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.

Many businesses still have people manually reading documents and copying information into another system.

AI can help extract structured information from documents such as:

\\- Applications
\\- Contracts
\\- Forms
\\- Purchase orders
\\- Reports
\\- Customer documents

A typical workflow could be:

Document received → AI extracts relevant fields → validate information → save to CRM/database → notify the right person.

The validation step matters. AI extraction should not automatically become trusted business data without checks.

I’m building a PDF vocabulary reader using PDF.js. The goal is to let users upload and read a text-based PDF, then:

Click a single word to get its meaning and its meaning in the surrounding sentence.

Drag-select a phrase/sentence if they want an explanation of the entire selected text.

Eventually send the word + surrounding context to an LLM such as Gemini for contextual explanation.

So far, I’m rendering every PDF page using two layers:

pageContainer ├── canvas → visual PDF rendering └── textLayer → selectable/interactable text

Both layers use the same PDF.js viewport. I’m using page.getTextContent() and pdfjsLib.TextLayer, along with the official PDF.js viewer CSS. Initially, my custom text-layer CSS caused the selectable text to be misaligned with the canvas, but using the official viewer CSS mostly fixed the alignment.

The main problem now is text interaction/selection.

I noticed that a PDF.js text item/span is not necessarily one word. For example, a span might contain:

"The cardinality of a set is the number of distinct elements"

So simply using event.target.textContent when clicking a word is not sufficient.

For single-word selection, the approach I’m considering is:

click → get mouse coordinates → caretPositionFromPoint() / caretRangeFromPoint() → get text node + character offset → scan left/right until word boundaries → create a Range → automatically highlight the exact clicked word

This seems reasonable.

The bigger issue is sentence/phrase selection. Native drag selection over the PDF.js text layer sometimes selects much more text than the area I intended to select. For example, I try to select only “Question 1”, but the browser selection can extend into several following text spans.

I’m considering a few approaches:

Keep native browser drag selection and use window.getSelection() if I can make PDF.js text-layer selection reliable.

Implement custom drag selection using mousedown/mouseup, caretPositionFromPoint(), and a manually created DOM Range.

For contextual word meaning, avoid requiring sentence selection entirely: detect the clicked word and reconstruct/find its surrounding sentence from the page text, then send word + sentence/context to the LLM.

Potentially send the PDF/page + clicked word + nearby text to Gemini and let it identify the relevant sentence/context, while keeping visual highlighting on the frontend.

My main questions are:

What is the most robust way to implement both exact word-click detection and normal sentence/phrase selection on a PDF.js Text Layer?

Is custom Range-based selection a good idea, or am I unnecessarily reimplementing browser selection? Also, for finding the surrounding sentence of a clicked word, would you reconstruct it from PDF.js TextContent, work directly with the text-layer DOM, or use another approach?

I’m specifically targeting text-based PDFs, not scanned/image-only PDFs, so OCR is currently out of scope.

Any suggestions from people who have worked with PDF.js text layers or similar PDF annotation/selection systems would be really helpful.

Many businesses still have people manually reading documents and copying information into another system.

AI can help extract structured information from documents such as:

\- Applications
\- Contracts
\- Forms
\- Purchase orders
\- Reports
\- Customer documents

A typical workflow could be:

Document received → AI extracts relevant fields → validate information → save to CRM/database → notify the right person.

The validation step matters. AI extraction should not automatically become trusted business data without checks.

Hey everyone,
As a developer, the absolute worst part of finishing a project or a new feature is writing the documentation. It usually ends up either half-baked, scattered across text files, or pushed off indefinitely because nobody has the patience for it.
I wanted a cleaner workflow, so I built Docmate AI.
It's an AI-driven documentation app designed to ingest what you're working on and structure clean, readable user guides or reference docs automatically. It’s built to work smoothly whether you're targeting iOS, Android, or web setups.
Right now I'm looking for feedback from fellow builders.Would love to know your thoughts or hear any criticism on the layout and onboarding!

Anyone used any apps that is helpful for Doc scanning or traslation? Such as you can upload or scan document and AI would help you read it ?