An Interview with Runik Mehrotra, President, AI Labs


AI Trends recently interviewed Runik Mehrotra, President, AI Labs

  1. Runik, can you tell me a little bit about your personal background and how you got involved with AI Labs? For sure. I started out as a technical guy, working for a large mobile application development firm. While working there, I realized that the market is super inflated and that I could make a lot more money by creating custom applications for clients about 80% cheaper than they were currently being priced. So my business partner and I started NYX Development, a mobile app development firm that worked to undercut the entirety of the mobile application market. After a successful exit, the two of us moved on to founding AI Labs. I was able to utilize a lot of my Mathematics background with AI and we both knew it was about to be a huge market. My current involvement with AI Labs is as the President. I oversee the technical team and development and work with the management team to manage the operations.
  2. Which specific markets are you targeting and why? We are targeting the institutional wealth management market, as this market is currently operating on legacy software solutions with most portfolio management being done by manual quantitative and qualitative analysis. Millions of wealth managers and financial advisors spend hundreds of hours every month creating custom investment solutions for their clients. In reality there is only so much financial data they can crunch, so many news articles they can read and so many portfolios they can create custom to their client. And actually after doing extensive market research, we found out they often slack off and sell generic mutual funds to their clients, giving them a solution that may not be perfect for them. Our goal is to be able to replace research divisions of advisory firms and automate workflows to cut overhead for these firms. FSAI essentially replaces the role of a research analyst and assists an advisory broker by creating a customized portfolio for the client, removing hundreds of hours of research. FSAI is able to analyze every earnings report for the past 10 years, hundreds of news articles, and crunch tens of thousands of numbers to build a portfolio, drastically minimizing the ability to make a mistake.
  3. As you know, there are hundreds, if not thousands of machine learning startups in existence today. I’ve spoken with several investors who are investing in as much as 5-10 machine learning companies each month right now.  What make AI Labs unique, and how do you plan to build a viable business in multiple markets with so much market confusion and competition? There are hundreds of FinTech startups as well.  And although AI Labs is not competing with most of them, there is definitely a lot of market confusion. I have seen many investors say they will only invest in a couple AI startups and some who say they will invest in as many as they can. AI Labs is different because of our approach and the market we are targeting. FSAI is viable because there is no upcoming competition in its target market. FSAI competes with the legacy solutions currently on the market, the existing software applications that brokers and advisors utilize for stock selection and investment management. Our service is driven by artificial intelligence and will be the new benchmark in financial technology. By developing a scalable automated platform, FSAI will disrupt the money management industry by minimizing reliance on human capital thereby allowing wealth managers and financial advisors to reduce operating costs and focus on growing assets and forcing higher returns.
  4. You say that you have a unique proposition in that your platform is a combination of many different aspects of AI. Can you elaborate? There’s a slight misconception right now about AI. Artificial Intelligence isn’t really a technology, it’s more of an umbrella term for powerful machine learning and computational intelligence technologies and algorithms. For some AI startups, a linear regression or a recursive neural network is enough. However, to truly emulate a research analyst, we use almost 20 or 30 of these technologies. In addition to what’s mentioned above, we use almost everything from Statistical Regression and Genetic algorithms (Particle Swarm Optimization) to Means End Analysis, and Natural Language Processing to create an intelligent system that can accurately analyze the market.
  5. Does your management team include professionals who have specific domain experience in the industry’s that you are targeting? Our team has a significant amount of past experience in both Machine Learning/Artificial Intelligence and Financial Services. Every member of our technical core team has been previously a part of FinTech startup or brokerage firm and a renowned individual in data science and AI. They have both the financial and AI knowledge to create a powerful product. Our Management team also consists of professionals in the industry. Our team includes past employees at JP Morgan, Goldman Sachs, and Fidelity, experienced RFA’s,  past executives at publicly traded companies, and previous employees in the advisory brokerage space. The concentrated team is passionate about the problem and has the technical skill to solve it.
  6. Can you explain your platform in terms of architecture and core technologies used? We have positioned ourselves as a stealth AI startup so a lot of what we do is proprietary. Especially in AI and Fintech, powerful and accurate algorithms are intensely sought after. So there’s not much I can explain in terms of architecture. What I can say in terms of our core technology is that we use a powerful machine learning platform with many elements to accurately predict risk and maximize return.
  7. Would you say you are predictive analysis platform, a learning platform or an AI platform? Please elaborate. I think we are a little bit of all three. Finance and risk prediction is a pretty difficult field to quantify and be accurate in. Our core prediction model is machine learning based but can definitely be classified as predictive analysis as well. And like I mentioned earlier, all of this is AI, we are using AI for almost every single calculation FSAI makes. This combination of all three is what makes FSAI so powerful.