I built a document chunking + embeddings API for RAG, looking for feedback

I work on production RAG systems and kept hitting the same problem: chunking quality quietly decides retrieval quality, but most setups just split text by a fixed token count and move on.

So I built a small API that does sentence-aware, structure-aware chunking and returns embeddings in one call (PDF, DOCX, CSV, XLSX, ZIP in, chunks plus vectors out, using bge-m3). The idea was to make the boring part of a RAG pipeline reliable so the rest of the stack gets cleaner inputs.

A few things I learned building it:

• Fixed-size chunking looks fine in demos and falls apart on real documents with tables and headings.
• Returning embeddings in the same call removed a lot of glue code people usually write themselves.
• Multilingual retrieval (Turkish in my case) is where weak chunking hurts the most.

It is live with a few free runs if anyone wants to try it: chunkingservice.com

I would like feedback on the chunking strategy and what formats or options are missing for your own RAG work. What are you using for chunking right now?