One Converter Handles All of It
Documents come from everywhere: an archive to ingest, a customer upload form, a mailbox of attachments. Write the integration once: the same call that converts a PDF converts a PowerPoint, with nothing to change on your side.
Everything Between Your Files and Your Vector Store
Structure That Survives the Conversion
Headings, nested lists, emphasis and links come through as real Markdown. Chunking on headings keeps each chunk in context, which is what makes retrieval accurate.
Heading Detection for Documents Without Styles
PDFs and exported documents often have no heading styles at all, only bigger fonts. The converter infers the hierarchy from font sizes and rebuilds real Markdown headings, so even flat documents become chunkable.
Tables as Markdown Tables
Tables convert to GitHub-flavored pipe tables that models read reliably. No HTML fragments, no flattened rows. Column headers stay attached to their values and merged cells are resolved, so every number keeps the row and column that give it meaning. When a retrieved chunk contains a table, the model can quote the exact figure instead of guessing from loose text.
Four Ways to Handle Images
One parameter decides what happens to every image. Strip them for the smallest output and lowest embedding cost, replace them with AI-generated text, keep them inline in one self-contained file, or get them back as separate files in a zip.
remove
Smallest output, fewest tokens, no binary noise
describe
AI-generated text replaces every image
embed
One self-contained file, images inline
extract
Markdown plus image files in a zip
AI Image Understanding for Maximum Retrieval Coverage
With describe, an AI model reads each image and writes text for it: scanned paragraphs are transcribed word for word, tables become Markdown tables, charts get their values and trends summarised, photos and diagrams get short descriptions. Content that only existed as pixels becomes searchable text. Purely decorative images are dropped.
Chart Data, Not Chart Pictures
Word charts carry their underlying values. Those become a data table in the Markdown, so the actual numbers are retrievable instead of a description of a picture.
Embedded Spreadsheets and Objects
An Excel sheet embedded in a document is normally invisible to text extraction. It is rendered and, with AI description, transcribed back into a Markdown table with exact values.
Equations and Diagrams
Formulas convert to readable linear text. SmartArt diagrams contribute their node text instead of disappearing.
Scanned Documents Join the Same Pipeline
Scanned pages have no text layer. With AI description, the model transcribes them word for word, so scans flow through the exact same call as digital documents.
Noise Removal
Page headers and footers are left out, and repeated decorative images are recognised and dropped. Repeating page furniture never reaches the vector store.
Predictable, Flat Pricing
Priced per conversion, not per page. Document ingestion costs stay predictable at any document size, unlike per-page parsing services.
Your Data Stays Yours
Documents are converted and returned. Nothing is retained, indexed or locked into a proprietary store, so there is no migration cost if you change providers.
Production Ready
Conversions run in parallel, with password support for protected files, page-range selection for PDFs and a configurable timeout.
report.pdf
done
contract.docx
converting
legal-scan.pdf
converting
From Markdown to Answers That Cite Their Source
Embedding-Ready Chunks
Return the document as retrieval-sized chunks, each with its heading path and page number, ready to load straight into a vector database.
Structured JSON Output
Return the conversion as JSON instead of Markdown, shaped to a schema you define, so the result maps straight onto your own data model.
"The lease term is 24 months, beginning on the delivery date."
contract.pdf · p. 12Page Citations
Page numbers are carried through the conversion, so an answer can cite the page it came from.
Document Metadata
Title, author and page count are returned with the Markdown, for filtering and provenance.
One Call, Any Document
POST a PDF, Office document, email, image or any of 200+ formats to the same endpoint.
Clean Markdown, retrieval-sized chunks, or JSON in your own schema. Set image handling with one parameter.
Chunk, embed and store. Headings, tables and page numbers are already in place.
Built for Document-Heavy AI Workloads
RAG Document Ingestion
From raw files to chunk-ready Markdown, in the shape your splitter expects.
Vector Database Loading
Ingest whole document archives with consistent structure across every format.
Direct Model Context
Pass converted files straight to a model as context, without a cleanup step.
Knowledge Base Migration
Move documents into wikis, static sites and knowledge bases as portable Markdown.
What Parsing Services Leave Out
You parse, you own the pipeline: that part is the market norm. The differences are in what a conversion costs, and what the converter can actually read.
Per-Conversion Pricing
Specialist RAG parsers price per page. A 300-page manual costs the same as a 2-page memo here.
Image Understanding
General converters output Markdown but skip what is inside images. Scans, charts and embedded objects become retrievable text here.
Input Formats
One integration covers every document source you have, plus 15 years of conversion infrastructure behind it.