Stephen Chin

Modern finance runs on software that was built decades ago, understood by fewer people each year, and too critical to stop. The hardest part of modernization is not writing replacement code. It is reconstructing the hidden map of business rules, dependencies, data flows, and downstream effects that accumulated around it.

GraphRAG has quickly become a catch-all term for everything from graph-enhanced retrieval to Text2Cypher, agent memory, and community-based summarization. That is exciting, but it also creates confusion. When people say “use GraphRAG,” what do they actually mean, and which pattern fits which problem?

Autonomous assistants are easy to demo and hard to make reliable. The problem is usually not tool access. It is memory. Most assistant architectures still treat memory as a chat log plus vector retrieval. That is fine for document question answering, but it breaks down when the assistant must...

AI models are getting tasked to do increasingly complex and industry specific tasks where different retrieval approaches provide distinct advantages in accuracy, explainability, and cost to execute.

RAG has become one standard architecture component for GenAI applications to address hallucinations and integrate factual knowledge. While vector search over text is common, knowledge graphs represent a proven advancement by leveraging advanced RAG patterns to access and integrate interconnected factual information, complementing the language skills of LLMs.

As enterprises have embraced GenAI architectures the biggest gap is connecting all of the structured and unstructured datasets across your organization. Knowledge graphs are a natural representation of connected data that allows you to enable higher accuracy and more explainable retrieval using GraphRAG.