Beyond RAG: Building Graph-Aware Retrieval for Contract Reasoning

Why We Moved Beyond Vector Search for Contract QA When we started building AgreedPro, one of the core technical questions seemed almost ordinary: how do you answer questions over contracts using modern AI retrieval systems? At first, the answer felt obvious. Use RAG. Chunk the contract. Embed the chunks. Retrieve the top matches. Pass them to a language model. Let the model generate the answer. That pipeline is familiar because, in many domains, it works. It works well on documentation, FAQs, product manuals, internal wikis, and other corpora where relevant information is typically localized. A question points to a paragraph, a section, or a small cluster of nearby passages. Retrieval is largely a similarity problem. Contracts are different. That difference was not immediately obvious when we were building early versions of AgreedPro. The model often produced answers that looked right. They were well-phrased, coherent, and aligned with the wording of the contract section that had been