Leaders' Circle Unveils Insights on Scaling IT Infrastructure for Future-Proofing

Leaders' Circle Unveils Insights on Scaling IT Infrastructure for Future-Proofing

There's never been a better time to start a business in India. The startup ecosystem is robust, regulatory changes have reduced paperwork, and the technology ecosystem is delivering an enormous bang for the buck.

Cloud, AI and ML are creating a new and level playing field for anyone with a strong idea. Startups today can begin with as little as a laptop, an idea, and a cloud subscription, removing entry barriers and flooding the market with great ideas and business, which propels the entire ecosystem forward at breakneck speed.

The third Leaders Circle dove deep into Scaling for Success: Future-Proofing IT Infrastructure. The session brought together Aniruddha Banerjee, Co-Founder, Abee Research Labs (SwitchOn); Amit Kriplani, CTO, Ace Turtle; Ritabrata Bhaumik, CTO, Ftcash; Biplab Sinha, CEO, Tradelab Technologies; Abhish Agarwal, Head of Engineering -Platform, MindTickle Inc; Tarun Ramesh, VP Engineering, Prozo; Prabhjit Singh, Senior VP Engineering, Reward360 Global Services; Saumitra Kumar, Head of Product, Engineering and Data Science, Goat Brand Labs Pvt Ltd with Ipseeta Aruni, Principal Solutions Consultant, Persistent and Debasis Bhattacharya, Head of Customer Engineering – Digital Natives, Google India. The session was moderated by Paromita Chatterjee.

The discussion began with Amit Kriplani talking about how the thinking around 'scale' has shifted from mere efficient inventory management, order management and high fulfilment rates to encompass customer acquisition and customer engagement. This expansion of scope has only been possible because of technology enablers, particularly Cloud tech. Saumitra Kumar too, feels that the focus area for businesses like his have multiplied. There are now two key areas: tech that helps with revenue and growth in the form of a suite of products that drive engagement, and tech that helps with efficiency and cost in the form of tools that help with inventory management, forecasting, dynamic pricing, etc.

Biplab Sinha spoke about the specific shift that has occurred in the wake of Fintech: where earlier people needed to speak to a 'broker' before placing a trade, to customers today who will only call you when your tech stack has let them down in some way. He too feels that there are two competing asks his tech stack has to answer to: ensuring utmost data security and compliance to all regulations, and ensuring maximum speed and accuracy in trades. Ritabrata Bhaumik, whose business involves the dispensation of unsecured loans, feels that it is commendable how fraud prevention technology has managed to stay a step ahead of fraudsters. Especially in his sphere, which, until now has been underserved, he's always been able to rely on his tech stack with confidence.

On the manufacturing side, Aniruddha Banerjee talked about how involving customers in training their AI has been a huge ingredient of their secret sauce in automating quality inspection for large manufacturing plants. Of course, they retain their focus on creating enormous flexibility in their solutions, so that brands can scale up or down dramatically. This involves building infrastructure that scales, and configuration that is largely DIY.

Debasis Bhattacharya talked about the democratisation that AI, ML and Cloud technologies have enabled, by reducing barriers to entry. Today, entrepreneurs are a card swipe away from Cloud infrastructure and can start with a very minimal cost, and scale as they go. Moreover, a lot of the heavy lifting is done by the managed service providers who take on these business processes, allowing entrepreneurs to focus on core business and growth. Entry barriers to AI and ML have also dropped as large cloud providers like Google are moving beyond VMs and uptime SLAs to offloading more and more of the managed infrastructure, so engineers can focus on solving for the business requirements they're working on.

Ipseeta Aruni also drew attention to industry specific solution accelerators, IaaS and PaaS solutions that create enormous flexibility for startups by taking an almost lego-like building blocks approach to building their technology stack. Moreover existing AI and ML models can be applied to specific use cases, creating an approach that begins from the generic and gets increasingly tuned to the specific use case at hand.

Abhish spoke about the importance of code optimisation to enable scale down the line. As startups are often in a rush to go to market initially, this creates challenges down the line as the original code base (which isn't optimised) gets copied in multiple places, and becomes harder to weed out. Prabhjit agreed with this, and pointed out that if applications aren't code optimised for scale, the costs can indeed become astronomical. Tarun agreed with both, calling attention to the problem as it can sneak into builds, and create enormous sticker shock down the line.

As Debasis Bhattacharya pointed out, Google has tools that can help with code optimisation, in addition to teams at Google and Persistent working with businesses to help scale their stack, without a proportional increase in costs. "If your Cloud Bill grows proportionally with your business growth there is a big problem," he said.

The conversation also covered other grounds: reversibility, the role of FinOps and DevOps in scaling well and keeping costs low, price sensitivity for startups, paucity of AI/ML talent, the scrappy nature of startups, starting small, data privacy amongst others. You can watch the entire discussion here. You can also catch up on previous Leader's Circles, and stay tuned to upcoming ones..

Moneycontrol journalists were not involved in the creation of the article.