01
Ingest
Pull text from PDFs, images, slide decks, Word docs, or pasted content.
How it works
Extract the text. Fix the OCR. Keep the structure.
The developer site is the technical side of Ellide: document cleanup, structured output, integration patterns, and why clean text beats raw PDF uploads in downstream AI systems.
02
Correct
Use a language model to repair OCR errors, normalize broken formatting, and recover the actual wording.
03
Structure
Preserve headings, lists, and page-level organization so the output works in chats and downstream systems.
04
Ship
Export lightweight Markdown or JSON that you can paste, diff, store, or feed into a pipeline.
Why this beats a raw PDF upload
Your model stops wasting context on cleanup.
OCR-heavy documents make models spend tokens deciphering noise instead of answering the actual question. Ellide turns that file into a cleaner representation before it hits the model.
Output options
Markdown for chats. JSON for systems.
Markdown is the default for human-in-the-loop AI workflows. JSON is useful when you need more rigid structure for code or automation.
Cross-link
Need the classroom workflow instead?
The education-facing site focuses on course-grounded tutoring, student study flows, and the guidance layer that shapes AI behavior.