Qrynt

For a long time I thought document extraction was the hard problem.

It turns out extraction is only the beginning.

After the text is extracted, every downstream system still has to answer questions like:

Is this a table or just aligned text?

Is this a key-value section?

Is this a heading or plain prose?

Is this a log or narrative text?

Every product seems to solve that layer differently—with prompts, heuristics, custom parsers, or post-processing.

That observation led me to build Qrynt.

Instead of stopping at text extraction, it deterministically restructures heterogeneous content into semantic structures that databases, ETL pipelines, search systems, and LLMs can consume consistently.

I'm curious whether others building AI, RAG, or data products have run into the same problem.

How are you handling the gap between "I extracted the text" and "my downstream system actually understands what it's looking at"?