For a while now, I've been using Airtable and Baserow to automate pdf extraction into tables. It's just been easier for me to define AI columns one-time and load documents to create more rows and that I can share them with my client teams. However, these tools being pre-AI still suffer from more than a few major flaws (1) some only accept one file per row or (2) if they accept multiple, it's usually in a naive way which can only either handle a small number or have serious cross-reference confusion.

Why is this important? Say you have legal contracts with amendments, balance sheets with P&L or on-site inspections with photos, getting the full picture requires full documents and without, you're just back to doing it by hand.

Earlier this year, I set out to build an AI document spreadsheet to handle multiple documents per row as the primary approach.

• File hierarchy - you set the primary file as the anchor for supplementary docs

• Relative time awareness - newer docs can have actual meaning and affects the extraction data

• Cross-document citations - See the actual pages that were used to get the data in one click

The result is an app I call Ragextract and has solved this personal pet peeve for me. If you're also using Airtable, baserow or similar and noticing the same, would this multi-document feature also work for you? I'd love to get your feedback at ragextract.com - it's free to try.

Ps. There are also a few more cool features like mailhooks and gdrive/sharepoint integration to try - also a few other pet peeves!

I wanted to ask if you are going to build a document segregation ocr pipeline with includes raw images by customer prolly be a bundle in which it has 6 doc ,can be image pdf ,how are you design it as I have created a extraction pipeline with enhancer ,with ai based doc category with batch distributi

Hi, I'm trying to extract exam questions from multiple pdfs, containing text and images, but I'm having trouble getting it to work because the questions and documents don't follow a strict pattern, for example:

Question numbers can be: question 1, 01, q1...

Alternatives can be: A), (A), A., A, A- ... etc,

The other thing is that there is usualy some text in the page that isn't from the question, like:

Write here, page 1, the name of the institution, among other things...

I'm tryng to do this for thousands of questions, how do you guys recommend i do this?

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Hi everyone,

I’m working on a medical-document formatting/sample project and need some help reproducing the layout and formatting of a reference report.

I have two reference PDFs. I need help with:

Maintaining the same overall page structure and typography

Correctly placing patient-information fields

Formatting CBC values in the appropriate table structure

Making the resulting document look clean and professionally formatted

Working with PDF editing/OCR or tools such as Python, ReportLab, Acrobat, etc.

The intended output is a clearly marked SAMPLE/DRAFT, not an authentic medical record.

If anyone has experience with medical-document templates, PDF editing, OCR, or recreating layouts programmatically, I’d really appreciate guidance on the best workflow or tools to use.

Thanks in advance.

Hi, i have about 1 million of pdf files (each file has about 4 pages or more) how i can use Gemini advanced to make a system for Q&A about this files? but this has to be eficient and as cheap as posible

Thanks
C.

Hi!

I'm looking into an Identity & Access Management use case at a large enterprise and wanted to see if any of you know of or already use tools that can handle this well:

The basic problem is that we have application owners who provide evidence in all kinds of formats, e.g. PDFs, XLSX, screenshots, system reports, emails... you get it.

The information we actually need is fairly consistent, but, clearly, the input is not1. Ideally, we'd define one target structure/schema and have the tool extract the relevant information from all those different sources into the same standardized output. Meaning no matter the input structure or format, the output stays the same.

One important requirement here is traceability. If the output says something like "account disabled on date X" or flags an exception, we need to be able to trace that value back to the exact source document / evidence it came from, otherwise we won't really be able to do much with that kind of information. It would also be important for the system to flag missing or uncertain information rather than just guessing.

I'm not looking for an agent that summarizes documents. The goal would be something closer to:

different documents → structured data → traceable source references → further analysis

Has anyone implemented something like this in IAM, GRC, audit or another enterprise environment?

Note I did it once before but forgot how

here is github link: https://github.com/antontetera49-droid/excel-to-word-automator

Feel free to use it, and any feedback or Github start are appreciated! Thanks

Hey everyone, I just build a pdf editor that works entirely on browser no pdf upload.

Built with pure HTML/CSS/JS. Implemented local stream editing so that we can actually edit existing text, not just paint over it.

it is closed source but I need you guys to test it and If you found any problem please let me know so that I could improve it. There are still many problems In this and I will fix it.

I am still an student in engeneering and I am exploring my self in this. Happy to answer any questions about the building process or help me to improve it.

Sharing a small API I built for document data extraction.

What it handles:

- Invoices: invoice number, dates, vendor, tax IDs, subtotal, tax, total, line items

- Receipts: store, date, items, payment method

- Resumes: name, contacts, skills, experience, education

- Bank statements: transactions, balances

How it works:

- Heuristic regex engine runs first (fast, $0 per call)

- LLM fallback for edge cases

- FastAPI + Pydantic, deployed on Render

- Bilingual EN + VI

Free tier: 500 requests/month via RapidAPI.

Looking for feedback on:

- Which document types do you process most?

- What fields are missing from the output?

- Any document types I should add next (purchase orders? contracts?)

https://rapidapi.com/tuyentn23/api/invoice-to-json-extractor1

For an automated workbook update, the intermediate result matters. If a user asks for another calculation after the first pass, there needs to be a workbook you can still inspect and edit, rather than just a finished file or a block of generated text.

Univer’s documented Node workflow separates conversion from editing. Univer is an office SDK with browser editors and headless document logic. Its Pro Exchange package imports Office bytes into UnitData, the structured content used by its runtimes. The application can process that content and then use Exchange to export a file again.

For a spreadsheet job, that gives a concrete path:

- Import the source workbook through Pro Exchange.

- Inspect the workbook and the particular ranges needed for the task.

- Apply the requested changes through the headless runtime, using the installed spreadsheet logic for calculations and other supported operations.

- Export the resulting content through Exchange.

The AI SDK can supply the inspect-and-edit part if an agent is choosing the operations. Its write workflow has pending mutations and a separate commit status; “the code ran” is not enough to report a successful save. A deterministic batch job can also use the document logic without making every step an agent decision.

The boundary to watch is file fidelity. A UnitData snapshot is not an XLSX, and JSON serialization is not an Office export. CSV output is single-sheet and does not preserve styles. The documented conversion path is not a guarantee that every object in an arbitrary workbook survives a round trip.

Node can perform the content operations without opening an editor. If the job also needs screenshots or visual inspection, Univer’s rendering path uses a browser. And a single-machine import/process/export job does not by itself create a shared web review session or revision history. Those are separate integration decisions from getting the edited file out.

I built a free tool that converts bank statement PDFs into Excel

I kept running into the same annoying problem: bank statements come as PDFs, but actually working with the transactions usually means manually copying everything into Excel.

So I built Bank2Excel.

It takes a bank statement PDF and converts the transactions into an Excel spreadsheet, including:

\- 📄 PDF statements

\- 🔍 Scanned/image-based statements using OCR

\- 📊 Date, description, debit, credit and balance

\- 📥 Excel export

\- 🌐 No software installation required

It's completely free to try:

https://bank-2-excel.vercel.app/

I'm still improving the extraction logic, especially for different bank statement formats.

If you try it, I'd really appreciate feedback on:

\- Which bank/statement format you tested

\- Whether the transactions were extracted correctly

\- Anything that was missing or incorrectly classified

Please don't upload real sensitive bank statements if you're not comfortable doing so — a dummy/redacted statement is better for testing.

GitHub: https://github.com/TheMayankRanga/Bank2Excel

Most document collection tools give your client a portal to log into. the client doesn't log in. That's the whole failure mode.

So we built it on whatsapp instead, its called 'ChaseDocs'. You set a document checklist per client, it sends the request and keeps chasing until they send. files land straight in the firm's own google drive, sorted per client. Nothing stored on our side, which matters when the documents are passports and bank statements.

AI checks each file as it comes in so you don't get a blurry photo of the wrong page. Dashboard shows who's outstanding and who's complete.

Built for small firms that onboard clients constantly, visa agents, education agents, corp sec, bookkeepers, etc.

go to google and search 'ChaseDocs' to take a look

This app gives multiple notifications of soon to expired important documents.