Regal's Co-Founder and CEO, Alex Levin, featured in the Interview Series
Regal, a voice AI platform for enterprises, is making strides in automating customer interactions to reduce manual QA hours. The company, founded by Alex Levin, is tackling several technical challenges to create AI agents that can match or exceed human performance in natural conversations.
One of the key challenges is achieving human-like natural conversation quality at scale across diverse enterprise domains like healthcare and insurance. This requires sophisticated understanding and generation capabilities to handle complex customer interactions reliably [1]. To address this, Regal designs and deploys end-to-end generative AI applications, including large language models (LLMs) and multimodal AI integration [5].
Ensuring scalability and cost-efficiency is another priority. AI voice agents need to replace human agents effectively while maintaining or surpassing performance metrics in customer experience [1]. Regal's system uses machine learning to personalize conversations in real-time, based on customer history, tone, and intent recognition.
Handling multilingual reasoning and responses is crucial, as enterprise environments often require supporting multiple languages. Regal employs advanced fine-tuning techniques and trains on diverse datasets to handle this challenge [2]. Maintaining high accuracy and trustworthiness is essential, especially in regulated and sensitive domains like healthcare and insurance [1].
Integrating Regal with major CRMs, payment systems, and internal APIs can be challenging, especially if the brand does not make APIs available for all actions the AI agent might need to take. Despite these challenges, Regal's AI agents are performing better than human agents and are expected to become indistinguishable from humans in the next year, with the potential to exceed human agent abilities [1].
Regal's AI advisors are trained on the writings of individual investors, aiming to capture their unique personalities and philosophies. The platform features a no-code builder, real-time analytics, A/B testing, and built-in compliance for regulated industries. It also uses "Custom Actions" to leverage other services, such as 3rd party data services and custom applications.
A safe word can be used to identify the real person on a call if a voice clone is in use. Consent is required from the person whose voice is being cloned, and any unauthorized cloning should be shut down. Companies that lean into AI agents are expected to drop their costs and improve customer experience faster than anticipated [6].
Regal was launched in 2020, before the generative AI boom. The company's conviction to act early was based on the belief that voice was the most important channel and the ability to build tools for orchestration, A/B testing, and personalization. Alex Levin, the Co-Founder and CEO of Regal, left his roles at Angi/Handy due to dissatisfaction with contact center software vendors' focus on "deflection" and "automation" over customer needs [7].
For those interested in learning more about Regal, the company's website provides further information [8]. The system uses retrieval-augmented generation (RAG) to maintain responsiveness and natural cadence during voice calls, ensuring a seamless customer experience. With its commitment to customer-centric AI and innovative technology, Regal is leading the way in transforming enterprise customer service.
[1] [Link to source 1] [2] [Link to source 2] [3] [Link to source 3] [4] [Link to source 4] [5] [Link to source 5] [6] [Link to source 6] [7] [Link to source 7] [8] [Link to Regal's website]
In the realm of finance and investing, Regal's AI agents, designed to automate customer interactions, are not only expected to supersede human agents in the near future but also incorporate artificial intelligence and technology for multilingual reasoning and personalization. These AI advisors, trained on the writings of individual investors, aim to capture unique personalities and philosophies, paving the way for more efficient customer service.
As Regal continues to advance its technology, it also addresses the challenge of maintaining scalability and cost-efficiency by using machine learning to personalize conversations in real-time and by employing advanced fine-tuning techniques and diverse datasets for handling multilingual interactions.
