Preparing Enterprise Data for LLM-Assisted Customer Issue Analysis: A Governance-Centric Framework
Bibliographic record
Abstract
The increasing adoption of Large Language Models (LLMs) in enterprise environments has transformed customer support operations by enabling intelligent issue classification, automated response generation, and context-aware analytics. The effectiveness performance of LLM-powered customer issue analysis relies on the quality, governance, security, and compliance of enterprise data preparation pipelines, though. Organizations are still grappling with a host of issues, including poor record management, differing metadata, concerns about privacy, and compliance with regulations, that hinder the trustworthiness and scalability of AI-powered customer service. To address these gaps, this study introduces a framework centered on governance principles for preparing data for use by LLMs to analyses customer issues, incorporating all of the following aspects into a single analytical architecture data ingestion, cleansing, metadata management, compliance enforcement, data lineage tracking and privacy-aware data preprocessing. This framework also adopts governance-centric components, like access control, audit logging, anonymization, semantic enrichment and policy validation, to enable secure and explainable AI operations. The analytical performance has been shown to be improved by experimental evaluation over 10,000 enterprise customer support tickets, resulting in 92.3% classification accuracy and 0.91 F1-score in comparison to conventional ungoverned LLM approaches. The framework also lowered the chances of hallucination, boosted readiness for compliance and kept the inference latency low enough to meet the needs of real-time enterprise applications. The findings show that data preparation with governance awareness significantly contributes to the reliability, transparency, and scalability of LLM-powered customer service solutions. The suggested framework offers a pragmatic approach for the trustworthy enterprise AI adoption and secure, compliant, and efficient analysis of customer issues for modern digital organizations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".