The ingestion layer, not another integration
Most “AI-ready document” tools sell you a connector into a specific platform. Marklune does the opposite: it sits at the front of your stack and turns documents into clean Markdown — the one format every LLM, vector store and agent can already read. You get the input your stack wants without adopting yet another dependency to maintain.
Where Marklune fits in your stack
PDF · DOCX · HTML · Excel · …
↓ Marklune (convert)
clean Markdown
↓
chunk → embed → vector store → agent / LLM context
One conversion step, then your existing pipeline takes over.
Structured Markdown vs. the alternatives
Each approach has a place. The point is to pick deliberately rather than default to pasting a PDF.
| Approach | Structured | Reusable | Cost at scale |
|---|---|---|---|
| Clean Markdown (Marklune) | |||
| Raw pasted PDF text | |||
| Multimodal vision (per query) |
When vision reading is the right call
For a single short document where you just want an answer, letting a multimodal model look at the PDF is perfectly reasonable — Marklune isn't trying to replace that. The conversion step pays off when you have many documents, need consistent chunks, want to cache results, or must cite sources: that's where structured Markdown is simply more reliable than re-reading the file every time.