TECH

Linq raises $6.6M to use AI to make research easier for financial analysts


When generative AI tools started making waves in late 2022 after the launch of ChatGPT, the finance industry was one of the first to recognize these tools’ potential for speeding up the data-gathering and research process. Speed, after all, is vital when you’re advising investors who need to respond quickly to market changes.

Now a startup called Linq is entering this space with an AI agent that can automate a variety of tasks required for financial analysis and research and has raised $6.6 million in a funding round led by InterVest and Atinum, with participation from TechStars, Kakao Ventures, Smilegate Investment and Yellowdog.

MIT alumni Jacob Chanyeol Choi and Subeen Pang founded Linq after they won the Samsung Open Collaboration in 2023, an accelerator-like program hosted by Samsung Financial Network. Choi told TechCrunch that win spurred him to build large language models (LLMs) for enterprises, particularly the financial sector.

“We knew the potential for a tool that could seamlessly integrate with a company’s data ecosystem, which led to the birth of Linq,” Choi said. “Our approach to embedding and retrieving data involves transforming data into vectors and employing techniques like vector search and retrieval-augmented generation (RAG) to provide generative AI services.”

Boston-based Linq says its AI agent uses domain-specific (finance, in this case) search and large language models to automate everything from scheduling and communication, to scanning research reports and building financial models. It can also summarize securities filings, earnings reports and call transcripts, and other company-specific information.

“Hedge fund analysts [and institutional investors], who need to cover hundreds of equities, will initially experience the most significant productivity boost, starting with earnings call transcript summaries,” Choi said, adding that more general AI tools like ChatGPT cannot fill the gap.

In addition to its B2B service for enterprise clients, the startup also plans to build B2C tools for AI equity research. Choi said these tools will let users synthesize vast amounts of data and will support portfolio managers in making informed investment decisions.

Linq will go up against incumbents that serve the equity research space with their own AI-powered offerings: Bloomberg’s terminals have a generative AI tool that can summarize earnings calls, and S&P has a document viewer that uses AI to surface relevant information from filings, transcripts, and presentations. It will also have to contend with other startups like Fintool, Finchat and Finpilot, which also offer AI-powered platforms for financial researchers and institutional investors.

Choi thinks Linq has an edge, however. What sets his company apart from its competitors is that it offers an end-to-end service to manage workflow and streamline processes, Choi said. He pointed out that its proprietary data-gathering system provides investors access to a broad spectrum of structured and unstructured data from across the world, including live transcriptions of earnings calls in languages other than English in countries outside the United States.

In addition to its tech, the startup has some expertise in the form of its two other founding members: Jin Kim previously worked in quantitative finance, and Hojun Choi is a former Goldman Sachs banker who has also worked at a private equity firm.

The startup already has a few good names on its roster of customers: Choi told TechCrunch that Linq has been working with Samsung Financial Network to automate their underwriting processes, in addition to more than 20 enterprise customers that include Samsung’s affiliates, KPMG US, and hedge funds in Asia and the U.S.

The company, which also has an office in Seoul, South Korea, will use the new capital for product development, hiring staff, and expanding into the Americas, Asia and the Middle East. The two-year-old startup has 12 staff and started generating revenue last October.



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