Draft:Kalpi.ai

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Kalpi

Type	Private
Industry	Financial technology
Founded	February 2025 (2025-02)
Founder	Ashwar Gupta
Area served	India
Key people	Ashwar Gupta (CEO)
Products	Kalpi.ai, KalpiQuant.com, Kalpi Capital
Number of employees	7 (May 2026)
Website	kalpi.ai

Kalpi is an Indian financial technology company that develops and operates a rule-based systematic investing platform for retail and institutional investors. Founded in February 2025 by Ashwar Gupta, the company is headquartered in Hyderabad, Telangana, India, with its registered office in Raipur, Chhattisgarh. The platform enables users to build, backtest, automate, and execute quantitative investment strategies across equities, exchange-traded funds (ETFs), and mutual funds without requiring any programming knowledge.

Kalpi operates two separate products targeting different segments of the investment market. Kalpi.ai is designed for retail investors, while KalpiQuant.com serves institutional clients such as portfolio management service (PMS) firms, alternative investment funds (AIFs), registered investment advisers (RIAs), stockbrokers, and family offices. In May 2026, the company raised ₹3.75 crore (US$390,000) (approximately US$450,000) in seed funding from Rainmatter Capital, the investment arm of Indian stockbroking firm Zerodha.

In May 2026, Inc42 featured Kalpi as one of five fintech startups to watch in India.

History

Founder's background and early career

Ashwar Gupta, the founder and CEO of Kalpi, was born and raised in Bhopal, Madhya Pradesh, India, where he completed his schooling at St. Joseph's Co-ed School. After completing his school education in Bhopal, Ashwar enrolled at the Birla Institute of Technology and Science, Pilani (BITS Pilani), one of India's premier engineering institutions. He completed a four-year Bachelor of Engineering (Honours) degree in Engineering from BITS Pilani between 2015 and 2019. During his time at BITS Pilani, Ashwar served as a coordinator for RadioAktiv, the campus radio station, during the 2017-2018 academic year. At BITS Pilani, Ashwar was also involved with the student union and worked on various technical projects, developing skills in Apache Spark, big data technologies, and computational frameworks.

After graduating from BITS Pilani in 2019, Ashwar joined Zeotap, a Berlin-based enterprise customer data platform, as a machine learning engineer. Over the course of nearly five years at Zeotap, Ashwar rose to the position of Senior Machine Learning Engineer, where he worked on enterprise-scale data infrastructure, machine learning pipelines, and customer data analytics. While his professional career at Zeotap was focused on technology, Ashwar developed an increasingly strong personal interest in investing and financial markets during this period.

Determined to understand finance at a deeper level, Ashwar pursued the Chartered Financial Analyst (CFA) Program administered by the CFA Institute. He cleared all three levels of the CFA examination on his first attempt, a feat that distinguishes a small minority of CFA candidates worldwide. The combination of his engineering training at BITS Pilani, his professional experience in machine learning and data engineering at Zeotap, and his financial education through the CFA Program would later form the intellectual foundation of Kalpi.

Following his tenure at Zeotap, Ashwar transitioned into quantitative finance, working as a quantitative researcher at PharVision Capital, where he gained experience in systematic and quantitative investment strategies.

Origin story

The idea for Kalpi emerged from Ashwar's direct experience at a United States-based hedge fund, where he worked as a quantitative researcher. During his time at the hedge fund, Ashwar witnessed first-hand how some of the world's most sophisticated investors made decisions. Every investment was backed by structured research, high-quality financial data, and carefully designed systematic processes, rather than emotions, market rumours, or social media commentary.

However, every time Ashwar spoke to friends and family back home in India about investing, he observed a starkly different reality. Many Indian retail investors were relying on WhatsApp forwards, Telegram groups, YouTube influencers, and gut instinct to decide where to invest their money. Ashwar recognised that the gap between institutional investors and everyday retail investors was not simply about capital. It was fundamentally about access to the right tools, structured workflows, and disciplined decision-making frameworks.

According to Ashwar, the core problem facing Indian retail investors was not a shortage of information, since financial news, market opinions, and stock recommendations were already available in abundance through digital channels. The real challenge, as Ashwar has explained in interviews, was helping investors convert that information into disciplined, rules-based investment decisions rather than acting on tips, emotions, or speculation.

This realisation eventually led Ashwar to leave his career in institutional quantitative finance and return to India to build Kalpi, a fintech startup with the stated mission of making institutional-grade investing tools accessible to every Indian investor.

Founding and early development

Kalpi was formally founded in February 2025 and incorporated with its registered office in Raipur, Chhattisgarh, while establishing its operational headquarters in Hyderabad, Telangana. Ashwar served as the sole founder, taking on the role of Founder and Chief Executive Officer.

During the early development phase, the company focused on building the core technology infrastructure, including the data pipelines, backtesting engine, portfolio construction algorithms, and broker integration layer that would underpin both the retail and institutional products.

Seed funding from Rainmatter Capital

In May 2026, Kalpi raised ₹3.75 crore (US$390,000) (approximately US$450,000) in a seed round led by Rainmatter Capital, the fintech-focused investment arm of Zerodha, India's largest stockbroking firm by number of active clients. Rainmatter Capital, co-founded by Nithin Kamath, is known for backing early-stage fintech and capital markets startups in India, with previous investments in companies such as Smallcase and Streak.

Faisal Mohammed, Vice President of Trading Operations at Zerodha, commented on the investment, noting that good systematic investing tools in India had largely remained locked inside large institutions, and that Ashwar was building something practical for investors who wanted to move past tips and hunches to a rule-based approach.

The company stated that the funding would be used to strengthen its engineering and artificial intelligence teams, acquire high-quality financial datasets, improve product capabilities, and expand distribution across both retail and institutional investor segments.

As of May 2026, Kalpi had seven employees. The company's cap table showed founders holding 76.51 per cent of the company, with funds holding 15 per cent and an employee stock ownership plan (ESOP) pool comprising 8.49 per cent.

Industry recognition

In May 2026, Inc42, a prominent Indian technology media publication, featured Kalpi as one of five fintech startups to watch in India, alongside Neoble, Frex, Spense, and iCReDS. The Inc42 feature highlighted Kalpi's no-code approach to rule-based investing, noting that the platform allows users or fund managers to define a systematic strategy (for example, selecting the top 20 stocks by revenue growth and low debt-to-equity ratio with monthly rebalancing), then test that strategy across years of historical market data before deploying it live.

The funding round was also covered by multiple Indian business and technology publications including Outlook Business, Entrackr, SiliconIndia, Analytics Insight, CXO Digitalpulse, Indian Startup Times, Indian Startup News, and The Head and Tale.

In July 2026, Indian Startup Times published a detailed interview profile of Ashwar, documenting his journey from BITS Pilani through his career in machine learning and quantitative finance to the founding of Kalpi.

Products and services

Kalpi operates a suite of financial technology products organised around the concept of systematic investing, an approach to portfolio management in which investment decisions are governed by predefined rules, quantitative criteria, and data analysis rather than discretionary human judgement. The company's product suite is divided into two primary platforms (Kalpi.ai for retail users and KalpiQuant.com for institutional users), supplemented by a research and analytics interface branded as Kalpi Capital.

Kalpi.ai (retail platform)

Kalpi.ai is the company's consumer-facing platform, designed for individual retail investors who wish to adopt systematic, data-driven investing approaches without writing code or requiring advanced financial modelling expertise.

Algo Portfolio Builder

The Algo Portfolio Builder (also referred to as the Basket Builder) is the platform's flagship tool. It enables users to construct rule-based investment portfolios through a four-step no-code workflow. The four steps involve:

1. Stock Screening and Universe Definition - Users define the criteria for selecting stocks from the broader market universe, using filters based on fundamental metrics (such as revenue growth rate, price-to-earnings ratio, debt-to-equity ratio, return on equity, earnings per share growth, market capitalisation, and dividend yield), technical indicators, or quantitative factor scores.
2. Allocation and Weighting Strategy - Users choose how to allocate capital across the selected stocks. Options include equal weighting, market-capitalisation weighting, or custom weighting schemes based on user-defined parameters.
3. Rebalancing Schedule - Users configure the frequency at which the portfolio is reviewed and rebalanced, such as monthly, quarterly, semi-annually, or annually, to ensure continued alignment with the defined screening rules.
4. Backtesting and Deployment - The completed strategy is tested against historical market data to evaluate hypothetical past performance, risk metrics, drawdowns, and returns before the user decides to deploy it with real capital through a connected brokerage account.

For users who prefer not to build strategies from scratch, Kalpi.ai offers a library of pre-built strategy templates (called Ready Made Baskets) that cover common investment approaches and can be used directly or customised further. Users can also create static baskets, which are manually curated collections of stocks without automated screening rules.

The Free tier of the platform permits users to create up to five algorithmic portfolios, while the Pro tier allows unlimited portfolios.

Portfolio Analyser

The Portfolio Analyser allows users to import their existing investment holdings by connecting a brokerage account. Once connected, the tool generates a comprehensive portfolio health report that evaluates the portfolio across multiple dimensions. These include:

• Diversification analysis - Assessment of how well the portfolio is spread across sectors, market capitalisations, and asset types, identifying areas of over-concentration.
• Risk metrics - Calculation of portfolio-level risk statistics including volatility, maximum drawdown, beta exposure, and value at risk.
• Factor exposure analysis - Breakdown of the portfolio's exposure to recognised investment factors such as momentum, value, quality, size, and low volatility.
• Concentration risk - Identification of excessive concentration in individual stocks, sectors, or themes that could expose the portfolio to idiosyncratic risk.
• Performance attribution - Analysis of which holdings and sectors contributed positively or negatively to overall portfolio returns.

Portfolio health reports are presented in plain language to make the analysis accessible to investors without advanced financial training.

Stock Research Module

The Stock Research module provides a comprehensive research interface for analysing individual stocks, ETFs, indices, and sectors. The research capabilities include:

• Fundamental analysis - Financial statement data including balance sheets, income statements, and cash flow statements, along with key financial ratios, valuation multiples, and growth metrics.
• Technical analysis - Price charts, technical indicators, candlestick patterns, support and resistance levels, and trend analysis.
• Quantitative signals - Proprietary quant scores covering rolling returns, volatility, Sharpe ratio, and factor-based scoring.
• Seasonality analysis - Historical analysis of stock performance across different months and quarters to identify seasonal patterns and trends.

Market Intelligence Tools

Beyond individual stock and portfolio analysis, Kalpi.ai provides a suite of market-wide research and intelligence tools:

• Index Tracking - Real-time tracking and analysis of major Indian market indices including the Sensex, Nifty 50, and sectoral indices, with performance breakdowns and constituent analysis.
• Sector Analysis - Comparative analysis of market sectors, enabling users to identify sector rotation patterns and relative performance across different market regimes.
• Relative Rotation Graph (RRG) Charts - Interactive RRG visualisations that plot relative strength and momentum of sectors, stocks, or indices against a benchmark, helping users identify which sectors are leading, lagging, improving, or weakening in the current market cycle.
• Initial Public Offering (IPO) Tracker - A tracking tool for upcoming, ongoing, and recently listed IPOs in the Indian market, providing details on issue size, pricing, subscription status, and listing performance.
• Foreign Institutional Investor (FII) and Domestic Institutional Investor (DII) Flow Monitoring - Daily and historical tracking of net buying and selling activity by foreign and domestic institutional investors in Indian equities, providing insights into institutional money flows that influence market direction.
• Market Events Calendar - A calendar of upcoming market events including earnings announcements, dividend dates, stock splits, board meetings, and regulatory developments that may impact stock prices.

Broker Integration and Trade Execution

Kalpi.ai integrates with Indian stockbroking platforms, enabling users to connect their existing brokerage accounts and execute trades directly from within the Kalpi interface. Key execution features include:

• One-click portfolio investment - Users can invest in an entire basket of stocks with a single action, rather than placing individual orders for each security.
• Portfolio rebalancing execution - When a rule-based portfolio signals that rebalancing is due (based on the user's configured schedule), the platform facilitates the necessary buy and sell orders through the connected broker.
• Direct broker connection - Integration with brokerage application programming interfaces (APIs) to enable seamless order routing and execution without requiring users to switch between platforms.

Alerts and Notifications System

The platform provides a configurable alerts engine that allows users to set up intelligent notifications across multiple dimensions:

• Price alerts - Notifications triggered when a stock reaches a user-defined price level or percentage change threshold.
• Technical indicator alerts - Notifications based on technical analysis signals such as moving average crossovers, relative strength index (RSI) levels, or MACD signals.
• Fundamental change alerts - Notifications triggered by changes in a company's fundamental data, such as earnings releases, changes in financial ratios, or corporate actions.
• Custom condition alerts - User-defined alert conditions combining multiple parameters.

The Free tier permits a maximum of three simultaneous alerts, while the Pro tier offers unlimited alerts.

Stock Screener

The platform includes a stock screening tool that allows users to filter the universe of Indian listed stocks based on customisable criteria including fundamental ratios, technical indicators, market capitalisation ranges, sector classifications, and quantitative factor scores. The Pro tier provides access to unlimited stock screens, while the Free tier has limitations on screening capabilities.

KalpiQuant.com (institutional platform)

KalpiQuant.com is the company's institutional product, marketed under the tagline "The operating system for systematic investing" and designed to provide infrastructure for professional investment managers. The platform is targeted at PMS firms, AIFs, RIAs, brokers, and family offices that require advanced quantitative research and portfolio construction capabilities beyond what the retail platform offers.

The institutional platform offers the following capabilities:

• Institutional Backtesting Engine - A high-fidelity backtesting system that allows fund managers to simulate investment strategies across extended historical time periods, evaluating performance, risk-adjusted returns, drawdowns, turnover, and other statistical metrics before deploying strategies with client capital. The institutional backtesting engine is designed to handle more complex strategy logic and larger data volumes than the retail backtesting tool.
• Factor Library with 300+ Pre-Computed Factors - Access to a library of over 300 pre-computed investment factors spanning categories including value, growth, quality, momentum, size, volatility, profitability, leverage, and liquidity. This allows institutional users to construct and evaluate multi-factor investment strategies without building their own factor computation infrastructure.
• Portfolio Optimisation - Mathematical optimisation tools for constructing portfolios according to specified constraints, risk budgets, return objectives, and regulatory requirements. These tools help institutional managers balance expected returns against risk within defined parameters.
• Risk Attribution - Analytical tools for decomposing portfolio risk across multiple dimensions, including factor-level risk contribution, sector-level risk allocation, and individual security risk analysis. This allows institutional investors to understand the sources of risk in their portfolios and make informed risk management decisions.
• Factor Analysis - Capabilities for analysing the exposure of existing or hypothetical portfolios to specific investment factors such as momentum, value, quality, size, and volatility. This helps institutional managers understand whether their portfolios are tilted towards particular factor exposures, whether intentionally or inadvertently.
• Tactical Overlay and Rules-Based Mandates - Tools for implementing tactical overlays on top of core portfolios, and for managing rules-based investment mandates that automatically adjust portfolio composition based on predefined triggers and market conditions.

According to the company, building comparable quantitative infrastructure in-house typically costs institutional investors between Rs 1 crore and Rs 2 crore (approximately US$120,000 to US$240,000). KalpiQuant aims to provide equivalent functionality at a significantly lower cost through a software as a service (SaaS) delivery model, effectively functioning as plug-and-play access to hedge-fund-grade tools.

Artificial intelligence and machine learning capabilities

Kalpi incorporates artificial intelligence (AI) and machine learning (ML) across its product suite. According to Ashwar, the AI systems within Kalpi operate on verified financial datasets and analytical models, rather than generating generic responses from general-purpose large language models.

The platform employs specialised AI agents that perform distinct tasks within the investment workflow. These include:

• Stock Research Agent - An AI agent that analyses company fundamentals, valuation metrics, historical performance, and multiple proprietary indicators to provide comprehensive research insights on individual securities.
• Portfolio Analysis Agent - An AI agent that evaluates portfolio composition, diversification, risk characteristics, and factor exposures, generating actionable recommendations for portfolio improvement.
• Risk Evaluation Agent - An AI agent focused on identifying and quantifying risk factors within investment portfolios and individual positions.
• Agentic Conversational Assistant - An AI-powered conversational interface (described by the company as an "agentic assistant") that provides users with on-demand portfolio analysis, investment research, and strategy insights through natural-language queries.
• ML-Backed Strategy Insights - Machine learning models that analyse market patterns, identify potential investment opportunities across sectors and individual stocks, and provide predictive insights to supplement traditional quantitative analysis.

Ashwar has emphasised that for a sector where trust matters as much as technology, the transparency and explainability of AI-generated recommendations is Kalpi's most important differentiator. Each AI agent performs a specialised task, making the analytical process more transparent and auditable compared to black-box AI systems.

Pricing structure

Kalpi.ai operates on a freemium model with three tiers:

• Free tier - Available at no cost indefinitely. Includes up to five algorithmic portfolios, one-click portfolio investment, portfolio analyser, basic pre-built stock baskets, seasonality analysis, a maximum of three alerts, limited data access, and stock technical and fundamental research tools.
• Pro tier - Priced at Rs 499 per month (plus 18 per cent GST), with a 20 per cent discount available for annual billing. Includes all Free tier features plus unlimited algorithmic portfolios, unlimited alerts, unlimited stock screens, advanced stock research tools, access to advanced pre-built baskets, data since inception, and access to an exclusive WhatsApp community.
• Enterprise tier - Custom pricing for institutions and organisations. Includes all Pro features plus unlimited strategy builder access, team collaboration tools, role-based access control, private cloud deployment, a 24/7 dedicated account manager, custom onboarding and training, enhanced security, and bespoke analytical solutions.

All plans can be cancelled at any time with no setup fees, and the subscription remains active until the end of the billing cycle.

Technology and infrastructure

Data infrastructure

Kalpi's platform is built on a proprietary data infrastructure designed to aggregate, validate, and serve financial data for both retail and institutional applications. Ashwar has identified high-quality financial data as a critical competitive requirement, noting that financial data in India is often expensive, fragmented across multiple sources, and inconsistent in quality, making data validation one of the company's biggest ongoing priorities.

The data infrastructure supports fundamental data (balance sheets, income statements, cash flow statements), technical data (price and volume data, technical indicators), quantitative signals and factor scores, and market-level data such as index constituents, sector classifications, FII/DII flows, and corporate actions.

The company has allocated a portion of its seed funding specifically to acquiring additional high-quality datasets and strengthening its data engineering capabilities.

Broker integrations

Kalpi integrates with Indian stockbroking platforms through their application programming interfaces (APIs) to enable direct trade execution from within the platform. Users can connect their existing brokerage accounts to both analyse their current holdings and execute new investments without switching between multiple applications.

Industry analysts have noted that the quality of broker integrations, including API reliability, low-latency order routing, and seamless settlement processes, is a critical factor in determining how well a quantitative investing platform translates analytical models into live trades.

Regulatory and compliance framework

As a platform that interacts with users' financial information and facilitates investment activity, Kalpi operates within the regulatory framework established by the Securities and Exchange Board of India (SEBI) and other Indian financial regulators. Ashwar has stated that the company works closely with regulated ecosystem participants and prioritises compliance with evolving financial guidelines.

Industry analysts have identified transparent performance reporting, robust risk disclosures, and prudent execution practices as important requirements for systematic investing platforms seeking to build long-term trust among both institutional clients and retail users.

Business model

Kalpi operates on a dual-segment business model, serving both retail and institutional investors through separate but technologically connected platforms.

The retail segment (Kalpi.ai) uses a freemium model, with a free tier to attract users and a paid Pro subscription for advanced features and deeper data access. Industry analysis has suggested that potential monetisation paths for Kalpi's retail business include subscription revenue, revenue-sharing arrangements with broker partners on retail trade flows, and premium data services.

The institutional segment (KalpiQuant.com) targets enterprise clients through custom-priced plans that include dedicated account management, private cloud deployment, team collaboration features, and bespoke analytical solutions. Industry observers have noted that potential institutional revenue streams could include enterprise subscriptions for analytics, licensing of backtesting engines, and white-label solutions for wealth managers.

Ashwar has described the dual-market approach as mutually reinforcing. Institutional clients push the company to build world-class, robust infrastructure, while retail users constantly challenge the team to make sophisticated investing concepts simple enough for anyone to use. That balance, according to Ashwar, keeps the company grounded and ensures the product evolves in both depth and accessibility simultaneously.

Market context and competition

Indian fintech and retail investing landscape

Kalpi operates within the broader context of rapidly growing retail participation in Indian capital markets. The number of demat accounts in India has surged in recent years, driven by factors including rising financial literacy, simplified digital broker onboarding processes, and increasing demand for technology-enabled investment tools.

The growth in retail participation has created demand for investment platforms that move beyond traditional stock screening and tipping services to offer structured, rule-based approaches to portfolio construction and management. This trend has been described by industry observers as a broader shift towards data-driven and automated investing, particularly among a generation of digitally native investors seeking disciplined portfolio management tools rather than speculative trading advice.

India's investment technology market is projected to grow at a compound annual growth rate (CAGR) of 21 per cent between 2025 and 2030, according to industry estimates cited in media coverage.

Competitive landscape

Kalpi competes in a landscape that includes several categories of financial technology providers:

In the Indian market, Kalpi's direct competitive landscape includes platforms such as Smallcase, which enables thematic basket investing and allows users to invest in curated portfolios of stocks, and Streak, which provides rule-based trading strategy tools, both of which are also backed by Zerodha's Rainmatter Capital ecosystem. The company has also drawn comparisons to traditional stock screeners such as Screener.in, although Kalpi differentiates itself by stating that it covers the full investment lifecycle from stock screening and strategy construction through backtesting, execution, portfolio monitoring, and rebalancing, rather than only the stock filtering stage.

Globally, Kalpi competes with established financial data and investment research platforms. According to Tracxn, the company's top competitors by market position include Seeking Alpha, The Motley Fool, and TipRanks, though these operate primarily in the United States market and focus more on editorial content and investment research rather than systematic portfolio construction tools.

In the institutional segment, Kalpi faces competition from robo-advisors, algorithmic mutual fund platforms, and quantitative portfolio features offered by major brokerage firms and financial data terminal providers.

Industry analysts have noted that competitive differentiation in the quantitative investing platform space depends on factors including data quality and coverage, backtesting engine reliability and fidelity, execution infrastructure and broker integration quality, product user experience and accessibility, transparent performance attribution, and regulatory compliance.

Investor education and community initiatives

In addition to its technology products, Kalpi has invested in investor education initiatives as part of its broader strategy to promote the adoption of systematic investing among Indian retail investors.

The company runs webinars, workshops, and maintains an active investor community, including a dedicated WhatsApp group for Pro subscribers, where users discuss investment strategies, share insights, and learn from one another. Ashwar has stated that technology alone cannot transform investing behaviour, and that building long-term investing habits among users is as important as developing sophisticated software tools. The larger goal, according to Ashwar, is to help investors build enduring habits of disciplined, data-driven investing rather than chasing short-term returns or reacting to market noise.

The Algo Portfolio Builder is positioned not only as a software feature but also as an educational tool, encouraging investors to articulate their investment theses in the form of quantifiable, testable rules and to evaluate those rules against years of historical data before committing real capital.

One customer anecdote highlighted in an interview with Ashwar illustrates the company's intended impact. The investor explained that before using Kalpi, he had to juggle multiple platforms for stock screening, portfolio analysis, spreadsheets, trade execution, and performance tracking. After switching to Kalpi, the entire workflow, from research to portfolio monitoring, could be managed in one place. According to Ashwar, what stood out most was not the convenience, but the customer's remark that for the first time, he felt like he had a system instead of just opinions.

See also

• Quantitative investing
• Algorithmic trading
• Financial technology in India
• Factor investing
• Robo-advisor
• Smallcase
• Zerodha
• Rainmatter Capital

External links

• Official website
• KalpiQuant.com – Institutional Platform
• Kalpi Capital – AI-Powered Systematic Analytics Platform
• Kalpi Documentation

Category:Financial technology companies of India Category:Indian companies established in 2025 Category:Companies based in Hyderabad, India Category:Software companies of India Category:Financial services companies established in 2025 Category:2025 establishments in Telangana