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American Focus > Blog > Tech and Science > Phonely’s new AI agents hit 99% accuracy—and customers can’t tell they’re not human
Tech and Science

Phonely’s new AI agents hit 99% accuracy—and customers can’t tell they’re not human

Last updated: June 12, 2025 10:55 pm
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Phonely’s new AI agents hit 99% accuracy—and customers can’t tell they’re not human
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A Breakthrough in Conversational Artificial Intelligence: How Three Companies are Revolutionizing AI Phone Support

A groundbreaking collaboration between AI phone support company Phonely, inference optimization platform Maitai, and chip maker Groq has led to a major breakthrough in addressing a key challenge in conversational artificial intelligence. The partnership has resulted in a remarkable 70% reduction in response times and a significant improvement in accuracy from 81.5% to 99.2% across four model iterations, surpassing industry benchmarks.

One of the persistent issues in AI phone calls has been the noticeable delays that give away the fact that a caller is interacting with a machine. This “uncanny valley” effect has long been a hurdle for businesses looking to implement AI in their customer service operations. However, with this new technology, Phonely has been able to overcome this challenge and provide a more seamless and human-like conversational experience.

The Four-Second Problem: Why AI Phone Calls Sound Robotic

One of the main reasons why AI phone calls have often sounded robotic is the delay in responses, which can be particularly noticeable during live conversations. Traditional large language models have struggled to respond quickly enough to maintain a natural flow of conversation, leading to awkward pauses that reveal the artificial nature of the interaction. Phonely’s CEO, Will Bodewes, highlighted the importance of reducing latency to create a more human-like experience.

How Three Startups Solved AI’s Biggest Conversational Challenge

The breakthrough came from Groq’s development of zero-latency LoRA hotswapping, which allows for instant switching between specialized AI models without any performance penalty. This, combined with Maitai’s optimization platform, has enabled Phonely to continuously improve its models and deliver faster, more accurate responses to callers. The system collects performance data, identifies weak points, and fine-tunes the models to address specific weaknesses.

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From 81% to 99% Accuracy: The Numbers Behind AI’s Human-Like Breakthrough

The results speak for themselves, with significant improvements in response times and accuracy levels. The time to first token has seen a drastic reduction, and overall completion times have also improved significantly. Phonely has seen a 32% increase in qualified leads for one of its major customers, showcasing the real-world impact of these advancements.

350 Human Agents Replaced in One Month: Call Centers Embrace AI

As call centers look to reduce costs and improve efficiency, the adoption of AI agents has become increasingly attractive. Phonely’s technology has shown particular strength in appointment scheduling and lead qualification, leading to significant improvements in customer interactions. One call center has even replaced 350 human agents with Phonely in just one month, highlighting the transformative potential of this technology.

The Hardware Edge: How Groq’s Chips Enable Sub-Second AI

Groq’s Language Processing Units (LPUs) provide the hardware foundation that makes sub-second AI possible. These specialized AI inference chips optimize for language processing, allowing for efficient management of multiple AI models without added latency. The cloud-based infrastructure also addresses scalability concerns, making it a cost-effective solution for enterprises.

Same-Day AI Deployment: Accelerating Implementation for Enterprises

One of the key advantages of this partnership is the rapid deployment capability, allowing for same-day transitions for companies already using general-purpose models. Maitai’s approach enables seamless integration and continuous performance improvements, addressing a common concern about lengthy implementation timelines that delay ROI.

The Future of Enterprise AI: Specialized Models Lead the Way

This collaboration marks a shift towards specialized, task-specific AI models that excel in specific use cases. The ability to deploy faster, more accurate models tailored to specific tasks removes traditional cost and complexity barriers, paving the way for more sophisticated AI applications in the future.

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Overall, the success of this partnership demonstrates the potential for innovative solutions in the enterprise AI sector. By combining specialized capabilities and infrastructure, companies can deliver AI experiences that exceed expectations and set new standards for automated customer interactions.

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