This paper presents an enterprise HR FAQ chatbot built using Retrieval-Augmented Generation (RAG) to provide grounded and verifiable responses to employee queries. To address domain mismatch in the Doc2Dial dataset, a custom enterprise HR knowledge base was constructed. The system uses FAISS for dense vector retrieval and IBM Granite-3.1-2B-Instruct for response generation, deployed via a Databricks pipeline and a Hugging Face Spaces interface. The system was evaluated through multi-model benchmarking against Phi-3.5-Mini, Qwen-2.5-1.5B, and SmolLM-1.7B under CPU constraints. Granite achieved the best balance between grounding accuracy and citation compliance, with a latency of 8.5 seconds and a perplexity of 5.09. In contrast, Phi-3.5-Mini achieved lower perplexity (3.35) but higher latency (12.2 seconds), while smaller models showed reduced grounding quality and inconsistent citation behavior. A strict L2 distance threshold (> 1.5) was implemented to prevent hallucinations, ensuring deterministic fallback responses for out-of-scope queries. Results demonstrate that a lightweight, constraint-aware RAG architecture can deliver reliable, citation-grounded answers in enterprise environments.