounds in self-driving vehicle data science gave them a unique perspective. Working across the U.S., Europe, and Japan, they had already spent a great deal of time refining large AI models to function efficiently on constrained hardware in real-time environments. This experience laid the groundwork for their new take on speech AI. They had already proven to themselves that massive neural networks are often overparameterized and that smaller models can achieve better results. Now, their goal is to bring this approach to the masses, with free access to AI voice agents for everyone.
Smaller Scale, Higher Standards in Speech AI
Smallest.ai boasts that in just six months, with a budget of under $100,000, they were able to outperform a major competitor (one valued at $3 billion, no less!) in third-party open-source text-to-speech (TTS) benchmarks. Their proprietary voice cloning pipeline has set a high internal standard for accurate and naturalistic AI-driven voice generation, which they hope will present a challenge that industry competitors will struggle to match. Most importantly, they are making this technology available for free, giving everyone the opportunity to use high-quality voice AI without the cost barrier. They take particular pride in these accomplishments because their models are built from the ground up. They value their independence from external dependencies, which allows them to operate with lower costs and greater flexibility.
Refining Tools for Real-World Impact
Smallest.ai's innovations were never meant to stop at theory and experimentation. Their commitment to making voice AI free has already begun reshaping multiple industries. Their voice agent platform, Atoms, delivers high-quality, low-latency, AI-driven voice services, making automated conversations more efficient and cost-effective. Their TTS API serves thousands of small-to-medium businesses (SMBs) and enterprises globally, and their technology is being leveraged by major edtech firms and media houses for voice cloning, content creation, and multilingual content distribution. Smallest.ai's custom voice agents are also hard at work in business operations, handling tasks such as outbound sales, lead qualification, debt collection, and customer support.
One of Smallest.ai's key achievements has been proving that small AI models can outperform large-scale systems, generating significant investor interest in the process. Even in an industry that thrives on innovation, Smallest.ai's unexpected selling point often catches investors by surprise. However, their success has proven the value of frugal innovation. By providing free access to these voice agents, Smallest.ai is showing the world how capital-efficient AI development can push beyond limits and become a truly competitive advantage.
With a bold goal of reaching one billion daily users, Smallest.ai's mission is to bring free voice AI to everyone, empowering people and businesses alike with this transformative technology. They aim to set a new standard for AI—one that values efficiency, scalability, and real-world impact over sheer size.
