I built a Gen AI chatbot to help a financial services firm streamline document searches, achieving up to 80% efficiency using Retrieval-Augmented Generation and the Titan model. After extensive comparisons among several LLMs, Titan proved most effective for my use case. Users praised the tool for saving time on documents that are dense with terminology and footnotes. However, despite this success, I'm grappling with whether my solution can compete with deep-pocketed giants like Amazon, whose solutions are integrated, visually appealing, and cost-effective.
While my Gen AI chatbot significantly improves document searching efficiency, I wonder if it can truly compete with the pricing and performance of established solutions like Amazon Q.
Despite the success and positive feedback on my chatbot, I recognize that merely working well isn’t enough; I need to ensure it offers superior value compared to enterprise giants.
Collection
[
|
...
]