Conversational AI platform Floatbot, incubated at NASSCOM CoE DS&AI Bangalore, is making steady inroads into India’s fast-growing conversational AI space by offering hybrid solutions that include omni-channel chatbots, voicebots, advanced NLP, speech recognition technologies & curated industry data.
Recently, IDC published a report titled IDC Innovators: Conversational AI Software Platforms in Banking and Financial Services in India, profiling three Indian companies that offer AI-based conversational software platforms for BFSI. One of the companies cited in the report is Floatbot – a Bangalore & Ahmedabad-based conversational AI platform supports speech recognition, Natural Language Processing and Machine Learning technologies.
Experts at IDC state that these chatbots are enabling businesses engage with customers more strategically, pushing conversion rates.
Here’s an interview with Jimmy Padia, CEO & Founder, Floatbot, who talks about his company’s journey charting new trends in this space.
Floatbot is ideally one of the first major players in the conversational AI space in India. What makes your interface so advanced and mature?
Since we launched, we were very clear that Floatbot will be fully DIY or “no code” platform that will allow a non-developer to develop the most comprehensive Chatbot and launch it across multiple channels. Overtime we have added several advance features including Voice modules. Now, Floatbot has become a hybrid platform, one of the very few globally, that allows development of Omni-channel Chatbot and Voicebot, within hours, pre-integrated with contact center, that allows users to seamlessly switch between voice and chat channel and still maintain past sessions and context. Floatbot’s no code platform combined with advance NLP, speech recognition technologies and industry specific data, gives us an edge over our competitors and makes us preferred choice to work with.
While conversational AI platforms are catching on in a big way, several pure play AI experts claim that very few companies truly understand its potential for business. How are you delivering that value to your customers?
In our initial engagement we understand KPIs of customers to launch Chatbot or Voicebot, whether its to increase customer experience, leads or to reduce customer support cost. We play role of consultants in extracting required details from customers and than start building bot that meets their KPIs. Also, we believe that Conversational platforms that are agnostic to industries and use cases will have hard time to add “real” value to business. We at Floatbot are narrowing down on industries and use case so that we can solve specific business problem and add “real” value to them. Our AI would be smarter than general platforms because of the past data and ready bot templates we would have for those use cases.
Tell us about your customer engagements – who do you work with and in what capacity? You can give 3-4 examples of your most important clients
Our large customers include Andhra Bank, African Development Bank, Calcutta Electric Supply Corporation (CESC), LifeCell, Shaadi.com, Pimpri Chinchwad Smart City (PCMC). Across all of them, we are working to automate customer support and FAQs asked by customers. For few customers, we have also provided digital services such as knowing balance, downloading statement/bill, bill payment, complaint registration. For PCMC, we are providing eGovernment services such as Property tax, Water Tax, Birth Certificate, Death certificate and Citizen grievance through Chatbot. At present, we are also engaged in paid POC with large international banks. insurances and financial institutions outside India on niche use cases.
You’re working with Andhra Bank, a govt bank that one would imagine isn’t as technologically advanced as privately owned retail banks. How was the experience building a conversational AI interface for them and what challenges did you encounter?
Unlike other govt banks or govt organizations, our experience in working with Andhra Bank was very professional and thorough. It changed our thought-process for a govt bank. Infact, we are undergoing UAT for PCMC right now and we are going through similar experience. Its no longer the case that govt banks or govt organisations are not technologically advance. They have become equally smart and advance, moreover most of the times they have one of big 4 as their consultants to ensure quality of delivery is matched.
In Andhra Bank, we worked with SPOCs of 17+ departments to collect 15,000+ knowledgebase documents and FAQ. GM of Andhra Bank and IT manager were so much particular about NLP accuracy, that in weekly meeting, GM would himself ask questions to Chatbot and check the answers. For UAT sign-off, total 50,000+ NLP queries were fired to test the NLP accuracy. Because Andhra Bank is a large bank, it was difficult for us to co-ordinate with all departments to get required data. It was also challenging to work with multiple vendors of Bank to enable payment gateway, Insurance services in Chatbot. IT team of Andhra Bank has been very supportive and SPOCs did fabulous job in following up and getting required information.
Name 3-5 major challenges companies face in developing conversational AI platforms for businesses today and how do you think they can be overcome?
I think that it will be difficult to get business for someone who is building Conversational AI platforms today. Following are the challenges they might face:
- Compete with mature players in same space
- Lack of customer references
- Platform may still be maturing and is not stable enough
- AI models are still not mature enough
- Price points may not make economic sense
My suggestion to these players would be to work on specific use cases or business problems that are not yet solved, wherein they can get quick wins and show differentiation.
Tell us a bit about what’s next for Floatbot? What new technologies/solutions are you working on and their relevance to enhancing your business
Narrowing down is way forward for Floatbot. We are narrowing down on the markets/regions and the industries/use cases that we work on. We are working on Speech technologies such as Speech to Text and Text to Speech. We already have it working for 2 languages, and now in process to add more regional languages. Floatbot is pre-integrated with Call Center solutions. We believe that Conversational AI platform horizontal across all industries and use cases will not work in future. Each one will have to find its sweet spot and focus on those use cases to accelerate growth. That said, even we at Floatbot are working to narrow down the use cases, markets and industries.
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