<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Enterprise AI | Yiqiao Jin CS PhD @ Georgia Tech</title><link>https://ahren09.github.io/tags/enterprise-ai/</link><atom:link href="https://ahren09.github.io/tags/enterprise-ai/index.xml" rel="self" type="application/rss+xml"/><description>Enterprise AI</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Wed, 01 Jul 2026 00:00:00 +0000</lastBuildDate><image><url>https://ahren09.github.io/media/icon_hu_eee6347cbdb2cc3f.png</url><title>Enterprise AI</title><link>https://ahren09.github.io/tags/enterprise-ai/</link></image><item><title>MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&amp;A</title><link>https://ahren09.github.io/publication/acl26_mm_bizrag/</link><pubDate>Wed, 01 Jul 2026 00:00:00 +0000</pubDate><guid>https://ahren09.github.io/publication/acl26_mm_bizrag/</guid><description>&lt;h2 id="abstract">Abstract&lt;/h2>
&lt;p>Enterprise question answering frequently spans heterogeneous modalities — text, tables, charts, scanned documents, and structured databases. We introduce MM-BizRAG, a multimodal retrieval-augmented generation framework designed for general purpose enterprise Q&amp;amp;A.&lt;/p></description></item></channel></rss>