LinqAlpha and Microsoft Partner to Build Secure Financial AI Infrastructure

July 14, 2025 | 10:00
(0) user say
Financial tech meets future-ready cloud — a new alliance is shaping smarter, safer finance in Asia.

NEW YORK, July 14, 2025 /PRNewswire/ -- LinqAlpha, a multi-agent AI investment research platform powering workflows at over 170 financial institutions, including leading investment banks, asset managers, and hedge funds, has been selected to join the 2025 Majung Program, a Microsoft-supported startup initiative co-led by South Korea's Ministry of SMEs and Startups.

Through this program, LinqAlpha will build its platform cloud-natively on Microsoft Azure's private cloud. This will enhance data security, deployment flexibility, and interoperability across enterprise financial environments. The Majung Program provides commercialization funding, Azure cloud credits, technical support, and go-to-market assistance for startups building cloud-native B2B platforms. LinqAlpha's participation reflects its technical maturity and alignment with institutional-scale AI use cases.

LinqAlpha's platform leverages generative AI and multi-agent systems to extract, analyze, and synthesize unstructured financial data, including earnings transcripts, regulatory filings, analyst reports, and macroeconomic commentary—empowering faster and more accurate investment decisions. As part of the engagement, LinqAlpha will integrate Azure-native services, including Azure AI Search and Azure OpenAI Service, enabling secure, in-environment deployments that meet the compliance and performance needs of financial institutions.

Suyeol Yun, Principal AI Engineer, and Gukyoung Jeong, Systems Architect at LinqAlpha, shared:

"Through the program we've strengthened the resilience, security, and scalability of our infrastructure. We're excited to deliver trusted and seamless AI-powered research capabilities to our clients across the financial sector."

Learn more at: linqalpha.com

By PR Newswire

LinqAlpha

What the stars mean:

★ Poor ★ ★ Promising ★★★ Good ★★★★ Very good ★★★★★ Exceptional