Build a pipeline once, run it on every document, and get clean, token-efficient Markdown with every change traced. Nothing silently dropped, nothing invented.
Headers, footers and page numbers removed. Hyphenation repaired, page-broken paragraphs merged, headings detected. Every single change is logged in the run report.
Drop a document into the builder. It is processed in an ephemeral working directory and discarded when the run finishes. Never stored.
Drag steps from the action catalog — parse, clean, structure, transform, verify, export — or describe what you need and let the AI assistant draft the pipeline for you. Tune each step, then save the pipeline to reuse on the next hundred documents.
Every run produces the Markdown plus a full trace: what each step saw, what it changed, and why. Download the .md or fetch it over the API.
Drag steps into place, tune each one, and save the pipeline to reuse on the next document. No code. This is the actual builder screen, with the real step catalog.
The Markdown is only half the product. The other half is proof. Each run records what every step did, so you can verify the output instead of trusting it.
These are the steps that ship today. Each one does a single job and reports what it did to your document.
Extract text verbatim from PDF, EPUB, HTML, TXT/LOG or CSV. Free, local, and coordinate-level provenance for PDF.
Recognise text on scanned pages, or just the pages you pick, and merge it in.
Remove repeating headers, footers and page numbers. Reflow paragraphs, repair hyphenation. Every fix is logged.
Re-join tables split across a page break. Rows are merged verbatim, every merge logged.
Detect bullet and numbered list items from the raw layout, before reflow.
Detect headings and merge page-broken paragraphs from layout signals.
An AI classifier detects headings and structure by meaning, falling back to the free layout-based pass if it's unavailable. BYOK.
Stamp every block with a stable, content-derived anchor, so citations and re-runs point at the same block even as the document changes around it.
Explicit, logged edits: find and replace, redact, delete lines, drop pages, set metadata. The one place text changes by design.
Auto-detect and mask emails, phone numbers, IP addresses and ID-like numbers. No regex authoring required.
Keep only sections matching a query — free, offline word matching. Every dropped block is logged.
An AI classifier judges relevance by meaning, not just words. Uncertain sections are kept, every dropped block is logged. BYOK.
An LLM compares each page image against the output Markdown and reports findings. It reviews, never rewrites. BYOK.
The free, deterministic counterpart to AI Check: audits that every number in the source survives into the output, without exposing the digits themselves.
Fail the run unless the output meets your checks: required/forbidden patterns, minimum headings or length, no empty or un-OCR'd pages.
Audit the output for PII that redaction should have masked. Reports counts per page, never the text itself.
Render the document as Markdown with YAML frontmatter.
Token-sized, overlap-aware chunks, one JSON record per line — heading path, pages and provenance included. RAG-ready.
Export every table as structured JSON — header, rows and per-table provenance — for downstream data work that Markdown tables can't support.
On the roadmap: DOCX parsing, external parser connectors (LlamaParse, Azure DI, Mistral OCR), and a sandboxed JS Inline step for custom logic.
A repeated header, footer and page number cost roughly 15 tokens per page. On a 200-page document that is noise your model re-reads on every single call.
~3,000 tokens of pure junk per 200-page document, before counting broken hyphenation and shattered paragraphs. The clean-up pass removes it once, and logs every removal.The PII step masks emails, phone numbers and ID-like numbers inside the pipeline, before export. Every redaction is explicit and listed in the run report, so compliance can see exactly what was masked, and where.
masked, and logged Deterministic masking you can rerun on the next thousand documents. No prompt, no maybe.Files are processed in an ephemeral working directory and discarded when the run finishes. Only the trace you choose to keep persists, and you can delete it any time.
AI-assisted steps are bring-your-own-key. Keys are sealed at rest with AES-256-GCM, decrypted only in-process to run your pipeline, and never logged.
Every saved pipeline is callable by id on the versioned /v1 API. Build and test it in the UI, then run it headlessly from CI, a cron job, or your ingestion service.
curl -X POST "https://markdown4ai.com/api/v1/pipelines/YOUR_PIPELINE_ID/run" \
-H "Authorization: Bearer $M4AI_API_KEY" \
-F "file=@contract.pdf"
{ "run_id": "…", "markdown": "…", "warnings": [] }
read the API reference
Every account gets free credits each month. When you need more, top up with a pay-as-you-go pack. Credits are charged per page, and a failed run is never billed.
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Sign in with a magic link, drop in a document, and run your first pipeline in minutes. Free credits included, no card required.
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