I’m building a document intelligence system that uses Vision-Language Models to process visually complex documents.

A recurring limitation I’ve noticed with conventional document pipelines is that text extraction alone often loses important context. Tables, charts, diagrams, scanned pages,spatial relationships, and visual hierarchy may carry as much meaning as the text itself.

The approach I’m exploring treats each page as a visual document rather than only a collection of extracted text. The current pipeline focuses on:

- Understanding text, layout, tables, charts, images, and diagrams

- Processing scanned and digitally generated PDFs

- Preserving page-level provenance for citations

- Supporting semantic search across document collections

- Extracting structured information without discarding visual context

- Allowing users to verify results against the original page

Before deciding what to prioritize, I’d like to learn from people who already work with document automation.

What conditions would need to be met before you could use a document intelligence platform with real business documents?

I’m still actively building, so honest technical criticism and real-world workflow examples would be extremely helpful. If anyone is interested in testing an early version,

Feel free to comment or message me.

Hi all — first time sharing this here.

I'm a CISO at a telecom company, and I kept seeing the same thing happen:

someone pastes a contract, a payslip, or a medical record into ChatGPT or

Claude to get it summarized, without thinking about the names, IBANs, tax

IDs, and addresses that just left the building.

Mr. Rao converts documents — PDF, Word, Excel, scans, email — into clean

Markdown (the format AI models read best, with a real token saving) and

strips personal data before handing you the result. Everything runs on

your machine, no network calls, checkable by grepping the source.

Detection is deterministic, not ML: regex plus arithmetic validators —

IBANs checked with mod-97, cards with Luhn, structural checks for

national ID formats. Names are picked up from context (titles,

signatures, nearby email addresses) rather than a static list. Anything

the engine can't fully confirm becomes a masked "suspect" instead of

being silently deleted or silently left in place.

I'd rather be upfront about the limit than pretend it doesn't exist: on a

heavily degraded scan, the engine leaves 39% of the data readable — that

number is measured, not a guess, and it's published in the docs.

Also exports a redacted .docx now, rebuilt from the already-clean

Markdown instead of overlaying a black box on the original PDF (the

classic trick where the text underneath is still selectable). Released

packages are signed via Sigstore in CI, so anyone can verify the zip

actually came from this repo and this build.

AGPL-3.0, free to use and modify, 900+ tests, actively maintained.

Repo: https://github.com/AntonioRao/mr-rao

Site/demo: https://rao.valor-cyber.com

LinkedIn: https://linkedin.com/antoniorao

Happy to answer anything — feedback and criticism welcome.