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Sırdaş
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PDF to Markdown for Perplexity

Prepare your PDF for Perplexity: clean Markdown, redacted personal data and a token count. All on your device.

Sent to servers: 0 BNothing left your device

About this tool

Perplexity answers better with well-structured text: headings, lists and paragraphs, without repeated headers or stray page numbers. We turn your PDF into Markdown with that structure and estimate the tokens so you know whether it fits in one conversation.

Before you paste anything into Perplexity, personal data is replaced with consistent placeholders ([NOMBRE_1], [DOCUMENTO_1]…): the same value always gets the same placeholder, so the model understands the document without knowing the people.

What Perplexity says about the files you upload

Quoted from its own pages, without summarising what they do not say. Policies change: open the source before you decide.

Its official pages refused to be read when we checked them (they returned a block), so we quote nothing from Perplexity. Read them yourself before uploading a document:

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When you do not need Sırdaş

If the document holds nobody's data and Perplexity accepts the PDF, upload it directly: it is faster. Sırdaş is for PDFs with names, ID numbers or accounts, or when you do not want the whole file to leave your machine.

How it works

  1. 01

    Drop the PDF.

  2. 02

    Review the Markdown and the highlighted data.

  3. 03

    Copy (or download the .md) and paste it into Perplexity.

Questions

Can't Perplexity read the PDF directly?
Many assistants accept PDFs, but uploading sends the file to their servers. With Sırdaş the PDF never leaves your machine: you only share with Perplexity the anonymized text you choose to paste.
Which data is hidden before pasting into Perplexity?
ID and passport numbers, tax IDs, emails, phones, dates, addresses, cards, medical record numbers and labelled names (patient, landlord, signed by…). Detection is automatic and can miss items: review the result.
How do I know the token count?
We show an estimate (about 4 characters per token). It is indicative; each model counts slightly differently.

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