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UniDataAgent automates enterprise reporting with 95% accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed UniDataAgent, an ontology-grounded system designed to automate question-to-report generation for enterprises. This system significantly reduces the time required for ontology construction, from about a week to a few hours, and report generation from several days to minutes. UniDataAgent achieved 95.0% accuracy on real business questions, outperforming document retrieval-augmented generation (RAG) which scored 72.5%, particularly on structured and compositional tasks. The system has already been deployed by China Unicom, demonstrating cost savings and potential for broader enterprise replication. AI

IMPACT Automates complex enterprise reporting tasks, potentially saving significant time and resources.

RANK_REASON Academic paper detailing a new system for enterprise question-to-report automation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

UniDataAgent automates enterprise reporting with 95% accuracy

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Academic paper detailing a new system for enterprise question-to-report automation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yutai Duan, Yahui Zhao, Zhangti Li, Yu Ma, Zhenfeng Qi, Shaoyang Yuan, Jing Fan, Jie Liu ·

    UniDataAgent: An Ontology-Grounded Agent for Enterprise Question-to-Report Automation

    arXiv:2609.27257v2 Announce Type: replace Abstract: Enterprise data agents must preserve organization specific semantics, not just translate questions into queries. We present ChinaUnicom DataAgent (UniDataAgent), an ontology grounded system for reusable question-to-report analys…

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