Shailendra Abhyankar, Founder & CEO of FinSoftAi, has spent decades building and scaling financial technology systems. He previously served as Managing Director for SunGard/FIS India, led innovation initiatives at BNY Mellon, and headed TCS’s Delivery Centre in Budapest, Hungary. Across roles spanning India, the US, Eastern Europe, and China, he has built and scaled financial platforms globally.
Nitin Menavlikar, Co-founder, brings techno-functional expertise across leading global investment banks, including Deutsche Bank, JPMorgan, and Société Générale, and entrepreneurial experience running a commodity broking business.
Together, they have operated at the intersection of financial infrastructure and market execution. A shared question began to emerge. If access to data has become ubiquitous, why do markets still behave in ways that consistently surprise participants? The answer, they realised, lies in the qualitative narratives that sit behind the data and ultimately drive market moves. That insight became the starting point for FinSoftAi’s SSi.
The Early Signal
The idea did not emerge overnight. Over a decade ago, while working at a Fortune 500 fintech firm, clients had already begun asking for sentiment data. There was growing interest in alternative signals – in understanding how markets were reacting beyond traditional metrics.
“We explored acquisitions, looked at existing solutions, but nothing really translated into actionable insight,” Shailendra recalls.
A small proof of concept built during a Credit Suisse hackathon, combined with conversations with CIOs across global investment banks, made the gap increasingly clear. Sentiment existed, but it lacked context, was often noisy, and did not translate into actionable insight.
From Sentiment to Narrative
For years, sentiment analysis remained limited. It could classify tone, but it struggled to capture context or separate signal from noise.
The arrival of transformer-based models changed that. For the first time, machines could understand language in context, not just process it. But even contextual sentiment wasn’t enough. Markets don’t move on information alone – they move on what is relevant, credible, and capable of shaping perception.
Generative AI pushed this further. It introduced the ability to embed reasoning – to evaluate not just what is being said, but why it matters, how strongly it connects, and whether it can move markets.
Instead of asking whether the market is positive or negative, SSi focuses on a deeper question:
What does the market believe – before it shows up in price?
Building at the Intersection
Shailendra’s journey into building SSi was shaped by a rare combination of depth in both technology and markets. He began in R&D, working on compilers and system software, before moving on to lead large-scale financial platforms across geographies, including India, the US, Eastern Europe, and China.
That experience offered a front-row view into how decisions are actually made. “Data explains what has happened – but it often lags. What drives decisions is how interpretations evolve and when they begin to gain traction,” he says.
After years of leading teams and scaling systems, he felt the pull to return to building. SSi became that convergence, bringing together technology, markets, and a deeper understanding of how narratives influence decisions.
How SSi Works
At its core, SSi is built to track how narratives form, evolve, and influence decisions. The platform processes unstructured data across news and social media, structuring this into signals that reflect shifts in market perception.
A clear example was the“SaaSpocalypse” narrative. Within a short span, sentiment across software companies shifted. Software went from being seen as a productivity enabler to a source of disruption – and markets responded quickly. The underlying fundamentals did not change overnight, but the narrative did, and valuations reset accordingly. “That’s narrative at work,” Shailendra explains. “And that’s what SSi captures.”
Since June 2025, SSi has been used in live trading with real capital, executing over 500 trades. Daily pre-market alerts have shown over 85% directional accuracy across different market conditions. For the team, this is critical. The goal is not backtested performance, but real-world validation.
“Every signal is tested in live markets – and that feedback continuously feeds back into the system, helping us refine the models and decision framework,” Nitin adds.
The Hard Problems
Building SSi was far from straightforward. One of the biggest challenges was extracting meaningful insight from noisy, unstructured data. Social media, in particular, presents a low signal-to-noise problem.
SSi addresses this through a layered approach that evaluates source and author credibility, audience resonance, and most importantly, content relevance to a company, event, or narrative.
On the infrastructure side, the founders went back to first principles. “I had to learn cloud systems deeply, especially AWS, to build a scalable architecture from the ground up,” Shailendra says.
Equally challenging was the market side. When you are building something new, distribution takes time. You are not merely selling a product – you are introducing a new way of thinking.
A New Layer in Investing
Shailendra believes that markets are entering a new phase. Fundamentals explain value. Technicals explain price behaviour. Narratives explain timing and direction.
As data becomes increasingly commoditized, the edge is shifting toward understanding how narratives form, evolve, and gain traction.
SSi is not positioned as a replacement, but as an addition – one that gives investors an edge in understanding how narratives are forming before they show up in price.
What Comes Next
FinSoftAi is now looking beyond equities. The team is expanding SSi across asset classes, including crypto and FX, where narratives evolve faster and often drive sharper price movements.
The opportunity lies in bringing these worlds together. Equities reflect institutional positioning, crypto captures retail sentiment in real time, and FX often embeds macro narratives. Together, they offer a more complete view of how narratives form and propagate.
This cross-asset perspective is central to the next phase of SSi – enabling more holistic insights that go beyond any single market.
The platform is also evolving, with exploration into agentic AI where insights actively support decision workflows.
Narratives are also expanding beyond text. Visual platforms and new data sources are becoming increasingly relevant, and SSi is being built to capture these shifts.
Because across markets, it is narratives that increasingly shape how capital moves.
Original Article
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