- Tell us about the business you represent, what is their vision & goals?
- Can you share a little bit about yourself and how you got into the field of artificial intelligence?
- What do your day-to-day responsibilities look like at your organisation?
- Can you share some of the proudest achievements you've experienced in your career?
- Which industries and processes do you see as the greatest opportunities for applying artificial intelligence?
- What are the most significant changes you expect to see in business due to AI?
For the next interview in our series speaking to technology specialists from around the world, we’ve welcomed Anand Prajapati, Co-founder and CTO at Leena AI.
Anand’s journey with Leena AI has been a culmination of his keen interest in all-things-tech, and his desire to provide the best HR solutions for large enterprises. As the Chief Technology Officer, he is responsible for overseeing existing products and conceptualizing new solutions.
Leena AI is an autonomous conversational virtual assistant that helps enterprises better service delivery for employee-facing teams. Positioning itself as a ‘Siri’ for employees, Leena AI empowers employee-facing teams by automating repetitive and routine tasks, allowing them time and resources to focus on strategic aspects over transactional ones.
Leena AI plays well with 100+ plus platforms, including SAP SuccessFactors, ADP, Oracle, Workday, and Microsoft Office 365. Founded in 2018, Leena AI today has over 350+ customers, including companies like Nestlé, Puma, AirAsia, Coca-Cola, Sony, and Etihad Airways with 6 million+ employees worldwide relying on the platform. The company’s products presently support 100+ languages.
Can you share a little bit about yourself and how you got into the field of artificial intelligence?
From a very young age, I was interested in the real-world applications of technology. Learning about Natural Language Processing and AI in school further piqued my interest, leading me to choose a tech-based college degree. I studied industrial and production engineering at the Indian Institute of Technology, Delhi, one of India’s most prestigious technology institutes.
Though my major was not related to computer science, it continued to fascinate me. During my penultimate year, I picked up a project on throughput, where we aimed to improve the measurement of the number of units of information a system can process in a given amount of time at a manufacturing shop floor with the help of artificial intelligence.
I worked on it for about 1.5 years, exploring multiple strategies to improve scheduling and maintenance in a shop. That taught me a lot about the real-world application of AI and the importance of collecting data in the right format. It has been a long road from there but I am glad to have had the opportunity to work in the field of AI - one that sparked my interest to such great lengths.
Initially, I spent a big chunk of my time working on product development. Alongside that, I worked on building the dream teams for multiple departments such as product, engineering, customer support, and information security, among others. Back then, since we prioritized shipping over documentation, a good amount of my time was spent recording the old systems.
Over time, as my role developed, I started working on higher-impact tasks that aimed at increasing efficiency and ensuring that our customer support function was the best out there. Currently, my work is focused on the Infosec team which essentially helps protect our digital data, align cybersecurity with our business goals, and ensure a culture of strong information security from the inception of the product feature.
We pivoted twice before Leena AI. Our first product was an AI-based content summarization algorithm named Zuppit Tech Solutions. We had just one employee when we decided to veer to a horizontal chatbot development platform named Chatteron.
At the time it seemed like a herculean task, but we worked day and night with extreme focus and launched it within just 3 months. We were already discussing automating IT functions for a large BPO company when we launched Chatteron. Without any background in building voice automation, I was able to add voice capabilities to our bot platform, which essentially meant that you could access the same bot via a phone call.
We have come a long way since then. Today at Leena AI, we have an engineering team of about 60 people who work tirelessly towards developing our extensive suite of solutions so we can provide our customers with the top-tier features that an enterprise virtual assistant can.
Which industries and processes do you see as the greatest opportunities for applying artificial intelligence?
So far, the best applications of AI have almost always been to support and enhance human decision-making, not entirely replace it. The healthcare industry has benefited a lot from using AI, clustered regularly interspaced short palindromic repeats (CRISPR) being a prime example of that.
While we have seen many applications of AI as a consumer, using AI to improve employee productivity still hasn’t reached its full potential. Information retrieval is an interesting use case here, like finding meaningful information from policy documents, shared knowledge bases, and internal portals among others. This is going to completely transform how people spend their time at offices.
In general, AI is all about optimization - either through automating repetitive routine work or analyzing huge amounts of data (past and real-time). It helps you make better decisions, and assist people in performing routine work in a much more efficient way, thereby demanding people to upskill themselves. If it doesn’t require creativity, AI can do your job given enough data.
What is your experience with using AI-backed data analysis or log management tools? What do you think is the benefit of using a log management tool with machine learning capabilities for an organisation?
At Leena AI, we’ve integrated with 100+ external systems, as well as following microservices-based architecture. From servers to microservices, every component that is a part of our product generates logs which are then sent to a centralized logging system. We generate hundreds of gigabytes of logs every day which is impossible to analyze manually, and this is where we adopt machine learning.
It has been tremendously helpful in helping us identify and fix issues that were otherwise very difficult to see. As technology improves, we expect to see far-connected devices and even more data. In today’s world, an AI-based log management tool is a must-have to not only derive meaning out of this haystack but also to use the respective information to improve products. If you enjoyed this article then why not check out our guide to observability vs monitoring or our article all about NRC RG 5.71?