This client, in particular, worked in finance and wanted a thorough analysis of their options. This client wanted to explore AI solutions but insisted on an LLM. However,
Large language models don’t inherently produce explicit confidence scores that are easily interpretable. A random forest model, trained on historical interactions, can actually be superior in this context and allowed cross-reference for low quality outcomes.
We created a striking visualization for a client that showcased the usage of wraps and its correlation to experience and tournament placement. This drove direction for the client and led to a substantial increase in revenue (25%) due to recommendations.
A client in a nonprofit organization requested machine learning methodology to create a precise focus on the company trajectory. After working alongside for several quarters (and after making an invaluable database), enough information was gathered to fulfill this request and provide ample guidance.
This report showed our methodology to the client, which helped establish trust in the thoroughness of our strategy. We also utilized a time series analysis, which can be notoriously difficult to manage.
