Lava Kumar is the Co-founder and CPO at Entropik, an AI-powered market research company. He holds an MBA from the College of William and Mary and an Engineering degree from Madras University. He focuses on developing AI-powered platforms using Deep Technology, Machine Learning, and Neural Networks. 

Kumar is also a regular speaker at market research and customer experience forums. In his free time, he explores Nordic countries.

INDIAai interviewed Lava Kumar to get his perspective on AI.

Having pursued an MBA from the College of William and Mary and an Engineering degree from Madras University, how do you think your educational background has influenced your career trajectory and approach to product development?

My engineering degree from Madras University gave me strong analytical and technical skills, essential for understanding and solving complex product development challenges. The College of William and Mary MBA added a strategic business perspective, enhancing my ability to align product development/management with market needs and company goals.

This combination of technical proficiency and business acumen enables me to effectively lead cross-functional teams, drive innovation, and ensure our products are technically sound and commercially viable. My MBA's customer-centric approach also helps me create products that genuinely resonate with users. Overall, my educational background has been instrumental in shaping my holistic approach to product management and leadership at Entropik. 

In addition to the management and leadership coaching during the MBA, multi-cultural scaling up and marketing have helped Entropik expand into global markets, positioning us as one of the global leaders in practical computing.

What inspired you to start an AI-focused company, and what unique problem does Entropik aim to solve?

Before Entropik, I was one of the founding members of an AdTech company based in New York. During my stint in the AdTech company, I was fascinated by the ability to reach and target suitable advertisements to the right people based on cookie data, 1st party and three-party data. During this time, I was inspired to think about reaching the right audiences at the right time based on the audience’s emotions, which was missing in the cookie-based targeting. 

It inspired me and my cofounder to start an AI-focused company because we wanted to leverage cutting-edge technology to understand better and improve human experiences. I was fascinated by AI's potential to analyze and interpret complex data in previously impossible ways, and it motivated me to explore its applications in understanding human emotions and behaviours.

Entropik aims to solve the unique problem of accurately measuring and interpreting human emotions to enhance customer experiences. Traditional methods of gathering customer feedback often fail to capture genuine emotional responses. Our AI-driven solutions bridge this gap by providing deeper insights into how people feel about products, services, and content. It empowers businesses to make data-driven decisions more aligned with their customer's needs and preferences, ultimately leading to more engaging and practical experiences.

As a co-founder of Entropik, what motivated you to start this venture, and what challenges did you face in the company's initial stages?

As a co-founder of Entropik, my motivation stemmed from a desire to harness AI technology to revolutionize how businesses understand and interact with their customers. Historically, understanding consumers has been limited to geographical and demographic parameters. It inherently created a significant gap in not reaching the audience at the right time. I saw an essential gap in the market for tools that could accurately capture and analyze human emotions, which are crucial for creating engaging and compelling customer experiences. 

In the initial stages, we faced several challenges. Securing funding was a primary hurdle, as we needed to convince investors of the potential and scalability of our AI solutions. Building a skilled and dedicated team was another challenge, as it required attracting talent with the right mix of technical expertise and passion for our vision. Developing and refining our technology to ensure accuracy and reliability took considerable effort and iteration. Despite these challenges, our determination to solve a unique and impactful problem kept us focused and driven.

Entropik focuses on developing AI-powered platforms using DeepTech, Machine Learning, and Neural Networks. Can you share insights on identifying and prioritising which technologies to integrate into your products?

Identifying and prioritizing technologies to integrate into our AI-powered platforms involves a strategic approach focused on innovation, market needs, and practical application. Here are some key insights into our process:

Market Needs and Customer Feedback:

We start by closely monitoring market trends and listening to our customers. Understanding their pain points and requirements helps us identify which technologies can provide the most value. Customer feedback is crucial in prioritizing features that address real-world problems.

Technological Advancements: We monitor the latest developments in AI, Machine Learning, Deep Tech, and Neural Networks. By staying informed about cutting-edge research and breakthroughs, we can assess which technologies have the potential to enhance our products significantly.

Feasibility and Scalability: When evaluating new technologies, we consider their feasibility and scalability. We assess whether the technology can be effectively integrated into our existing systems and if it can scale to meet the demands of our growing customer base.

Prototyping and Experimentation: We adopt a hands-on approach by prototyping and experimenting with new technologies. It allows us to test their capabilities and evaluate their performance in real-world scenarios. Prototyping helps us determine each technology's practical benefits and limitations.

Strategic Fit: Technologies are prioritized based on how well they align with our strategic goals. We focus on innovations that enhance our core offerings and differentiate us in the market. Strategic fit ensures that our technological investments support our long-term vision.

Cross-Functional Collaboration: Collaboration between our R&D, product development, and business teams ensures that technological decisions are well-rounded. This cross-functional approach helps balance technical potential with market viability and business objectives.

This structured approach ensures that the technologies we develop/integrate into our products are cutting-edge and aligned with our mission to deliver meaningful and impactful AI solutions.

As an experienced product manager in ad tech, how do you see the role of AI evolving in this industry over the next 5-10 years?

AI will move beyond basic demographics to create dynamic customer profiles, considering real-time behaviour, contextual factors, and sentiment analysis. It will allow for hyper-personalized ad experiences, increasing engagement and conversions. 

AI will become the ultimate campaign manager, constantly optimizing real-time bids, placements, and creatives based on performance data. It will free up human product managers to focus on strategy and innovation.

How do you ensure the ethical use of AI in your products, and what measures have you implemented to prevent bias and provide transparency?

Since our research product digs deep into user behaviour with facial coding, eye tracking, voice AI, and even general AI, here's how we fight bias and ensure things stay ethical:

Data Diversity: We collect data from a wide range of people to avoid our AI getting stuck in a stereotype loop. Think global faces, accents, and backgrounds!

Human Review: We don't let the AI run wild. Experts double-check their findings to ensure that they understand things.

Explainable AI: We build transparency. You'll be able to see why the AI makes certain connections, so there are no hidden surprises.

Clear User Control: People will have a say in what data is collected and how it is used. Privacy matters!

GDPR, ISO, and SOC II are honour badges, showing we take data privacy seriously.

GDPR keeps us honest: It ensures we get explicit user consent for collecting data and gives people control over it.

ISO gives us structure: It sets up a secure system for handling all that data, keeping it safe from prying eyes.

AICPA SOC II TYPE II builds trust: It shows independent auditors have examined our security practices, so you know your data is in good hands.

How does Entropik differentiate itself from competitors in the AI space, and what is your unique value proposition?

At Entropik, we differentiate ourselves in AI with our integrated Insights AI suite, which combines Emotion AI, Behavior AI, Generative AI, and Predictive AI. It allows us to offer comprehensive consumer and user research on a unified platform. Our platform supports mixed-method research, providing both qualitative and quantitative insights seamlessly. With real-time analytics and interactive dashboards, we enhance data visualization and facilitate swift decision-making. Our solutions are scalable and customizable, catering to various industries and regions. Our unique value proposition lies in delivering profoundly emotional and behavioural insights, predictive accuracy, and optimized user experiences, all while improving research efficiency and enabling data-driven decisions.

What advice would you offer aspiring individuals wanting to pursue an AI career?

As a product manager, I advise aspiring individuals pursuing AI careers to focus on building a solid foundation in mathematics and programming. Stay curious and keep up with the latest AI research and developments. Gain practical experience by working on real-world projects and contributing to open-source AI initiatives. Develop a deep understanding of collecting, cleaning, and analysing data. Sharpen your problem-solving skills and think critically about how AI can solve real business problems. Networking is crucial; connect with professionals in the field through conferences, online communities, and meetups. Lastly, be persistent and adaptable, as the field of AI is constantly evolving.