Imagine sifting through mountains of digital documents—emails, contracts, presentations—searching for that one crucial piece of information. That's the daunting reality of eDiscovery in legal cases. Traditionally, this process involved armies of lawyers manually reviewing every document, a slow, expensive, and error-prone endeavor. But what if AI could help? New research explores how combining the power of graphs and Large Language Models (LLMs) can revolutionize eDiscovery, making it dramatically faster and cheaper. The challenge lies in balancing performance, cost, and the need for clear explanations of why a document is relevant. Existing AI methods, while improving speed, often lack the transparency required in legal settings. This is where the innovative "DISCOvery Graph" (DISCOG) approach comes in. DISCOG uses a two-step process. First, it builds a graph representing relationships between documents, senders, recipients, and key terms. This graph then predicts the relevance of each document to the legal request. Second, LLMs step in to provide human-readable explanations for these predictions, crucial for legal teams to understand and justify the AI's choices. The results are impressive. Tested on a real-world dataset of Enron emails, DISCOG significantly outperformed traditional methods, achieving higher accuracy in identifying relevant documents. More importantly, it slashed the number of documents needing manual review, leading to potential cost savings of up to 99.9% compared to traditional methods and 95% compared to current LLM-only approaches. This research points to a future where AI handles the heavy lifting in eDiscovery, allowing legal professionals to focus on strategy and analysis, not tedious document review. While challenges remain, the combination of graphs and LLMs offers a promising path towards a more efficient and cost-effective legal process.