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Conversational artificial intelligence (AI), often regarded as the crosshair between natural language processing (NLP) and natural language understanding (NLU), is algorithm-based intelligence that helps computers listen to, process, understand and make meaning out of human language. With COVID-19 accelerating the digital transformation drive, organizations have increasingly adopted new ways to match customer expectations around timely query resolution. An important part of the customer experience process, conversational AI, is quickly taking center stage.

A survey by Liveperson shows 91% of customers prefer companies offering them the choice to call or message. An effective way for enterprises to meet this demand is via chatbots. As customers increasingly expect round-the-clock availability from businesses, chatbots have become an efficient way to make this possible. Chatbots are cost-effective, efficient and can routinely handle human requests, allowing the difficult queries to be reserved for human agents.

With the influx of AI-driven chatbots also comes the need for more sophistication, as well as the   need for chatbots to understand questions and produce the right answers to help customers get the best experience. Particularly, this is relevant for the emerging metaverse space, as companies seek to improve their presence and user experiences in a virtual world., a Florida-based company focused on providing enterprises with AI-first virtual assistants, wants to take conversational intelligence into the metaverse. Raj Koneru, CEO at, told VentureBeat in an exclusive interview that conversational AI is the foundation of the metaverse.


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The rise of conversational AI

More companies have embraced the use of conversational AI throughout the last few years. Open-source AI language models have now helped developers and businesses in the conversational AI space build better chatbots, help with expediting customer experience and also improve employee experience.

Conversational AI merges the practicality of AI to produce human-like interaction between the human who asks the questions and the machine that answers. Chatbots powered by conversational AI can recognize human speech and text. The conversation also occurs with an understanding of intent, enabling more accurate answers.

As voice and conversational AI take on an increasing volume of customer interactions, it also becomes more important for bots to tap into historical data, connecting data from voice calls to data from messaging conversations. Conversational AI helps to make this possible by helping machines make sense of the human voice they are listening to, understand biases and process the answers that humans are looking for.

Shortening shipping and implementation without skimping on usability

Businesses need their chatbots built with conversational AI, said Koneru. However, the problem is that building such solutions from the ground up requires a long process that includes writing numerous codes. This can hurt customer experience, as chatbots will continue to be less than effective while building conversational AI. The long wait time is often a result of issues like levels of sophistication, language and geographical support. There is also accounting for the time it takes to train the AI and ensure it is ready for market fit.  

To shorten the wait time, many businesses turn to companies like, which offers a no-code automation platform that caters to the conversational AI needs of companies looking to improve customer experience and interaction with their products. 

Gartner’s Magic Quadrant for Enterprise Conversational AI Platforms 2022 lists as a leader in the conversational AI space, showing the company’s upward trajectory in the industry. Other companies on the list include Amelia, Cognigy, Omilia, IBM and

Koneru said uses a combination of NLU approaches — including fundamental meaning (semantic understanding), machine learning and knowledge graph — to identify user intent with a higher degree of accuracy and execute complex transactions. He said can achieve a high level of accuracy by following specific methods, which include:

  • Fundamental meaning: Analyzes the structure of a user’s utterance to identify words by meaning, position, conjugation, capitalization, plurality and other factors.
  • Machine learning: uses state-of-the-art algorithms and models to predict the intent.
  • Knowledge graph: provides the intelligence required to represent the importance of key domain terms and their relationships in identifying user’s intent.
  • Ranking and resolver engine: determines the winning intent based on the scores provided by the three engines.

Eyes on the metaverse

Gartner predicts that 25% of people will spend at least one hour daily in the metaverse by 2026 — leading businesses to join the race to stake their claim in the metaverse. Companies are preparing customer touchpoints and are proactively mapping out user experiences.

According to Koneru, humans will have conversations with avatars, essentially chatbots, in the metaverse. So, it’s key to create avatars that can understand the dynamics of human conversation, process it and deliver precise results, even with all the human nuances that may interfere. This is where conversational AI comes in.

Utilizing conversational AI to improve virtual experiences in the metaverse shows considerable promise, as Koneru noted, “the metaverse is ripe with a lot of use cases that traditional businesses can benefit from.”

“There have to be some good reasons in business processes that lend themselves to being physically present that prevent them from doing so digitally. The same is the case with the post office and many educational services. If you think of telehealth, going into the metaverse and meeting a virtual representation of your doctor who can check your vitals could potentially be one of the stronger use cases,” he said.

According to Koneru,’s attempt to improve the metaverse world with conversational AI will see it become the first conversation AI company to focus on businesses in the metaverse while still servicing the needs of everyday businesses. However, G2’s review shows has competition in Intercom, Zendesk Support Suite, Drift, Birdeye and others. has raised $106 million in equity over the last eight years and currently serves more than  200 Fortune 2000 companies.

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