Lessons from the Trenches: Building a Deep-Tech Startup

Almost three years in the startup world, especially in the deep-tech space, has been quite the roller coaster ride. It has also been packed with valuable experiences. In this article I will share some lessons learned along the way, which I hope will be useful for prospective tech entrepreneurs.

Validation First

Entrepreneurs are dreamers. We dream of a future improved by our ideas, and we work hard to make them a reality. This desire to ‘disrupt’ the status quo and impart change is inherent in all entrepreneurs.

However, dreaming without an objective mindset could, more often than not, lead to tunnel vision, and ultimately failure. Even dreams need to be measured up against the cold reality of the market. Sometimes that means the market is not ready for the solution, or the need is not as pronounced as you thought, or the barriers to entry are high, etc… Did you know that the number 1 reason startups fail is because there is no real market need for the solution?

I had to learn to be more objective from the get-go, and that was hard work. It is hard not to force your biases on reality, especially with a tech idea close to your heart. But one has to let go of that attachment to be able to step back and validate the idea objectively. This is why I strongly believe in validation first. The more time spent developing a solution prior to validation, the more attachment one has to it, and the less objective one is.

Long Product Development Cycle

Many deep-tech solutions originate in academic institutions, where time is abundant, labor is cheap (Masters and PhD students), and infrastructure is readily available. Thus, spin-off startups from academic institutions do not spend the bulk of their time on product development, since most of the research has already been done.

We on the other hand, wanted to validate first, which meant that the entire product development cycle was going to take place within the company. This created a challenge for us, since unlike academia we did not have the luxury of time, money or research infrastructure. We had some initial ideas on how to develop the product (given our technical training), we got a few grants, hired our first employee, met weekly to discuss progress, and outsourced experiments when possible in order to make it happen. We managed to develop a proof of concept after 2 years of hard work and limited resources, which was no small feat.

The Right Team

I cannot emphasize enough the importance of the team, especially in the early days of a startup. I am not talking here about the co-founders, since that is a separate topic, but rather the team assembled around the co-founders. We learned quite early on that in addition to technical expertise, it is imperative that our employees have the right attitude. Here are three characteristics that we have found to be very important in addition to technical skills:

1. Willingness to face challenges and to problem solve creatively. It is normal for us to feel discouraged when faced with difficulties, and believe me there are lots of those in the startup world, but it is dangerous if we remain in a state of discouragement. Therefore, employees working in deep-tech startups need to understand that there will be more challenges, and they will need to be driven towards finding creative solutions, and not to get stuck in a state of discouragement.

#lessons-learned #deep learning

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How This Cybersecurity Startup Is Using Machine Learning

The COVID pandemic has massively escalated the surge of cyberattacks and data breaches despite having robust security controls, software, and solutions abundantly available in the market. A lot of this could be attributed to the vulnerability businesses offer the cybercriminals to take advantage of the situation quickly. While the conventional cybersecurity approach has benefited many, having cybersecurity without cyber-intelligence and necessary awareness can put the security professionals off-guarded to more complicated and novel threats.

Furthermore, with limited cybersecurity resources, businesses need to prioritise their efforts to strengthen cyber posture effectively; however, many organisations do not have an anchor point or a guiding principle, to begin with. With cyber-intelligence inputs missing from cybersecurity capabilities like incident management, vulnerability management, risk assessment and brand monitoring, businesses end up running their security practice in silos instead of an integrated approach.

And, thus, in an attempt to revolutionise the cyber threat visibility and intelligence market, CYFIRMA, a cyber analytics startup assists businesses to understand the relevance of the current threat landscape. Not only it provides insights on threat actors and indicators, emerging threats and digital risks, but also automatically applies intelligence into cyber posture management. To dig deeper, Analytics India Magazine got in touch with the chairman and CEO of the company, Kumar Ritesh, to understand how the company uses a predictive intelligence-driven approach to discover cyber threats.

#startups #cyber security startup india #cybersecurity startup #machine learning #startup #startups

The Novice's Guide To Side-Hustles

I don’t know about you but in my view, work culture seems to have taken a 360- degree turn since the time I got into the workforce in 2012. For years before that, I saw people stay in the same job for years.

They often left their comfort zones and hometowns to immigrate to new cities and countries in search of that one perfect opportunity — in which they then stayed all the way till retirement. Say for my father and many other relatives who stayed in the same job for more than 20 years.

Nothing wrong with that however the times have changed since then. In recent times people have started changing jobs more frequently owing to COVID more and more people, as well as, companies are looking for gig workers and contractors.

#startup-lessons #side-hustle #startup-advice #web-monetization #startup #startups #startups-top-story #entrepreneurship

Lessons from the Trenches: Building a Deep-Tech Startup

Almost three years in the startup world, especially in the deep-tech space, has been quite the roller coaster ride. It has also been packed with valuable experiences. In this article I will share some lessons learned along the way, which I hope will be useful for prospective tech entrepreneurs.

Validation First

Entrepreneurs are dreamers. We dream of a future improved by our ideas, and we work hard to make them a reality. This desire to ‘disrupt’ the status quo and impart change is inherent in all entrepreneurs.

However, dreaming without an objective mindset could, more often than not, lead to tunnel vision, and ultimately failure. Even dreams need to be measured up against the cold reality of the market. Sometimes that means the market is not ready for the solution, or the need is not as pronounced as you thought, or the barriers to entry are high, etc… Did you know that the number 1 reason startups fail is because there is no real market need for the solution?

I had to learn to be more objective from the get-go, and that was hard work. It is hard not to force your biases on reality, especially with a tech idea close to your heart. But one has to let go of that attachment to be able to step back and validate the idea objectively. This is why I strongly believe in validation first. The more time spent developing a solution prior to validation, the more attachment one has to it, and the less objective one is.

Long Product Development Cycle

Many deep-tech solutions originate in academic institutions, where time is abundant, labor is cheap (Masters and PhD students), and infrastructure is readily available. Thus, spin-off startups from academic institutions do not spend the bulk of their time on product development, since most of the research has already been done.

We on the other hand, wanted to validate first, which meant that the entire product development cycle was going to take place within the company. This created a challenge for us, since unlike academia we did not have the luxury of time, money or research infrastructure. We had some initial ideas on how to develop the product (given our technical training), we got a few grants, hired our first employee, met weekly to discuss progress, and outsourced experiments when possible in order to make it happen. We managed to develop a proof of concept after 2 years of hard work and limited resources, which was no small feat.

The Right Team

I cannot emphasize enough the importance of the team, especially in the early days of a startup. I am not talking here about the co-founders, since that is a separate topic, but rather the team assembled around the co-founders. We learned quite early on that in addition to technical expertise, it is imperative that our employees have the right attitude. Here are three characteristics that we have found to be very important in addition to technical skills:

1. Willingness to face challenges and to problem solve creatively. It is normal for us to feel discouraged when faced with difficulties, and believe me there are lots of those in the startup world, but it is dangerous if we remain in a state of discouragement. Therefore, employees working in deep-tech startups need to understand that there will be more challenges, and they will need to be driven towards finding creative solutions, and not to get stuck in a state of discouragement.

#lessons-learned #deep learning

How This Startup Is Building A Language Understanding Engine

According to Research and Markets reports, Artificial Intelligence for speech recognition market in India is anticipated to expand at a compound annual growth rate (CAGR) of ~65.17% during the forecast period (2019-2024) and is expected to reach a value of INR 14.61 Bn by 2024.

The increasing demand for smart speakers and voice-enabled devices, coupled with rising penetration of speech recognition technology in customer care services are driving this growth, further stimulating development and innovation in the space.

#startups #ai startup india #chatbots #indian nlp startup #language understanding #speech recognition #startup india #vernacular.ai #voice automation

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We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

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