LLM’s for Enterprise –Generative Q&A on Your Private Knowledge Base

How to construct a cost-effective, secure, and trustworthy Generative AI solution with a purpose-built open architecture using RAG (Retriever Augmented Generation)?

Once upon a time, in a world buzzing with excitement and intellectual curiosity, ChatGPT emerged as a transformative force. Unless you were dwelling on Mars, chances are you had already embarked on a fascinating experience with ChatGPT.

As the wonders of ChatGPT permeated the collective consciousness, enterprises were quick to envision the potential within their own realms. A common desire echoed “I wish we had an internal ChatGPT-like tool for our company.”

While the availability of ChatGPT OpenAI APIs is one option, many companies wonder “Why settle for existing options when we can strive for a purpose-built architecture tailored to our needs?”

Why do you need a private/purpose-built LLM stack?

•Do you want to avoid hallucinations?

•Do you want to fine-tune the model to your enterprise data?

•Do you want to protect your enterprise data from going outside?