Estonia's Alphachat.ai Provides “Smart Layer” Conversational AI Chatbot For Customer Experience
An interview with Indrek Vainu, CEO of AlphaChat.
AlphaChat is a Conversational AI platform. It makes customer service more efficient and customers happy. Anyone can build their own Intelligent Virtual Assistant (i.e. a smart AI chatbot) and set it up for customer support automation in less than an hour.
What inspired the idea behind alphachat.ai?
The idea started around 9 years ago, when I met my current CTO of the company Hendrik Luuk. I studied economics at Harvard back in the day, and at that time I was the CEO of a 70-person machine learning company. Hendrik’s background was in neuroscience and was strong in machine learning, so when we came together, we began to think about how AI can have a greater impact on people. After various pivots, trial and error, we settled on the automation of conversational AI as having the greatest impact. Before that point, we worked on creating a search engine for genetic data, followed by image processing domains, and eventually our various projects led us to focus on chat automation – which is alphachat.ai today.
We founded the company based on the premise that we wanted to see AI make an impact on customer use cases and truly help people. The vision of alphachat.ai is to liberate humans from doing boring and tedious work. This year, messaging became one of the primary modes of conversation, as people want to get in touch with companies via messaging channels. The challenge that many companies face is how they can deal with a bunch of customer queries simultaneously. We happen to be the intelligence layer that shields customer service teams from an avalanche of messages from customers, and provides accurate and helpful responses that customers need. Therefore, we serve as this absolutely necessary piece of automation in the middle that companies need when they scale. This is why we focus on messaging – because many businesses are becoming commodities, and customer experience at the end of the day is the way to differentiate.
What are some key AI use cases that you can share?
Our focus is on conversational AI for customer service. In its simplest form, conversational AI might look like a clickable bot that addresses your questions with pre-made answers. The evolution from this would be intelligent virtual assistants (or virtual customer assistance), which are advanced bots that understand natural language and provide more in-depth responses to specific queries. For example, a person may ask a bot to break down the specifics of an invoice he or she received from a telecom company. Or maybe a person has a question regarding when their package is arriving. These types of queries that are simple for the AI to understand and automatically solvable are the types of solutions that we provide with our Natural Language Understanding chat automation product.
What are your predictions for the future of AI chatbots?
We believe that we are building out solutions that are absolutely necessary for the future. Expanding on my last thought about the shift towards natural language processing chatbots, I believe the future will make human-machine communication more commonplace. The bet that we are taking is to move beyond natural language understanding, and into the domain of providing personalized information through authentication. For example, if you think about a website, it’s basically a customer’s window into a company. Therefore, what we are doing is creating a smart layer over the website with a conversational chatbot, so that the customer can interact with the chatbot and get the answers he or she needs. For example, a person might have a question about an invoice. The bot would then digest the natural language and then tell the person to log in via a pop up window. Then, the bot would tell the person that there is an invoice for 45 euros that was due yesterday and must be paid, and let the customer know the breakdown of that 45 euros based on the services used. In this way, not only is the bot capable of understanding natural language, but it’s also providing customers with personalized information about their accounts, and the products and service that they used. The reason why this is important is because a trend that we have been seeing lately is that peoples’ queries are very specific. They don’t care about general things like knowing the average time of delivery for a package to a customer’s doorstep. Instead, they really want to know where their package is in transit and when their specific package will arrive. In these cases authentication of users will provide very strong benefits for users, and bots can then provide as much information as human customer services agents.
Do you have any competitors in Estonia/abroad, and if so, what is your competitive advantage?
There are millions of different chatbots out there, but what differentiates us is that we are focused on providing the best experience for web-based messaging automation. When you think about the categories I’ve introduced, our main focus is on built-in natural language chatbots, which automatically separates us from 90% of our competitors. Then for the other 10%, we stand out with our deep automation, or the authentication, building and retrieval of personalized information. Anybody can go to alphachat.ai right now and build their own smart chatbot. You don’t need any kind of coding experience, and if you happen to be a developer with experience, our Alpha OS allows you to build your own bots with authenticated queries. Our solution makes it so that you don’t need to waste your budget and time on expensive consultants or run 3 month long POCs and pilots. Instead, you can leverage alphachat.ai to build a very competitive and practical conversational AI. In essence, if you’re capable of running a simple Zoom call, then you can use our product. But we still provide a platform that is sophisticated enough where it doesn’t lose the functionality for conversational AI. We’ve unlocked that right balance and have brought it to market.
What are your future ambitions for the company?
Growth is our focus. Currently we are processing around a quarter of a million conversations per month, and our objective is to scale that up to tens of millions. What’s unique about our company situation is that we have not received any source of outside financing and are profitable on our own. Our team has a very strong technical understanding in machine learning, and our entire product has been built in-house within the company. This means that we haven’t outsourced anything, and that we are able to control the whole experience of the product for the user. Therefore, we have endless ways in which we can carry out our roadmap and provide the best possible user experience.
About Indrek Vainu:
Indrek runs alphachat.ai, a Conversational AI company automating customer experience through messaging. He is an experienced entrepreneur having previously managed a 70-person machine learning company. Indrek holds a degree from Harvard University and is a frequent speaker at technology events.