Linqalpha co-CEO Jacob Chanyeol Choi
LinqAlpha, a New York-headquartered AI startup building intelligence tools for institutional investors, has raised US$22 million in a Series A round backed by a group of global and Asian investors.
The round was anchored by AVP, Atinum Investment, and GFT Ventures. It also drew participation from financial institutions and venture platforms across Singapore, Hong Kong, South Korea, Japan, and India, including SBI Investment, Z Venture Capital, Betatron Venture Group, East Ventures, SV Investment, Samsung Securities, Mirae Asset Venture Investment, Mirae Asset Capital, NH Investment & Securities, Shinhan Venture Investment, Hana Ventures, and NuVentures.
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The company said the capital will be used to expand its global team, deepen integrations with market and alternative datasets, and accelerate deployment of its multi-agent platform across equities, macro, credit, and multi-asset strategies.
Founded by Jacob Choi, Subeen Pang, Jin Kim, and Hojun Choi, LinqAlpha brings together former Goldman Sachs analysts and MIT computer science PhDs. The startup says its platform is already used by more than 70 financial institutions across the US, Europe, and Asia, including sell-side sales, trading, and research teams at investment banks, as well as buy-side firms such as Causeway Capital Management and Schonfeld Strategic Advisors. Its buy-side clients collectively manage more than US$5 trillion in assets.
From search to synthesis
LinqAlpha is entering a market where institutional investors are drowning in data but still struggling to convert information into judgment quickly enough.
For public-market investors, the problem is no longer access. Earnings calls, regulatory filings, broker notes, central-bank commentary, shipping data, satellite imagery, social-media chatter, supply-chain signals, and credit-market movements are all available in some form. The harder task is linking these signals before they become obvious to everyone else.
That challenge is particularly acute in Asia, where market-moving events can cut across geographies within hours. A semiconductor supply-chain disruption in Taiwan or South Korea, a policy decision in China, a currency move in Japan, or a change in commodity demand from Southeast Asia can quickly influence equities, credit, macro trades, and sector views globally.
“The first wave of AI in finance made analysts faster. The next wave changes what they can know,” said Hojun Choi, co-founder and co-CEO of LinqAlpha. “The edge no longer comes from retrieving information; it comes from systems that surface market-moving signals before they are priced in.”
LinqAlpha’s pitch is that generic AI assistants are not enough for professional investors. Instead, the company allows institutional teams to deploy specialised AI agents that learn their investment frameworks, thesis history, research preferences, and feedback loops. These agents are designed to synthesise internal research with external market data, rather than simply retrieve documents or summarise information.
Jacob Choi, co-founder and co-CEO of LinqAlpha, described the product as “a second brain for every investment team”, built to turn accumulated research into actionable insight across liquid public markets.
Why Asian capital is paying attention
The investor syndicate is notable for its Asian depth. Beyond the lead investors, the round includes names from Japan, Korea, India, Hong Kong, and Southeast Asia. East Ventures, one of Southeast Asia’s most active early-stage investors, and Betatron Venture Group, a Hong Kong-based venture platform, are among the backers. SV Investment also brings links across Korea and Southeast Asia.
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This matters because Asia is both a market opportunity and a stress test for products such as LinqAlpha. The region has complex cross-border capital flows, a fragmented data environment, multilingual markets, and a growing base of institutional investors that need to track global and regional signals simultaneously.
Singapore, in particular, has become a key hub for asset managers, hedge funds, family offices, and private banks. According to the Monetary Authority of Singapore, assets under management in the city-state stood at roughly US$4 trillion in 2023, making it one of Asia’s most important capital-allocation centres. The government has also been pushing the use of AI and data in financial services through regulatory sandboxes, AI governance frameworks, and industry collaborations.
Across Southeast Asia, the broader financial-services sector is also becoming more data-heavy. Digital banks, brokerages, wealth platforms, and institutional trading desks are generating and consuming more real-time information. While much of the region’s fintech investment over the past decade went into payments, lending, and consumer finance, AI infrastructure for financial institutions is now becoming a more serious category.
That shift is being driven by two forces: pressure on financial firms to improve productivity, and the need for differentiated insight in markets where information is increasingly commoditised.
A crowded race in financial AI
LinqAlpha is not alone in chasing this opportunity. The financial information market is already dominated by incumbents such as Bloomberg, LSEG, FactSet, S&P Global, and Moody’s, all of which are embedding generative AI into terminals, data products, and research workflows.
Bloomberg has developed finance-specific AI models and has been adding AI-powered search and summarisation to its ecosystem. FactSet and LSEG are also integrating large language models into analytics and workstation products. S&P Global has Kensho, which applies AI to financial and business data.
On the startup side, companies such as AlphaSense, Hebbia, Rogo, Dataminr, and RavenPack are competing across market intelligence, document search, alternative data, and financial research automation. AlphaSense, for example, has grown into a major player in AI-powered market intelligence by aggregating broker research, transcripts, filings, and expert-call content. Hebbia targets financial and legal professionals with AI agents for complex document analysis. Dataminr and RavenPack focus on real-time event detection and alternative data signals.
The competitive question for LinqAlpha is whether it can move beyond summarisation and search into persistent, investor-specific reasoning. Many AI tools can now condense a transcript or retrieve a filing. Fewer can adapt to how a portfolio manager or analyst builds conviction, tracks prior theses, and weighs contradictory market signals over time.
Manish Agarwal, General Partner at AVP, said LinqAlpha is targeting “a larger opportunity” than retrieval or automation: helping institutional investors discover differentiated insights in markets that reward speed, context, and proprietary judgment.
Execution will decide the outcome
For all the excitement around AI agents, the bar in institutional finance is high. Tools must be accurate, auditable, secure, and deeply integrated into existing workflows. Hallucinations, weak sourcing, or poor data lineage can be costly in investment decision-making. Financial institutions also tend to move slowly when adopting new systems, especially those touching proprietary research and trading processes.
LinqAlpha’s early traction with banks and buy-side firms gives it a stronger starting point than many AI startups selling into finance. But scaling across regions and asset classes will require more than model quality. The company will need robust data partnerships, enterprise-grade controls, and enough customisation to serve different investment styles without becoming a services-heavy business.
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The new funding gives LinqAlpha room to build that infrastructure. It also reflects a broader conviction among Asian investors that the next generation of financial technology will not simply digitise transactions, but reshape how capital-market professionals think, research, and act.
For Southeast Asia, where Singapore is increasingly competing as a global asset-management and AI hub, LinqAlpha’s round is another sign that institutional finance AI is becoming a serious investment theme. The winners will be those that can turn fragmented market noise into decision-ready intelligence before the rest of the market catches up.
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