Percorrer por autor "FRANCO, GUILHERME LIMA"
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- Assistente virtual inteligente para acesso a dados de negócioPublication . FRANCO, GUILHERME LIMA; Conceição, Luís Manuel SilvaModern organisations increasingly struggle to access and interpret enterprise data that is dispersed across isolated Business Information Systems (BIS). These silos hinder the ability to obtain a unified view of information, which is essential for timely and informed decisionmaking. Advances in Large Language Models (LLMs) offer the possibility of querying such data in natural language, thereby lowering the technical barrier for business users. However, the adoption of these models in corporate environments is constrained by concerns over data privacy, regulatory compliance, and the high operational costs of cloud-based solutions. These challenges underline the need for on-premises, resource-efficient approaches that preserve control over sensitive information. This dissertation presents an intelligent virtual assistant that answers business questions by orchestrating Model Context Protocol (MCP) tools to inspect schemas, draft explicitprojection SQL, validate read-only execution, and ground responses in results from a local Microsoft SQL Server instance of AdventureWorksDW2022. No model fine-tuning is performed; instead, the approach combines runtime schema filtering, deny-list validation, and prompt scaffolding to minimise hallucinations and enforce governance. A controlled evaluation over 52 representative prompts compares three configurations: a prompt-only baseline (B0), MCP with unfiltered schemas (B1), and a curated setup with filtering and explicit projections (S). The curated configuration yields substantially higher execution accuracy and fewer schema-error incidents than both baselines, demonstrating that governed tool use materially increases correctness without relaxing the privacy posture on a single on-premises workstation. Latency observations are reported descriptively and are attributable primarily to model generation rather than orchestration. These findings support the feasibility of privacy-preserving, on-premises conversational analytics under the EU General Data Protection Regulation (GDPR) and the EU Artificial Intelligence Act (Regulation (EU) 2024/1689), and suggest practical next steps: broadening schema coverage, refining curation policies, and exploring lighter local models and decoding strategies to improve interactivity.