Enabling Business Operations With Computer Vision: Interview With Tanay Dixit, CPO of Wobot.ai

Please tell us briefly about your background.

I’m Tanay Dixit, Co-Founder & CPO of Wobot.ai. Previously I was involved in product development across deep learning, computer vision, web, and mobile for over eight years.

At the age of 19, I co-founded Senfinance while preparing for CFA examinations, which got me involved in the world of technology and led me towards the journey in entrepreneurship and product development.

I then co-founded a development agency called WEMO with the intent to help brand managers with deep learning technology-based marketing solutions. Later, for several years, I was involved with deep learning and computer vision in fashion, building one of the most innovative products in the retail space, Talespin.

Currently, I'm building Wobot.ai, which we established in 2017, and I oversee product development, where I augment deep learning and computer vision for various sectors with the intent of commercializing deep tech at scale.

What's your company called? And in a sentence or two, what does it do?

The company is called Wobot.ai, and we are a Video Intelligence Platform that enables businesses across food services, manufacturing, retail, hospitality, and others to do more with their existing camera systems. We help go beyond simple security and improve operations and gather insights for operators.

What is the origin story?

Our story dates back to 2015 when the co-founders were working on different pieces to a puzzle. Back then, we were running our own startups. Few of us were applying computer vision to fashion, and the other had built a smart audit and inspection platform to help operators digitize their checklist on an app.

In 2016 while working with customers across different sectors through the inspection app, we realized that there was a need for an autonomous validation for operational tasks as well as the possibilities of computer vision. At that time, we also learned that 100’s of millions of cameras were mostly being used for post mortem analysis and that too for security purposes only.

Later that year, we had a ‘what if’ moment and imagined a feedback process for operations to happen through these existing cameras. We believed that such a platform would make not only such feedback pre-emptive and non-biased but also continuous as well as actionable. So, in 2017, we started converting food-related operational checklists to computer vision models that could run on existing CCTV cameras. Soon enough, we realized that this could work across industries and checklists. That was it for us to accelerate our journey to today.

What do you love about your team?

Passion, commitment, out-of-box thinking, and our nimbleness.

If you weren’t working at your company, what would you be doing?

Somehow would have stumbled later on to this opportunity. In the meantime, I would be building another startup.

At the moment, how do you measure success? What are your core metrics?

A lot of our success metrics are driven by customer-driven index as well as team growth.

What’s most exciting about your company traction to date?

We continue to add intelligence in many cameras across different sectors and use cases.

What technologies are you currently most excited about, and most worried about? And why?

The future of computer vision is bright, and we are still scratching the surface with deep learning and AI.

What drew you to get published on HackerNoon? What do you like most about our platform?

Great place to be in as the audience is technologists.

What advice would you give to the 21-year-old version of yourself?

Focus and concentrate on the small tasks that lead to the big ones.

What is something surprising you've learned this year that your contemporaries would benefit from knowing?

While the bigger picture is important, it is essential to plan and execute the smaller steps.