IIT Madras graduate Raghav Talwar is addressing a significant challenge in the realm of quantitative trading: the protracted duration it takes to test new datasets and strategies. Many small and mid-sized trading firms are hindered by limited resources and sluggish research cycles, making it difficult for them to compete. Talwar’s AI-based solution seeks to expedite strategy development, thereby providing a competitive advantage to firms that may lack extensive technical teams or robust infrastructure.
### A Time-Consuming Process
In 2024, over 65% of quantitative traders indicated that evaluating a single new dataset takes them more than a month. Given that milliseconds can translate into millions in the trading world, this presents a critical issue. Small to mid-sized trading firms, particularly those managing assets below $500 million, often lack the capacity—be it time, skilled personnel, or technological infrastructure—to conduct rapid experimentation. This is where Talwar’s venture, Nexera, comes into play. The startup intends to streamline the process of developing trading strategies by leveraging Generative AI to enhance the speed, scalability, and intuitiveness of financial research.
### Bridging the Gap for Traders
“Every trader has ideas. The challenge is turning those ideas into signals quickly enough to matter,” Talwar remarks. Nexera is designed to tackle this very issue. Founded by Talwar, who graduated from IIT Madras, Nexera AI is creating a seemingly straightforward solution: a platform enabling traders to test their concepts in a much shorter timeframe. Instead of relying on additional dashboards or reports, it introduces an “answer engine”—a financial co-pilot that assists traders in researching, building, and experimenting with strategies in days rather than months. “The ideas aren’t the issue,” he explains. “Traders have tons of them. The real bottleneck is the time it takes to test whether they work.”
### From Intern to Entrepreneur
Raghav Talwar’s journey to becoming an entrepreneur was not premeditated. It began with an internship during his third year at an options trading firm, where he anticipated an exhilarating environment filled with dynamic markets and innovative strategies. Instead, he encountered a cumbersome and chaotic behind-the-scenes process. The task of developing trading models felt more like piecing together spreadsheets than solving intricate financial challenges. This realization prompted him to engage with industry professionals—more than 300 conversations revealed a common trend: traders were devoting more time to managing data than to strategic decision-making. “I started cold-emailing traders to see if they faced the same friction,” he recalls. “And they did. Almost all of them.” By the end of his final semester, Talwar was resolute in his decision. He rejected job offers, forwent placements, and committed to launching a full-time startup.
### Early Challenges and Successes
His determination bore fruit, as he successfully raised $400,000 in pre-seed funding from AngelList and gradCapital, achieving financial independence even before graduating. However, the initial phase was far from glamorous. Raghav traveled to the U.S., sent over a thousand cold emails, and arranged meetings with anyone willing to listen. Five of those firms eventually became Nexera’s inaugural pilot customers. The product he developed, based on their feedback, turned out to be something traders didn’t know they were missing. It allows users to pose questions like “How did oil prices react to OPEC decisions in the past five years?” and receive precise, contextual answers supported by actual data—without the need for coding. Nexera essentially functions as a junior quant analyst available around the clock, delivering results in seconds, utilizing various data sources, and learning from every query. “We’re not trying to replace traders,” says Raghav. “We just want to free them from grunt work so they can think better and move faster.”
### The Implications of Slow Responses
In the trading landscape, the cost of delay can be substantial. A recent survey by Bain & Company revealed that 72% of quantitative teams can evaluate only three new datasets simultaneously, with 32% taking over two months just to assess one. This poses a significant challenge, especially since market inefficiencies—the fleeting insights that drive profitable trades—can dissipate within weeks or even days. Larger hedge funds can afford to employ teams of engineers and develop bespoke tools, while smaller and mid-sized firms—representing a substantial portion of the market—often cannot. These firms frequently find themselves waiting for someone to code a strategy, only to realize that the opportunity has already passed. Recognizing this gap, Raghav designed Nexera to fill it, automating the most time-intensive aspects of the trading process—such as signal construction, model testing, correlation analysis, and backtesting—allowing traders to focus more on strategic thinking.
### Standing Out in a Crowded Market
The financial sector has seen a proliferation of AI technologies, but many offerings tend to prioritize marketing flair over genuine utility. Nexera differentiates itself by applying AI where it truly makes a difference: by simplifying research, automating repetitive tasks, and facilitating the exploration of data-rich inquiries. Traders retain the final decision-making authority, but with enhanced tools to expedite their process. Nexera is not attempting to be an all-encompassing solution; rather, it is dedicated to excelling at a singular function: enabling traders to transition from concept to strategy in record time. In an increasingly competitive environment, tools like Nexera may provide the necessary advantage for smaller firms to remain relevant.
