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How AI is Disrupting Customer Service? Hear From a Female Innovator!

Do you ever call customer care and end up waiting for hours to get assistance or find it challenging to follow up on emails? These are problems that all of us face! But meet Rezo.AI—a new-age startup addressing these critical issues!

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Imagine calling a customer service hotline and being put on hold for what feels like an eternity, only to be met with a scripted response that doesn’t quite address your issue. Or consider the frustration of waiting days for an email reply that merely redirects you to another department. These are common scenarios that many of us face when dealing with traditional customer service methods—experiences that can leave customers feeling neglected and frustrated.

In today’s fast-paced digital era, such inefficiencies are no longer acceptable. Customers demand quick, personalized, and efficient responses to their inquiries. This growing expectation has paved the way for a wave of innovations aimed at transforming customer service experiences. One significant player in this space is Rezo.AI, a startup dedicated to solving these pervasive issues with its cutting-edge conversational AI technology.

What is Rezo.AI?

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Rezo.AI, founded in 2017 by IIT alumni Rashi Gupta and Manish Gupta, seeks to revolutionize the customer service landscape by automating interactions across multiple channels including voice, email, WhatsApp, social media, and chat. Utilizing advanced deep learning and neural networks, Rezo.AI’s platform ensures that customer inquiries are resolved swiftly and accurately, enhancing overall satisfaction and operational efficiency. Wondering Rezo.AI really solves these issues? Well! Let's get into the interview featured above and hear it from the founder, Rashi Gupta herself. 

Today, as we take a closer look at Rezo.AI’s journey. We will explore how this startup is making significant strides in the conversational AI domain, the unique problems it addresses, and its impact on the industry.

To begin with, Gupta shared the core idea behind Rezo.AI. 

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She said, "the core idea behind starting Rezo’s was to enable large enterprises for having a very effective communication with their customer base, so that was the whole context and then not only use voice and whatsapp and unan to do that communication but power that with solid data."

Role of AI and Analytics in Industries

She asserted that data and analytics are very core for Rezo primarily it all depends on which startup is leveraging what but for us that’s the very core premise because we have built the whole engine, the communication platform that we have built primarily built it with powering the data so data to an extent for example if I’m calling you, I get to know what kind of person are you, at what time you pickup the call, what language do you talk, what kind of offers I can pitch you, what would you typically latch on to, so I have the whole persona built for you, so it's not that I’m doing an ad hoc calling in an attempt that you will pickup my call and have an communication 

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Chatbot Automation and Human-Like Response

Humanlike conversation chatbot when we do, we’re doing more than a human-like very effective sort of communication because what we’re trying to do when the communication is on we are able to cut-out a lot of elements from there in the sense, what’s the sentiment is like, what the emotion is like, what he is buying, what he want to buy tentatively bases the language the verbatim that the customer is typing in, Is it a hot lead, Is it a warm lead, so you can cut out one thing or everything, once you look at the data you can call out all of the spectrum out. 

On being asked, why human-like chatbots?, she said, "Because the way a human would have identified the conversation and would have taken certain calls, it's that what we're trying to relate to."

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Further explaining how do Rezo.AI ensures a balance between chatbot automation and AI danger, Gupta said, you need to have a very very controlled model and we do more than the chat, we do voice, so the models have to be very very controlled, in the sense that I’m pitching a borrower or something and I parallel starts to sell pizza, so the model need to be very controlled, if you led the model talk what it wants, the conversations could go and it could be a large penalty to the enterprises, so the models have to be very controlled, the communication has to be very effective what you want to communication is the crux and what you bring

 

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