Slowly but surely, Google Home, Alexa, Siri-Homepod, and Cortana have made their way into our daily lives, becoming invisible members of our homes. AI chatbots are no longer a thing of fancy \u2014 in fact, they\u2019re on their way to becoming as ubiquitous as our\u00a0 TV, giving us weather and news updates, playing our favourite songs, flipping channels for us, and more.\r\n\r\nHowever, these voice assistants have their failings and are, at times, inconsistent. One time, I asked Alexa to play the last song on the playlist, she burst out into a weird laugh. I was amused and later relieved that she wasn\u2019t behaving as creepily as I\u2019d heard she does sometimes. Thankfully, even the weird laughing has stopped for some time now.\r\n\r\nWhile such failings might be admissible to an extent in B2C products like the ones mentioned above, AI enterprise solutions are a different ball game altogether and have no room for failure. Used mostly in the supply chain, banking, retail, healthcare, and other sectors by enterprises, AI enterprise solutions have to be nearly 100% accurate while being equally efficient to reduce the human support in business functions.\r\n\r\nWith a view to understanding the technology behind enterprise chatbots, their nuances, and the efforts made by companies to meet market expectations and eliminate any scope of failure, Inc42 spoke with Sriram Chakravarthy, co-founder and CTO, Avaamo, an AI startup.\r\n\r\n\r\n\r\n\r\n\r\n \r\n\r\nAvaamo offers deep learning virtual assistant platforms to enterprises, primarily in sectors such as fintech, healthcare, telecom, and retail. We also asked Chakravarthy about the future plans of the California-based company, which has an operations and development team working from its Bengaluru, India, office.\r\nEnterprise AI: Culture And Context Underpin Language\r\nArtificial intelligence, which replicates human intelligence with accuracy, is no easy technology to adopt and execute. Humans speak and type multiple language-specific words. Hence, for an AI chatbot to be truly global and of use to an enterprise catering to various country-markets, it needs to understand multiple languages.\r\n\r\nFor an AI assistant, this simply means loads and loads of data processing.\r\n\r\nChakravarthy says, \u201cThis segment is very data-driven. Data is the oil that runs this whole business. So, we have built domain-specific machine learning tools for each of these industries.\u201d\r\n\r\nWhile there is a significant commonality as some of the machine learning (ML) modules can be adopted across markets \u2014 whether it\u2019s a telco in Singapore or a telco in Australia or India \u2014 the real challenge lies in deciphering the nuances of language-related variables. This also involves the way people talk.\r\n\r\nChakravarthy cites an example, \u201cIn India, we use the word \u2018recharge\u2019 quite often for mobile and data plans. However, in the US, the concept of recharge hardly exists because they mostly have fixed monthly mobile plans.\u201d\r\n\r\nThere are innumerable such linguistic and cultural nuances that define how a company understands and delivers chatbot solutions for different business problems it\u2019s solving. Further, the meanings of some words\/terms vary sector-wise. So, ML modules have to be customised accordingly.\r\n\r\nAvaamo bots are capable of processing multiple languages. Chakravarthy says, \u201cWe have bots that are currently live in 14 different international languages. They can understand, process and revert in these languages. Besides, we have already incorporated seven-eight major Indian languages as well. The bots are also capable of processing Hinglish (a mix of Hindi and English), Banglish (Bangla and English), Tamlish (Tamil and English), etc.\u201d\r\n\r\nPeople sometimes enter Hindi\/other language inputs in the English script, therefore, Chakravarthy adds, \u201cWe have enabled all these types of use cases. For instance, if one types Portuguese in English or a mix of both, some words co-exist in both languages but their meanings vary immensely. Suppose you type \u2018bento\u2019 \u2014 it could be a Portuguese word which means blessed or it could also refer to \u2018Bento\u2019 the beer brand.\u201d\r\n\r\nAn AI bot needs to understand the difference between the meanings of the word. This is where context provides the due intelligence. A lot depends on how you use a particular word and this, again, varies from person to person, explains Chakravarthy.\r\n\r\nThere could be situations where the bot may not be able to verify the exact meaning of the word that a user has entered; in such cases, it will ask the user what he\/she means. For instance, in the abovementioned example, the bot asks whether the user meant \u2018bento\u2019 as bento (blessed) or Bento as beer.\r\nThe Beer Game Intelligentsia\r\nChakravarthy takes us through another use case to explain the AI game. \u201cTake the example of a large beer manufacturer which happens to be our client. The company, which has a large presence in Brazil, has been there for over a hundred years. Recently, they were looking at leveraging their WhatsApp channel for better supply assistance and approached Avaamo for a solution.\u201d\r\n\r\nHe elaborates that despite having applied the Bullwhip effect (forecasting supply chain inefficiencies) to the client\u2019s supply chain, beer shops that were SMS and WhatsApp-friendly needed to be given apt and quick responses. The question could be, \u201cHey, what\u2019s the status of my order?\u201d Or, since the client also supplies coolers along with beer cases, it could be: \u201cHey, the cooler is not working...\u201d\r\n\r\nNow, in addition to collecting personal identities of the user interacting with the chatbots and the Beer Game (beer distribution game was developed by MIT in the 1960s to demonstrate supply chain management principles), which includes batch planning, production, scheduling, ingredient forecasting, and, most importantly, client orders, the enterprise chatbot for this company required to deliver clear and precise responses to users.\r\n\r\nThis supply chain distribution game involved point-of-sale (POS) data collection, electronic data interchange (EDI), and vendor-managed inventories (VMI) to improve communication accuracy and efficacy.\r\n\r\n\u201cWe have automated the entire channel down to the last person. There are a couple of more solutions like this that we have built for a large freight company,\u201d says Chakravarthy.\r\nDigging Deeper Into Data Than Alexa And Google Assistant\r\nExplaining the difference between AI enterprise solutions and consumer tech chatbots, Chakravarthy says that in the case of Alexa and Google Home, most often, you ask a question or give a command and the voice assistant can simply address it. For instance, you say, \u201cHey, Alexa, play a Taylor Swift Song\u201d and Alexa responds by playing the song, which is already available on the Amazon Music list or on other lists.\r\n\r\nHowever, in the case of the enterprise chatbots, it is a multi-turn conversation. For instance, if you say, \u2018Hey, I need insurance\u2019, it can\u2019t just get you the insurance. So, it will say, \u2018Okay, I will help you with that. What\u2019s your name, what\u2019s your age. And, so on.\u2019\r\n\r\nTo enable enterprise chatbot conversations, companies have to engage with users deeply and collect a lot more information and then process this data. He adds that there are a lot of nuances involved in this because user responses are subjective and the response design varies from person to person.\r\n\r\nFor instance, when asked \u2018What\u2019s your name?\u2019 by an enterprise chatbot, there are many ways in which a person may respond:\r\n\r\n \tSome users will simply pronounce their name say \u2018Mr X\u2019\r\n \tSome will say \u2018My name is X\u2019\r\n \tSome will say my name is \u2018X\u2019 and my surname is \u2018Y\u2019, and so on.\r\n\r\nNow, the actual reply \u2014 the name itself\u00a0\u2014 needs to be filtered irrespective of the way it has been answered.\r\n\r\nSo, right from evolving the machine learning solution, analysing the data collected, to responding to a query, these chatbots have to go through a lot of challenges.\r\n\r\n\u201cOur focus remains developing an intelligent conversation interface. We have gone through Phase I and are now working on Phase II of the development, which is about the depth of the conversation, bringing more context to the conversation. For instance, if a user logs in after a week, the chatbot needs to connect with their conversations that happened in the past,\u201d says Chakravarthy.\r\nHey Avaamo, What\u2019s The Plan Now?\r\nAvaamo currently caters to more than 40 country-markets. Among these, the US and India are the top markets for the startup. It has over 50 enterprise customers in India including Honeywell, Wipro, ICICI Prudential Life Insurance, City Union Bank, Axis Bank, Reliance Nippon Life Insurance, SBI Mutual Fund, Aditya Birla Life Insurance etc. It is now slowly expanding into other regions, including Malaysia, Singapore and Australia.\r\n\r\nThe startup recently raised $14.2 Mn in a Series A funding led by Intel Capital, a division of the US-based hardware giant Intel Corporation. Avaamo\u2019s total funding now stands at $23.5 Mn. Chakravarthy lays down the big plans Avaamo has for the Indian market as well as for foreign shores.\r\n\r\nHe says Avaamo is using the funding for three major purposes: sales and marketing, R&D, and increasing engagement with its partners. \u201cWe have a team of 50 engineers here at our Lavelle Road office, Bengaluru. While it is not about team size but quality, we are hiring at least 30-40 more ML\/data scientists who will be working on the product development that be served globally.\u201d\r\n\r\n\u201cWe are also quickly enabling our partners by giving them a chance to building their AI chatbot around our platform. We are going to spend a large part of the amount on building a coherent partner ecosystem,\u201d he adds.\r\n\r\nChakravarthy adds that the enterprise AI solutions market has tremendous potential across the globe and that it\u2019s not localised to a particular country market. \u201cEvery large company is looking at a new technology \u2014 automation \u2014 to call itself a last-mile organisation. It\u2019s talking to its suppliers, employers, consumers. There is a lot of costs associated with service desks. Every company is looking at innovative ways to reduce the cost of operations and services,\u201d he explains.\r\nThe AI Market In India\r\nAccording to an Intel India-commissioned report by US-based IDC, 68.6% of Indian firms might deploy AI solutions by 2020. Another study by Accenture says that AI could add $957 Bn to the Indian economy, increasing the country\u2019s income by 15% in 2035, by changing the nature of work to create better outcomes. These projections present a huge opportunity for AI startups working on enterprise AI solutions.\r\n\r\nBesides Avaamo, a number of startups have mushroomed in the enterprise chatbot space, offering customised and differentiated solutions. There are also Amazon\u2019s Alexa as part of its Amazon Web Services (AWS), SigTuple, Haptik, Niki.ai, Flutura, Uncanny Vision, Innefu Labs, Netradyne, Active.ai, staqu, Formcept, and other players in this space.\r\n\r\nRatan Tata-backed Niki.ai leverages natural language processing (NLP) and ML to enable brands to converse with customers over a chat interface and help the latter shop for products and services.\r\n\r\nHowever, in India, not all sectors have picked up on automated AI assistance yet. The verticals that are hyperactive in enabling AI-assisted customer service include insurance, retail, healthcare, and banking. \u201cIn India, most of the demands actually come from fintech, while in case of Indonesia, Malaysia and Australia, it\u2019s more of telcos. If you will look at the US, it\u2019s kind of a mixed bag \u2014 manufacturing, retail,\u201d says Chakravarthy.\r\n\r\nBut considering how enterprise chatbots are revolutionising the way of companies respond to human interactions, whether from customers or employees, it won\u2019t be long before they are adopted on a larger scale and across industries. Enterprise AI has not only automated the process of interaction, but also reduced the cost and usual delays in human responses.\r\n\r\nThis is just the beginning and we won\u2019t be surprised if AI captures the entire thinking space tomorrow taking over most of the decision-making of today\u2019s times.