[Techie Tuesday] Meet Nitin Das, who is building teams of AI experts to make industries and businesses more sustainable

In this week’s Techie Tuesday, we feature Nitin Das, Co-founder and Chief AI Officer at AiDAsh.
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For Nitin Das, joining IIT Delhi was a no-brainer. While today he is the Co-founder and the Chief AI officer of AiDash, a startup that builds artificial intelligence (AI)-first suite of solutions for an enterprise’s business operations — Nitin’s interest and curiosity in technology had begun early. 

Hailing from Jabalpur, Nitin spent his childhood experimenting with TV sets and other electronic devices. 

“Initially, I did damage most of them in the process, but my family understood my passion and encouraged me. My father often bought several books and equipment to help develop my interests. This helped me develop a deep liking towards technology and its applications in real life, and still inspires me to continue the journey,” recollects Nitin. 

While technology is so deeply embedded in every aspect of daily life now, in the 80s, it was a different story. Technology then meant — landline telephones, large CRT TV sets, and radios. 

“I had always been intrigued by the functioning of electronic circuits. Each time I opened gadgets and discovered the working of circuits, I got excited and my love for technology grew,” says Nitin. 

"This interest kept growing with me. With each phase of my education and career, I was exposed to different types of interesting and innovative technologies, and I gradually moved to the deep world of artificial intelligence and machine learning.” 

IIT Delhi and many firsts

Nitin’s first exposure to a computer was when he joined BTech at the Computer Science department of IIT Delhi in 1999. He recalls how he enjoyed programming thoroughly, especially where he could simplify problems with the click of a button, that otherwise would take longer to achieve. 

“During this period, I built a lot of games and solved numerous physics problems that I only understood theoretically until then. Now, I could understand its real-life applications and solve it through computer program simulations. As I started solving more complex problems, my way of thinking changed drastically, and I started diving deep to understand and solve them better. "

He continues, "During this period, I also learned about voice and video processing at scale, mathematical problems related to AI/ML which finally got me into that world. I started playing around with different problems involving demand forecasting, media mix optimisation, large scale distributed systems, image and video-based AI/ML solutions at scale, and so on.”

The practical applications attracted Nitin more, and he believed that any real-world problem could be converted into a computer program and its solution could be derived by assuming different scenarios.

Nitin Das

The world of distributed computing

“My first job, in 2006, was to build distributed data and computing framework at a time when any of the now commonly available distributed computing environments were not available. This helped me learn to manage large datasets, and the building blocks of designing the frameworks. It also helped establish my knowledge of handling large datasets and computations at a large scale. And in the longer run, this became the base of my learning around big data and AI/ML,” says Nitin.

He later joined an analytics company and learned about customer behaviours through just in time analytics. "There, I got a chance to work on data analytics and extracting meaningful information to make it usable to customers,” adds Nitin. 

Starting up 

Nitin’s first entrepreneurial stint was in 2008 when he co-founded Uvaca with his friends. The company dealt with voice and video over IP. 

“We built hardware and software stack to transmit TV channels and Internet over WiFi on license-free frequencies of 2.4/5.8 GHz. In a short span, we managed to build some good products and attracted some customers but were a little ahead of time and decided to wind up the business and explore other exciting emerging technologies. But this experience introduced me to the thrill, challenges, and complexities of running a startup,” says Nitin. 

After Uvaca, Nitin opened a few stealth-mode startups mostly in the computer vision domain, at a time when it was not too popular in the market. 

In 2010, Nitin joined Knowlarity, where he developed the initial stack for Interactive Voice Responses (IVR) related applications. And then a year later, he joined Kvantum, an AI-based consumer insights and marketing performance analytics company as its first employee. 

“Here, I built the initial AI/ML deep tech stack for data analytics and generating insights at a large scale. Kvantum was recently acquired by Yum Brands. This stint introduced me to the applications of AI/ML in the real-world scenario,” explains Nitin. 

Target and next startup 

In 2015, US retail giant Target acqui-hired Kvantum, which saw Nitin join the former as Lead AI Scientist. Here, Nitin developed several AI and ML models related to demand forecasting and video-based consumer analytics, and learned about the applications of AI/ML in the retail industry. 

“I was deeply involved in using technology to extract meaningful information about consumers and products, and how this information can be utilised to increase return on investment (ROI), customer footfall, introduce new items, placements, and so on.”

Four years later, in early 2019, with the aim of starting up again, Nitin co-founded AiDash with Abhishek Singh and Rahul Saxena.

“All my prior experiences around building large data frameworks, data analytics, AI/ML, and operating a startup helped contribute to building and growing AiDash to what we are today along with my amazing team,” says Nitin. 

Nitin admits it wasn’t until he started his career that he understood the practical applications of computer solutions and how they can be used to solve real problems of the world. 

“All the theory that I learned during my academic life was finally put to practice during my career. The fundamentals I learned about mathematics, programming, and AI/ML has helped me during my entire career and especially at AiDash as we are a deep technology company and our work involves building complex software stack, computer vision-based algorithms, AI/ML algorithms, large datasets, and distributed computing,” says Nitin. 

In co-founders Abhishek Singh and Rahul Saxena, Nitin found a shared love of using AI/ML technologies to solve problems that impacted the environment and society. 

“With AiDash, we wanted to build intelligent products around vegetation management, distributed asset management, disaster management, and sustainability planning. We focused on problems in areas like power utilities, oil and gas, transportation, construction, wastewater, and mining. With traditional operations and maintenance practices, these industries face huge problems in terms of cost and time. Their assets are distributed over large areas and critical assets are often missed,” says Nitin. 

Using AI for larger problems 

At their AI-first vertical SaaS company, the team employs multispectral satellite images and AI-powered algorithms to solve end-to-end problems of enterprise customers. 

“We help industries monitor and manage their assets while helping them to plan end to end executions on an intelligent, easy to use SaaS platform. This helps reduce operational and maintenance costs, and increases the reliability of services. Our disaster management and sustainability planning products have a direct impact on the environment, and contribute to the larger problems, such as gearing up for disasters and initiating quick post-disaster relief operations, measuring and enhancing biodiversity values of land, water, and air, and tracking and reducing the carbon footprint for sustainability initiatives,” says Nitin. 

According to Nitin, the team caters to a niche domain with very few competitors, and thus took a while to understand the core problems of the different industries in-depth. During the initial phase, geospatial technology and its application with AI/ML was quite new and challenging, he adds. 

Setting up the team was also a challenge as it was difficult to find people who were experienced in both AI/ML and geospatial technologies. Those with expertise in the geospatial domain were not experienced in building production-grade solutions. To overcome this problem, the team enabled people both on the geospatial and AI/ML sides. 

“Now we have a stellar team that builds deep tech products involving expertise on both the domains. So, in a short span, with the right focus and team, we developed expertise around the geospatial domain and AI/ML, and extract meaningful information that helps build deep tech products for different industries,” says Nitin. 

A Series B-funded company, Nitin adds that AiDash is on a mission to make industries more sustainable and resilient by solving interesting challenges at the intersection of satellites, AI/ML, and geospatial analytics. 

To support that, the team is building scalable infrastructures around data and algorithms, exploring varied opportunities, and understanding deeper problems in the sustainability and disaster management domains across the globe. 

Today, while hiring for a core domain or technology, Nitin believes it is important to understand the depth of problems to be solved and the skills required to solve them. Start building your team from top to bottom, he says, and emphasises the importance of hiring senior members who have a vision, passion, and expertise in building teams.

“My advice to young techies would be to not be driven by brand name and compensation; be motivated by the opportunities to grow and learn. Find companies and opportunities that can provide a real boost to your career in the long run and that also aligns with your expectations and aspirations. A career should give you the opportunity and equip you with the skill to solve problems with what you learned,” concludes Nitin. 
Edited by Anju Narayanan

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