richardmeng

We'll dockerize it.

what's wrong with vercel?

Sumble has been my critical tool to research the organization structure and responsibility in a large company, technology adoption like which organization has the LLM adoption.

Congrats on the launch!

A lot of infrastructure work is needed to make the SQL experience seamless work for unstructured data. And at the most part we do fork the open core data warehouse and build on top of it.

I think by parse it means more like document understanding

Today's large vision models like GPT-4o can parse the content heavy papers pretty well (and respect their structures).

Yah basically it allows you to send PDFs as image patches into GPT-4o model that workflow can be easily built.

Feel free to send me an email richard@roe-ai.com, happy to evaluate your case and try to save that 200K :p

We use Gemini to analyze the video in its raw format.

To add to Jason's point -

There is a big UI part here, because for multimodal data analytics, we think it's crucial for people to see and hear data.

For the RAG search, many DBs have built-in vector search, but chunking, indexing, and maintaining the index are kind of on your own. This may not be a problem for technical people, but it's a hassle for data people who own hundreds of data products within a company. Therefore, we have a semantic search index builder that allows one to build an auto-refreshing semantic search index with no code, and completely keep hands free from coming up with their own vectors.

In addition, data analysis often needs to interrogate the search results further. For example, let's say we have used pgvector to find all the photos related to the Golden Gate Bridge. But then we want to interrogate questions like which of these images has someone wearing a blue shirt. We have to apply another model, and that is outside of a normal DB's responsibility.

I guess to add to Jason's point, it depends on how data engineers/data analysts are perceived in their roles within the company. For some companies, we see a data analyst taking end-to-end responsibility from the data engineering to BI, but for others we also see a clear separation, data engineers doing data pipelining and data modeling, but data analysts are, in fact, business analysts. Regardless, we think that SQL is the common interface for both of the parties, and we're excited to see who will be the power users.

This does not work with Redshift. This is a query engine for unstructured data like documents, images, videos. Those data do not quite fit into Redshift / Bigquery data warehouse.

Thanks!

Right, our product is designed for data practitioners who want snappy data analytics on unstructured data.

Thanks for sharing your project, super cool idea! What does it take if we want to integrate our SQL engine with datachain?

Likely, can you elaborate on your use case and what db do you use?

Good points! We'll update our landing pages as you suggested.