The global conversational AI market size was valued at US$5.78 billion in 2020 and is projected to reach US$32.62 billion by 2030, registering a CAGR of 20.0% from 2021 to 2030. Conversational AI consists of a set of technologies that enables automated messaging and speech applications. Conversational AI is the set of technologies behind automated messaging and speech-enabled applications that offer human-like interactions between computers and humans. Boost.ai is a leading conversational AI platform, providing the most scalable customer service chatbot solution for the enterprise. Analytics Insight has engaged in an exclusive interview with Bill Schwaab, VP of North America for Boost.ai

Kindly brief us about the company, its specialization, and the services that your company offers.

Boost.ai is a leading conversational AI platform, providing the most scalable customer service chatbot solution for the enterprise. Boost.ai's conversational AI platform enables organizations to build virtual agents that drastically increase customer satisfaction by providing customers with 24/7 support and reduced wait times while providing highly accurate information. The platform's ability to scale vertically and horizontally with a network of bots in production without losing accuracy sets it apart from the rest. The service is ideal for large-scale deployments looking to employ a smarter chatbot for both employees- and customer-facing applications.

Powered by proprietary Natural Language Understanding (NLU), the virtual agent (VA) can parse the nuances of human language and comprehend the underlying intent of a customer's request. It understands what is important in a conversation and is able to reduce false-positive responses by over 90%, leading to 75% of customers choosing to talk to the bot over a human, when offered the choice. Boost.ai supports any language, and comes out-of-the-box ready with 1500+ topics by vertical, with 50+ live VAs in the financial services industry alone.

Additionally, with the solution being no-code, it's easy for teams to leverage employee experience to train the AI on new topics without technical knowledge of the back end of the service. The intuitive interface to existing backend systems helps Boost.ai to go above the standard limit of requests and perform customer-specific advanced processes such as transferring funds or blocking cards without any human interaction unless specifically requested.

Could you highlight your company's recent innovations in the AI/ML/Analytics space?

There are three proprietary technologies unique to the Boost.ai platform: The Boost.ai Automator, Virtual Agent Network (VAN), and Boost.ai's Automatic Semantic Understanding (ASU). Each can be considered a point of differentiation in the conversational AI space. The  Boost.ai  Automator scans and analyzes a company's existing data sources (website, chat logs, other chatbots) and combines them with relevant search data to build a fully-functioning virtual agent in a matter of hours,

rather than days or weeks. With the Virtual Agent Network, Boost.ai is able to deploy multiple virtual agents together in a functioning network. This makes it possible to connect disparate departments within an organization via a single chat interface so that customers can seamlessly move between bots without breaking the flow of conversation. Finally,  Automatic Semantic Understanding (ASU) – is a  proprietary collection of NLP algorithms that enable virtual agents developed on the Boost.ai platform to gain a better understanding of end-user intent. ASU enables chatbots to more accurately parse complex sentences, identify multiple intents within the same sentence (and provide actionable responses rather than a confused "I don't know"), and reduce false-positive answers by over 90%.

Tell us how your company is contributing to the IoT/AI/Big Data Analytics/Robotics/Self-Driving Vehicles/Cloud Computing industry of the nation and how the company is benefiting the clients

We at Boost.ai provide enterprise organizations an opportunity to increase customer satisfaction, cut costs, and drive revenue with the help of our industry-leading conversational AI platform. Boost.ai's virtual agents can interface with already existing backend systems and a customer's own account via user authentication to perform advanced processes without the need to involve a human service representative. Our platform is also self-learning, and scalable for much larger deployments, handling thousands of intents. We've taken a powerful technology, and delivered simple to use AI that you can analyze and improve – without losing the human touch!

In the public sector, our technology is used as a front-end for IoT, Open Data, and Robotic Process Automation (RPA). The Södertälje municipality in Sweden uses the boost.ai platform to interface with various back-end systems allowing residents to gather information about air quality levels, parking occupancy, beach water temperature, and availability of electric car charging stations in real-time via the virtual agent.

What is your biggest USP that differentiates the company from competitors?

What makes us stand out from the rest of the field is our ability to truly scale, and scale with accuracy. We believe that in order to generate genuine value from conversational AI it is necessary to follow a simple equation: multiply the number of interactions by the value per interaction. Our platform is geared to maximize this equation by being eminently scalable across a broad scope of topics. The more interactions our virtual agents handle, the more value they create for our clients – something that is often the opposite of many other solutions.

While our virtual agents come pre-built with thousands of topics for rapid spin-up time, I find it so impressive that our bots continue to learn and grow in the spaces we deploy them. We believe and invest in empowering our customers to take on the ownership and maintenance of their chatbots and virtual agents. Building and iterating a solution that not only works for our customers but is something they took part in generating is key to any engagement we have.

How is your company helping customers deliver relevant business outcomes through the adoption of the company's technology innovations?

We help our customers to better serve their own customers through scalable self-service. AI-powered virtual agents help relieve the high volume of traffic to internal teams and contact centers, freeing up valuable time to customer service teams, while allowing the option to seamlessly transfer to a live agent when needed. Boost.ai  virtual agents consistently provide a 90%+ resolution rate and receive a 94% customer satisfaction score from clients. Additionally, with chatbots and virtual agents, teams can extend their service hours to 24/7, serving a variety of customer needs at any given time. Our CAI also enables our customers to provide instantaneous responses to customer service queries, which is a consistently online and demanding world that is integral to remaining competitive.

What are some of the challenges faced by the industry today?

Some key challenges the industry faces today include the accuracy of AI programs, consistency of their information, ease of access to documentation, and limited service hours among others. Take, for example, an organization's IT support system. On any given day they can encounter any number of issues or outages that create a flood of demand for assistance. Meeting this rush of tickets or calls at a moment's notice can be expensive and impractical, especially if disruptions are only temporary. The effective management of scalability, however, ensures a higher level of support, and means like chat automation unburden employees from certain tasks and can increase their satisfaction as well as reduce internal turnover.

What are the key trends driving the growth in Big Data analytics/AI/Machine Learning?

Key trends driving growth in the artificial intelligence sector are innovations like natural language understanding and voice automation. As conversational AI matures and can act in a more congruent manner with human agents, it can handle a majority of repeated customer interactions. What this looks like is organizations perhaps taking a chat-first approach through virtual agents or intelligent voice programs, or human agents querying virtual assistants for technical information all while staying on the line. In either case, human agents are free to focus more extensively on difficult customer cases to furnish a higher level of service. This is all possible because developments in natural language processing have made it much more precise than previously.