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AI Trading Platform Tradl Raises $4.1 Million Seed Round from Nexus, Stellaris to Bring Quant Tools to Retail Traders

Bengaluru-based Tradl has raised $4.1 million (about ₹40 crore) in seed funding from Nexus Venture Partners and Stellaris Venture Partners to scale its AI-plus-quant market intelligence platform for India's retail traders.

NSE Exchange Plaza in Mumbai — Tradl lists NSE, BSE and MCX as exchange partners

Bengaluru-based Tradl, a start-up building AI-led market intelligence tools for retail traders, has raised $4.1 million (about ₹40 crore) in a seed round from Nexus Venture Partners and Stellaris Venture Partners, with a few angel investors also participating. The fundraise, first reported by Inc42 on 26 October 2026, is reflected in the company’s filings with the Ministry of Corporate Affairs. Tradl and its investors have not yet commented publicly on the round.

The deal is another sign that investors are looking for the next layer of value in India’s retail investing market. In the past few years, competition among brokers has focused on making accounts easier to open and trades cheaper. Tradl wants to compete on the quality of research and analysis available to individual traders.

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Institutional-grade analytics for retail traders

Shubham Bansal and Nilesh Raj founded Tradl in 2025. Both are IIT Guwahati graduates. Bansal spent about five years at Goldman Sachs, and Raj previously worked at the fintech start-up Stable Money.

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The platform pairs large language models with a quantitative engine. Users describe what they are looking for in plain language. The AI turns that request into instructions for the quant engine, which applies fixed rules to market data to produce the result. Tradl says the design means the same query on the same data will always return the same answer. The company presents this as a safeguard against the inconsistent, “hallucinated” outputs that general-purpose chatbots can produce when asked about numbers.

Its product suite includes an AI stock screener, AI-assisted technical charts, and backtesting tools that test trading strategies against historical market data. It also offers sector-rotation tracking and a calendar of corporate events that can move share prices. Features such as chart-pattern alerts, broader market insights and personalised option chains are currently listed as available through private access. Tradl names the NSE, BSE and MCX as exchange partners on its website and CMOTS as its data partner.

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Betting on a “fourth wave” of broking

On its website, Tradl describes Indian broking as moving through four waves: from physical to digital trading, then discount broking and mass participation, then near-instant demat onboarding enabled by Aadhaar, PAN and UPI, and now AI. The founders describe their roadmap in three phases: assistance, automation and, eventually, agentic trading. In the final phase, traders would supervise software agents rather than carry out every trade themselves.

Co-founder Nilesh Raj has said on the company’s site that the aim is for “a trader with ₹1 lakh” to access the same depth of analysis as a “₹100 crore desk”.

Funding momentum in AI-led investing tools

The round comes as investors back AI-native products for retail market participants. According to Inc42 data, AI start-ups attracted $438 million across 35 deals in Q3 2026, up 265% year-on-year, making AI the most-funded sector of the quarter. Trackk, an investment and broking platform, raised a $3.7 million seed round led by Lightspeed earlier this year. Investment advisory platform StockGro recently raised ₹110 crore from existing backers as part of its ongoing Series B.

Why it matters for India’s fintech ecosystem

For the market, the growth of AI-led research tools brings both opportunity and responsibility. Better analytics could help self-directed investors make more disciplined, data-backed decisions. As these tools move from analysis towards automation, however, transparency, explainability and investor protection will come under closer scrutiny from both users and regulators. Tradl’s choice to keep numerical outputs on a deterministic quant layer, rather than leaving them to a language model alone, is one way the industry is trying to address those concerns. With fresh capital from two prominent early-stage investors, Tradl now needs to show that retail traders will adopt AI-led research at scale.

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