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$
USD
I will convert PDF and word documents to clean ai rag ready markdown
Amin bm
About this gig
Want to convert a large volume of scanned documents and non-text files into text suitable for Rag? I can do that for you. But not only that.
A good RAG starts on a research repository or project documents before the documents are stored. Text documents always contain a lot of data that interferes with lexical or vector search: lists, images, references, endnotes, etc. They not only occupy your database, but also waste resources in the search process and produce many redundant results. But removing this noise manually is very difficult and time-consuming. But automating this process requires a tool that recognizes the noise from the content and does not remove the content of your texts by mistake.
I built MD for AI, my own document processing engine that has been tested on hundreds of real books. This engine preserves attribution in each fragment so that the retrieved parts can remain connected to their source. I also have a separate Persian and multilingual OCR pipeline for scanned content.
I convert PDF, Word, and other documents into clean, structured, and citation-ready Markdown for RAG systems, AI search, custom GPTs, and knowledge bases.
Get to know Amin bm
Amin bm
Programming and Tech
- FromUnited Kingdom
- Member sinceAug 2026
Languages
English
Python and JavaScript developer specializing in reliable RAG, hybrid search and document processing systems. Translating and studying different languages is my passion, and I have thousands of pages of specialized and general translations.
I build complete, tested solutions that turn private documents into searchable knowledge bases. My public work includes a live serverless retrieval backend and md for AI, my own pipeline for cleaning, structuring and chunking books for RAG. I bring a Masters in Economics, strong quantitative reasoning, documented source code and honest project scoping.
My Portfolio
FAQ
Is this just PDF to text conversion?
No. The focus is retrieval ready structure: cleanup, hierarchy, chunks, metadata, attribution and quality checks. Simple format conversion alone is a different low value service.
Do you support scanned PDFs and images?
Yes, through optional OCR quoted after inspection. OCR is unnecessary for documents that already contain usable text and is not included automatically.
Do you limit every order by page or file count?
No universal limit fits every corpus. Clean similar files can be processed efficiently, while one difficult scan may require more work. Standard and Premium are scoped after a representative sample.
Which output formats can you provide?
Markdown and JSONL are the primary outputs. Plain text, structured JSON or a project specific schema can be discussed before ordering.
Will the output work with my RAG framework?
I can adapt chunk and metadata fields to an agreed target such as a custom Python pipeline, FastAPI service, vector database importer or comparable RAG workflow.
