Lack of awareness hinders investment in deep tech: CynLr founder N.A. Gokul

Bengaluru-based robotics startup CynLr has less than 50 employees, but over 250 technology partners and vendors already between 2019 and now. The company, which launched a new research hub in Bengaluru last month, had raised $4.5 million from Speciale Invest, GrowX Ventures and Venture Catalysts, among others, in April 2022. It has raised a total of $5.25 million to date. In an interview, N.A. Gokul, founder of CynLr, discussed the problems facing robotics and deep tech firms in India, especially with respect to fundraising. Edited excerpts:

What are the vision robots that CynLr is trying to build and how do they differ from traditional approaches?

Industrial robots today need engineering hardware that takes at least two years to build. Beyond that, these robots have to be trained to do specific tasks and adapt to even minor environmental changes. Despite this strenuous training, the robot may fail to do its job even if the object is slightly disoriented or the environment in which it is working does not have consistent conditions. They are intelligent but not intuitive.

We are building vision platforms that make these robots intuitive without having to code a lot of things or undergo detailed algorithm design stages. We do 3D depth analysis of the object, which unlike other systems is not just focused on light/stereo, helping it adapt to varying shapes, orientations, and weights of the object. Our robots are designed to sense motion and ‘feel’ the object, enhancing object handling.

What are some of the challenges in the industrial robotics sector?

One of the most prominent challenges our industry faces is a lack of awareness. People often confuse tech development and tech adoption. We are at a foundational point where new tech is built and hence the approach is very different. We cannot expect a process that was built for a different industry to work for entirely new tech. This lack of clarity and awareness often hinders talent acquisition and raising funds.

Most investors come from a software or finance background. They do not understand the deep-tech space, nor can they visualise its scale. 

Another major challenge is the India discount problem. The global market assumes that India is incapable of producing novel and relevant hardware tech. We have faced this issue as well.

How would you compare the industry in India to others, like China?

As per the last International Federation of Robotics report, South Korea, Japan, and China are among the countries with the highest robotics density, which denotes the number of robots per 10,000 human workers. India lags behind severely. 

The previous assumption that China offers cheap labour is no longer true. In fact, India’s labour is cheaper, yet China is preferred because it has a thriving ecosystem in place for manufacturing. The productivity output in China is four times higher than in India. The level at which they are equipped to handle the machinery makes them really fast. This is a gap we need to fill.