RAG-Driven Data Quality Governance for Enterprise ERP Systems

• URL: http://arxiv.org/abs/2511.16700v1
• Date: Tue, 18 Nov 2025 12:08:44 GMT
• Title: RAG-Driven Data Quality Governance for Enterprise ERP Systems
• Abstract summary: We present an end-to-end pipeline combining automated data cleaning with LLM-driven query generation.<n>The system is deployed on a production system managing 240,000 employee records over six months.<n>This modular architecture provides a reproducible framework for AI-native enterprise data governance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Enterprise ERP systems managing hundreds of thousands of employee records face critical data quality challenges when human resources departments perform decentralized manual entry across multiple languages. We present an end-to-end pipeline combining automated data cleaning with LLM-driven SQL query generation, deployed on a production system managing 240,000 employee records over six months. The system operates in two integrated stages: a multi-stage cleaning pipeline that performs translation normalization, spelling correction, and entity deduplication during periodic synchronization from Microsoft SQL Server to PostgreSQL; and a retrieval-augmented generation framework powered by GPT-4o that translates natural-language questions in Turkish, Russian, and English into validated SQL queries. The query engine employs LangChain orchestration, FAISS vector similarity search, and few-shot learning with 500+ validated examples. Our evaluation demonstrates 92.5% query validity, 95.1% schema compliance, and 90.7\% semantic accuracy on 2,847 production queries. The system reduces query turnaround time from 2.3 days to under 5 seconds while maintaining 99.2% uptime, with GPT-4o achieving 46% lower latency and 68% cost reduction versus GPT-3.5. This modular architecture provides a reproducible framework for AI-native enterprise data governance, demonstrating real-world viability at enterprise scale with 4.3/5.0 user satisfaction.

Related papers

• Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows [54.49251107006261]
  We introduce Spider 2.0-AIFunc, a benchmark of 465 verified instances across 125 real-world databases covering six types of AI functions on the Snowflake platform.<n>We find that the strongest proprietary models reach 67-70% execution accuracy while the best open-source model achieves 58.1%.
  arXiv  Detail & Related papers  (2026-07-07T12:54:08Z)
• When RAG Meets Query Planning: Logical Query Trees for Resolving Exploratory Reasoning Problems [56.24850338895714]
  RAG struggles with textbfexploratory reasoning problems (ERPs) that are the complex queries involving high uncertainty and ambiguity.<n>We introduce emphPlanRAG, an RAG framework that modelsERPs of natural language as textbflogical query trees (LQTs).<n>PlanRAG outperforms state-of-the-art iteration-based and graph-based RAG systems on our newly constructed dataset, textbfWikiWeb-ERP.
  arXiv  Detail & Related papers  (2026-07-01T06:43:55Z)
• Conversational Query Engine for Mixed-Modality Heterogeneous Enterprise Data Sources [0.816316992802876]
  Existing AI-enabled interfaces force users to select the right tool.<n>We present COGNI, a production conversational BI system that treats natural-language analytics as a heterogeneous query processing problem.
  arXiv  Detail & Related papers  (2026-06-15T17:02:22Z)
• BatchDAG: LLM-Planned Execution Graphs for Scalable Ad-Hoc Analysis Over Enterprise Data [0.0]
  BatchDAG is a system in which an LLM generates a typed acyclic graph (DAG) of operations.<n> BatchDAG processes queries over 50,000+ meetings in under 60 seconds.<n>In production at Brevian, BatchDAG processes queries over 50,000+ meetings in under 60 seconds.
  arXiv  Detail & Related papers  (2026-04-17T19:22:34Z)
• An Agentic System for Schema Aware NL2SQL Generation [0.0]
  Large Language Models (LLMs) raise concerns regarding computational overhead, data privacy, and real-world deployability in resource-constrained environments.<n>We propose a schema based agentic system that strategically employs Small Language Models (SLMs) as primary agents, complemented by a selective LLM fallback mechanism.<n>The system achieves an average cost per query of 0.0085 compared to 0.094 for LLM-only systems, achieving near-zero operational costs for locally executed queries.
  arXiv  Detail & Related papers  (2026-02-25T02:58:02Z)
• Semantic Caching and Intent-Driven Context Optimization for Multi-Agent Natural Language to Code Systems [0.0]
  We present a production-optimized multi-agent system designed to translate natural language queries into executable Python code for structured data analytics.<n>Unlike systems that rely on expensive frontier models, our approach achieves high accuracy and cost efficiency through three key innovations.<n>We describe the architecture, present empirical results from production deployment, and discuss practical considerations for deploying LLM-based analytics systems at scale.
  arXiv  Detail & Related papers  (2026-01-16T11:32:20Z)
• Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL [63.578576078216976]
  CLIN is a benchmark of 633 expert-annotated tasks on MIMICIV v3.1.<n>We evaluate 22 proprietary and open-source models under Chain-of-Thought self-refinement.<n>Despite recent advances, performance remains far from clinical reliability.
  arXiv  Detail & Related papers  (2026-01-14T21:12:06Z)
• CSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scale [0.0]
  We propose a novel hybrid Retrieval Augmented Generation (RAG) system consisting of contextual, structural, and relational retrieval.<n>We demonstrate that CSR-RAG achieves up to 40% precision and over 80% recall while incurring a negligible average query generation latency of only 30ms on commodity data center hardware.
  arXiv  Detail & Related papers  (2026-01-10T13:20:07Z)
• SING-SQL: A Synthetic Data Generation Framework for In-Domain Text-to-SQL Translation [2.0799061948689306]
  SING-a is a fully automated two-stage framework for generating high-quality, high-coverage synthetic Text-to-data.<n>SING-LM is a family of compact language models fine-tuned on the synthetic data.
  arXiv  Detail & Related papers  (2025-09-30T02:14:49Z)
• Agentic LLMs for Question Answering over Tabular Data [6.310433217813068]
  Question Answering over Tabular Data (Table QA) presents unique challenges due to the diverse structure, size, and data types of real-world tables.<n>This paper details our methodology, experimental results, and alternative approaches, providing insights into the strengths and limitations of Table QA.
  arXiv  Detail & Related papers  (2025-09-11T08:12:38Z)
• RAISE: Reasoning Agent for Interactive SQL Exploration [47.77323087050061]
  We propose a novel framework that unifies schema linking, query generation, and iterative refinement within a single, end-to-end component.<n>Our method emulates how humans answer questions when working with unfamiliar databases.
  arXiv  Detail & Related papers  (2025-06-02T03:07:08Z)
• SOPBench: Evaluating Language Agents at Following Standard Operating Procedures and Constraints [59.645885492637845]
  SOPBench is an evaluation pipeline that transforms each service-specific SOP code program into a directed graph of executable functions.<n>Our approach transforms each service-specific SOP code program into a directed graph of executable functions and requires agents to call these functions based on natural language SOP descriptions.<n>We evaluate 18 leading models, and results show the task is challenging even for top-tier models.
  arXiv  Detail & Related papers  (2025-03-11T17:53:02Z)
• DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL [18.915121803834698]
  We propose DB-Explore, a novel framework that systematically aligns large language models with database knowledge.<n>Our framework enables comprehensive database understanding through diverse sampling strategies and automated instruction generation.
  arXiv  Detail & Related papers  (2025-03-06T20:46:43Z)
• RSL-SQL: Robust Schema Linking in Text-to-SQL Generation [51.00761167842468]
  We propose a novel framework called RSL- that combines bidirectional schema linking, contextual information augmentation, binary selection strategy, and multi-turn self-correction. benchmarks demonstrate that our approach achieves SOTA execution accuracy among open-source solutions, with 67.2% on BIRD and 87.9% on GPT-4ocorrection. Our approach outperforms a series of GPT-4 based Text-to-Seek systems when adopting DeepSeek (much cheaper) with same intact prompts.
  arXiv  Detail & Related papers  (2024-10-31T16:22:26Z)
• SelECT-SQL: Self-correcting ensemble Chain-of-Thought for Text-to-SQL [3.422309388045878]
  We introduce SelECT-, a novel in-context learning solution that uses an algorithmic combination of chain-of-thought, self-correction, and ensemble methods. Specifically, when configured using GPT as the base LLM, SelECT-Turbo achieves 84.2% execution accuracy on the Spider leaderboard's development set.
  arXiv  Detail & Related papers  (2024-09-16T05:40:18Z)
• DAC: Decomposed Automation Correction for Text-to-SQL [51.48239006107272]
  We introduce De Automation Correction (DAC), which corrects text-to-composed by decomposing entity linking and skeleton parsing. We show that our method improves performance by $3.7%$ on average of Spider, Bird, and KaggleDBQA compared with the baseline method.
  arXiv  Detail & Related papers  (2024-08-16T14:43:15Z)
• SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended) [53.95151604061761]
  This paper introduces the framework for enhancing Text-to- filtering using large language models (LLMs) With few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error analyses. With instruction fine-tuning, we delve deep in understanding the critical paradigms that influence the performance of tuned LLMs.
  arXiv  Detail & Related papers  (2023-05-26T21:39:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.