Product Landscape Analysis
Wealth Management - Registered Investment Advisors
The number of new application models coming out of the large-language model space is close to unprecedented. These off the self LLMs come with such advanced knowledge and flexibility it is no wonder the flood-gates for its use have opened. In this use-case we are using chatGPT to explore a specific product landscape and understand both the organizational, product, features, users and aggregate pros and cons of these offerings. The product space choosen is the Registered Investment Advisor Wealth Management industry. This represents a complex landscape of finance, compliance, client relationship management, performance and reporting that there are no shortage of companies addressing this approximately $30 trillion industry (not a typo.)
chatGPT Extract
The goal here is to explore a subject area and then iteratively extract additional data points on companies, products and features. The initial input into this model was a curated list of approximately 1000 companies in the RIA space and then to report on their multiple product offerings and features. The technique used is based upon chatGPT and 'prompt engineering'[1] used to construction of system and user prompts that provide chatGPT with additional context to respond to questions. In addition to the construction of the Product Knowledge Graph, the system is equally impressive in creating cypher[2] queries based upon the known Neo4j graph model .