Jeremy Neely Text Jeremy

Documents & OCR · Beta

AME-XDOC

Moves hundreds of Word documents to a new template, and checks itself.

Built for: A manufacturer moving its machine SOPs into a new setup and changeover form

A conversion run scoring each document as it goes (sample SOPs).
A conversion run scoring each document as it goes (sample SOPs).

The problem

When a company replaces a document standard, hundreds of Word files have to be rebuilt, not just restyled. Content moves between sections, values buried in paragraphs belong in tables, and equipment photos must survive. By hand it is weeks of work, and the hard part is knowing which converted documents you can trust.

What I built

I built a converter that follows rules written in a readable Markdown file, which an AI can draft from a few example pairs. Every converted document gets a confidence score, a checklist result and a plain-English log entry, and a rule change is only adopted if it does not make any known-good example worse. It was built and proven on a manufacturing client's machine SOPs.

How it works

  1. Convert three documents by hand as examples.
  2. Let the AI write the conversion rules and test them against the examples.
  3. Run a small trial batch, review, and fix rules in plain words.
  4. Run the full set overnight with a stop-by time.
  5. Review only the documents that scored low.
How AME-XDOC is wiredA rule change is adopted only if it makes no known-good example worse. Originals are never modified.
Comes inInside AME-XDOCGoes out Legacy Word files a folder, hundreds Rules file Markdown, AI-drafted Example pairs old + known-good new Convert script in a sandbox Check checklist, pictures intact Score confidence per document Batch review proven on every example Converted documents new template, photos kept Run log plain words, then detail Low scores to review a person checks only these

Built with

  • Python
  • python-docx
  • pywebview
  • Claude (Opus, Sonnet, Haiku)
  • ReportLab
  • pytest
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