u/kaku2050

I have a feeling that the most valuable RAG systems are built on data that is sensitive for companies. That way, the data never leaves their controlled infrastructure. However, processing a massive amount of data of various formats and sources into a format suitable for a vector DB without using hosted parser APIs like Azure Document Intelligence, LlamaParse, Unstructured, etc. Seems like a nightmare.

I want to find out how this looks in practice and map out where the real pain points hide in these projects.

So if you've built one of these: on-prem or air-gapped because you had to (regulated data, client contracts), or just because you wanted control/privacy/cost  

Sources could be anything: PDFs and tables on disk, or data pulled from internal tools like Confluence, Jira, SharePoint. 

Drop a comment about what your biggest pain points were. What breaks, what eats time, what you'd do differently, what stack you used

I have a feeling that the most valuable RAG systems are built on data that is sensitive for companies. That way, the data never leaves their controlled infrastructure. However, processing a massive amount of data of various formats and sources into a format suitable for a vector DB without using hosted parser APIs like Azure Document Intelligence, LlamaParse, Unstructured, etc. Seems like a nightmare.

I want to find out how this looks in practice and map out where the real pain points hide in these projects.

So if you've built one of these: on-prem or air-gapped because you had to (regulated data, client contracts), or just because you wanted control/privacy/cost  

Sources could be anything: PDFs and tables on disk, or data pulled from internal tools like Confluence, Jira, SharePoint. 

Drop a comment about what your biggest pain points were. What breaks, what eats time, what you'd do differently, what stack you used

I have a feeling that the most valuable RAG systems are built on data that is sensitive for companies. That way, the data never leaves their controlled infrastructure. However, processing a massive amount of data of various formats and sources into a format suitable for a vector DB without using hosted parser APIs like Azure Document Intelligence, LlamaParse, Unstructured, etc. Seems like a nightmare.

I want to find out how this looks in practice and map out where the real pain points hide in these projects.

So if you've built one of these: on-prem or air-gapped because you had to (regulated data, client contracts), or just because you wanted control/privacy/cost  

Sources could be anything: PDFs and tables on disk, or data pulled from internal tools like Confluence, Jira, SharePoint. 

Drop a comment about what your biggest pain points were. What breaks, what eats time, what you'd do differently, what stack you used

Looking for people who've built an enterprise RAG running fully locally / on-prem including the ingestion pipeline, where instead of reaching for cloud APIs (LlamaIndex, Unstructured, etc.) you did the heavy lifting locally.

Sources could be anything: PDFs and tables sitting on disk, or data pulled from internal tools like Confluence, Jira, SharePoint → structured format → vector DB.

I'm trying to map out where the real pain points hide in these projects. What breaks, what eats time, what you'd do differently. Not affiliated with anyone, not selling anything. I'm researching this for myself. 

If you've done this drop a comment with the stack you used or just "in" and I'll send over a short doc with 6 questions, about 10-15 minutes. When I'm done I'll post a summary of the findings back in this thread so everyone can see what came up.

Looking for people who've built an enterprise RAG running fully locally / on-prem — including the ingestion pipeline, where instead of reaching for cloud APIs (LlamaIndex, Unstructured, etc.) you did the heavy lifting locally.

Sources could be anything: PDFs and tables sitting on disk, or data pulled from internal tools like Confluence, Jira, SharePoint → structured format → vector DB.

I'm trying to map out where the real pain points hide in these projects. What breaks, what eats time, what you'd do differently. Not affiliated with anyone, not selling anything. I'm researching this for myself. 

If you've done this drop a comment with the stack you used or just "in" and I'll send over a short doc with 6 questions, about 10-15 minutes. When I'm done I'll post a summary of the findings back in this thread so everyone can see what came up.

Looking for people who've built an enterprise RAG running fully locally / on-prem — including the ingestion pipeline, where instead of reaching for cloud APIs (LlamaIndex, Unstructured, etc.) you did the heavy lifting locally.

Sources could be anything: PDFs and tables sitting on disk, or data pulled from internal tools like Confluence, Jira, SharePoint → structured format → vector DB.

I'm trying to map out where the real pain points hide in these projects. What breaks, what eats time, what you'd do differently. Not affiliated with anyone, not selling anything. I'm researching this for myself. 

If you've done this drop a comment with the stack you used or just "in" and I'll send over a short doc with 6 questions, about 10-15 minutes. When I'm done I'll post a summary of the findings back in this thread so everyone can see what came up.

Looking for people who've built an enterprise RAG running fully locally / on-prem — including the ingestion pipeline, where instead of reaching for cloud APIs (LlamaIndex, Unstructured, etc.) you did the heavy lifting locally.

Sources could be anything: PDFs and tables sitting on disk, or data pulled from internal tools like Confluence, Jira, SharePoint → structured format → vector DB.

I'm trying to map out where the real pain points hide in these projects. What breaks, what eats time, what you'd do differently. Not affiliated with anyone, not selling anything. I'm researching this for myself. 

If you've done this drop a comment with the stack you used or just "in" and I'll send over a short doc with 6 questions, about 10-15 minutes. When I'm done I'll post a summary of the findings back in this thread so everyone can see what came up.

Hey! I'm looking for someone who has built an enterprise RAG running fully locally / on-prem, together with a document ingestion pipeline (PDFs/tables > structured format > vector database)

I'd like to learn what the biggest problems are that you run into on projects like this. I have a few questions, and I'm happy to share back whatever I uncover in my research

If you'd like to help, drop a comment or send me a DM. This is purely exploratory. I'm not selling anything