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Here is the second of three parts of our interview with Aakrit Vaish (you can find the first part here, and the third part here), CEO & Co-Founder of conversational AI platform Haptik. Aakrit co-founded Haptik in 2013 as a unique mobile assistant platform before it transitioned into conversational AI and is now one of India’s foremost platforms in that space.
Q 8. A confluence of technological developments are now helping voice become a serious contender to text – how do you see this playing out and what is Haptik doing to ready itself for this?
As mentioned in the previous question, Voice is going to lead the next phase of growth for the industry and subsequently Haptik in general. It just intuitively makes sense, it’s easier and takes lesser time to speak a command than type it.
We’ve built out capabilities to build voice bots, and are first large implementation goes live next week actually. Stay tuned and watch this space 🙂
Q 9. NLP in the Indian language space on a tech-level appears to be still in the rudimentary stages. Is this impression correct? And if so, what are Haptik’s plans in terms of working with Indian languages within chatbots?
Yes, you are right when you say it’s still rudimentary in terms of technology. But that’s also because the problem is so hard to crack. Most Indian speech is a combination of multiple languages and not just one. The concept of “Hinglish” is unique to India and there is no set data set available for it.
75% of our R&D efforts are going only towards getting vernacular languages right. We are going to be spending a lot of time and investment here, and expect at least 5-10 live implementations across languages from us in 2019.
Q 10. How does Haptik intend to stand out in the increasingly crowded enterprise chatbot market?
Focus on few key things:
- Largest base of consumer data – The fact that we also have a direct to consumer product means that we get real conversational data from end users, which helps train the models continuously over time. This just continues making our systems better, and always ahead of any other competition that will depend on third parties for this.
- Dedicated use cases – We will only focus on end consumer engagement and not touch internal operation areas like HR, IT helpdesk, etc. For our focus use cases, we will build out products to solve end to end business problems which could go beyond the scope of simple chatbots.
- Types of clients – We have a set target customer segment that is very refined and precise. We will only go after those and not touch anything outside.
- One stop shop – Continue to build and have all capabilities in house, such that our customers don’t have to go anywhere else to solve their set of problems.
Q 11. The fledgling self-service chatbot space appears ready for take off. Is Haptik planning to launch a DIY platform? And where do you see the DIY offerings settling?
The DIY mobile app space never took off, so I have my doubts here for chatbots as well. We have no plans to have a DIY platform, though I do see there could be some niche plays here if done correctly. Smaller market and a bit of a distraction for us.
Q 12. Haptik has an AI trainer team that works on the bots – what does that training entail exactly? Could you elaborate on the process? And what kind of skills does a ‘trainer’ possess?
Yes, that is again one of our differentiators. Our AI trainers take a bunch of unorganized data sets from clients and turn them into bot workflows using Haptik tools. These data sets could be FAQ documents, training manuals, product catalogs, etc. The tool then has a number of features for them to make the conversion happen.
The skill set an AI trainer needs to have is analogous to a business analyst. They need to understand the problem the client is trying to solve, take the data, use a set of tools and accomplish the tasks.