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    Home»AI»GIM Closes US$20M Series A to Build Agentic AI Systems for Capital Markets
    AI Funding

    GIM Closes US$20M Series A to Build Agentic AI Systems for Capital Markets

    The AI investment technology company has closed its third funding round within a year to develop agentic market testing systems
    Fintech News Hong KongFintech News Hong KongJuly 9, 20262 Mins Read
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    GIM Series A funding
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    Grace Investment Machine (GIM) has secured US$20 million in a Series A funding round to develop agentic AI systems for capital markets.

    The financing marks the investment technology company’s third funding round within its first year of operations.

    A US venture capital firm and Hony Capital co-led the round, with IDG Capital and existing investor Monolith Capital also participating.

    GIM will use the capital to build multi-agent systems that generate, test, and refine investment hypotheses using market data.

    The company aims to move beyond AI models that only assist with research.

    Instead, it is focusing on closed learning loops where intelligent systems improve their reasoning capabilities continuously through measurable feedback in live markets.

    “We believe investment AI is moving from information assistance to autonomous hypothesis generation and testing,”

    said Jiahao Xu, founder and CEO of GIM.

    “GIM is building systems that can reason across market data, evaluate signals through feedback, and improve over time in real-world capital markets.”

    To support this architecture, GIM is developing foundation models tailored to financial environments. Its flagship paper, CogAlpha, was accepted to the ACL 2026 main conference with an Oral recommendation.

    The paper details a seven-layer framework designed to translate raw data into actionable investment signals.

    The company plans to deploy its technology across institutional strategies and, over time, vehicles accessible to individual investors.

    GIM is currently testing its AI-driven strategies in live validation across multiple asset classes and markets.

     

     

    Featured image credit: Edited by Fintech News Hong Kong, based on image by wayhomestudio via Magnific

    Grace Investment Machine (GIM)
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