We present Fathom, a Retrieval-Augmented Generation (RAG) pipeline for automated fact-checking, built entirely using lightweight open-source language models. The system begins with HyDE-style question generation to expand the context around each claim, followed by a dual-stage retrieval process using BM25 and semantic similarity to gather relevant evidence. Finally, a lightweight LLM performs veracity prediction, producing both a verdict and supporting rationale. Despite relying on smaller models, our system achieved an AVeriTeC score of 0.2043 on the test set, a 0.99% absolute improvement over the baseline and 0.378 on the dev set, marking a 27.7% absolute improvement.