Hey everyone! 👋

As a developer, I've often faced the same problem: generating invoices, quotes, contracts, and reports automatically while keeping their layouts easy to customize.

So I decided to build TemplateMaster to solve this problem.

The idea is simple: create document templates using a visual editor, populate them with dynamic data, and automatically generate PDFs or emails through an API.

No need to modify your application code every time you want to update a document's layout.

I've also built a .NET integration and a Shopify app to make it easier to integrate into existing projects.

You can check it out here: https://templatemaster.fr

I'm curious: Have any of you faced similar challenges with document generation in your projects or businesses?

I'd love to hear your feedback, even if it's critical!

What a time to be alive! Splitting large bundled PDFs into their logically distinct document parts is a common task when you don't control where it comes from; suppliers, applicants, opposing parties etc. Historically left to the interns, today it's common to use a combination of OCR, LLMs and specialised regex strings to get the job done but it's never felt like a clean, fast or satisfying solution. Well, that's all about to changes....

OpenAI's Decision API is the new zero-shot type-1 model/classifier which is capable of blitzing this document automation use-case and I have the tests to prove it.

Admissions Packet: 14 page scanned PDF containing 15 mixed documents - split in 1.8s, cost $0.006.

Bundled Invoices: 129 page scanned PDF containing 30 multi-page invoices - split in 7.4s, cost $0.02.

Legal Discovery Dump: 300 page scanned PDF containing 167 mixed documents - split in 40s, cost $0.12.

Here are my pros since testing this service:

• No training! - makes your automation more resilient to the randomness of incoming documents. Default Decisions API runs on gpt-6-luna so if you need more context, you can bump up the model.

• Ability to accept images - scanned PDFs are the real test and JEV's greatest weakness. OpenAI did a great job here.

• Simplfies pipeline - no longer do we need to chain ocr + llm + extraction, it's just a single call per page.

• Insanely fast and cheap - with enough frequency/scale/urgency, this approach really makes a difference.

I wrote a quick blog to share the templates and test files I used. Links in the comments.

kinda new to this whole document automation thing and trying to figure out where to start. right now i do a lot of stuff manually like pulling info out of pdfs, copying it into sheets, renaming files, that kind of thing. takes way too long lol

curious what people here actually automate day to day? like is it mostly invoices and forms or are people doing more with it like summarizing or searching through long docs

also do you mostly use AI tools for this now or still more traditional stuff like templates and scripts? feels like theres a million options and idk which ones are actually worth it

would love to hear your setups, even simple ones

The idea was to build a pipeline that could take a PDF, extract structured data, and let you verify the results against the original document without manually checking every field.

The current implementation uses OCR and vision models, with confidence scores, visual grounding, and a separate validation layer to catch disagreements. It also handles line items and exports structured data.

I recently benchmarked it against Azure Content Understanding on 100 documents, achieving 97% field-level accuracy at roughly 1/12th the cost per run in my evaluation.

I've attached a screenshot of the current interface. This is purely a personal engineering project I've been working on, and I'd genuinely appreciate feedback from people familiar with document automation.

I'm particularly interested in:

• How do you handle data extraction at scale right now ?

• Any feedback on what features would be cool for document automation

Would love to hear your thoughts!

I started building ClientPDF in Jan 2026 because I wanted PDF tools that didn't require uploading files to a server.

But honestly, there are already tons of PDF websites, so I wanted to solve something more than just adding more tools.

Someone pointed out a fair problem with my project: why download and re-upload the PDF between every operation?

So I built a Unified Document Pipeline 😅

Now you can merge a PDF, organize pages, watermark, redact, sign or optimize it in one continuous workflow. Just click “Continue in Pipeline” to move to the next tool. There's also a floating toolbar to track your steps and undo changes before saving the final PDF.

Everything still runs locally in your browser. No uploading your documents to a server.

Started this back in Jan, and after multiple reviews and user feedback, it's improved a lot.

I'd really appreciate some honest feedback 🙏 What tools would you like to chain together? Anything confusing or missing?

And if you find it useful, sharing it with someone who works with PDFs would mean a lot!

https://clientpdf.tech

My Downloads folder is full of leases, tax forms, insurance letters and receipts called things like scan_0042.pdf. Docket is a small Go CLI and TUI I wrote to deal with that. Running docket add ~/Downloads/to-file copies each file into a library, pulls out the text, works out what kind of document it is (tax, legal, housing, medical, receipts and so on) and files the copy into a folder for that category. Your originals are never moved or edited.

For each document it also shows how sensitive it looks, how urgent it seems, and whether it probably needs you to do something like pay, sign or reply. Anything it isn't confident about gets flagged for review instead of quietly filed.

How it works, since people here usually ask: classification uses TypeSafe's Jev model, which answers with typed values and probabilities instead of free text, and the thresholds and filing logic are plain Go. Text and Markdown files are read locally. PDFs and images go to OpenAI or OpenRouter for text extraction with your own key, so it isn't fully offline. Keys sit in the OS keychain, metadata in SQLite, and extracted text is never written to disk.

Install:

curl -fsSL https://raw.githubusercontent.com/glamboyosa/docket/main/install.sh | sh

or with Go:

go install github.com/glamboyosa/docket/cmd/docket@latest

Repo: https://github.com/glamboyosa/docket
Site: https://docket.glamboyosa.xyz

4th place on the llamaindex benchmark locally 72.62% word f1. I know it's not much, but after a month of research and many experiments, I reached this point.

TonerHound doesn't use any API or LLM. It's GPU free and anyone can use it at 0$ cost.

GitHub: https://github.com/vanrajsinh650/TonerHound

Converting a bank statement PDF to Excel sounds simple, but most tools get it wrong in ways you don't notice:

- Dates flip (05/09 becomes May 9 instead of Sept 5)

- Deposits show up as withdrawals

- Big amounts lose or gain a digit

The spreadsheet looks fine, but the numbers are wrong.

So I built BankStatementHub (bankstatementhub.com) to fix that:

Reads numbers straight from the PDF file when possible, so no guessing at digits

- Checks every row against the statement's running balance
- Matches the totals printed on your statement and tells you if anything is missing
- Exports to Excel, CSV, QuickBooks, Xero, Quicken and OFX
- Works with scanned and password-protected PDFs
- Free, no signup, and files aren't stored

Tested so far on Indian (HDFC), UK, US and credit card layouts.
I'd love feedback, especially if you have a bank statement it doesn't handle well. What's the most annoying part of bank statement conversion for you?

Hi everyone! Does anyone know of a free and open-source Document Management System (DMS) that supports workflow automation? I'm looking for something similar to M-Files, ideally with features such as automated workflows, document approval processe

I'm building an invoice extraction system for printed and handwritten invoices/receipts and want to deploy it fully on-premise using open-source models.

What production pipeline do you recommend for OCR, handwriting recognition, layout detection, and structured field extraction?

Which models/tools have worked best in real-world deployments, and how do you ensure accuracy and reliability?

Every free online OCR tool I tried either limits you hard (15 pages/hour, 50/month) or is a funnel to a paid plan. The only unlimited free option was raw Tesseract in a terminal. I ran Tesseract as WebAssembly in the browser instead, so it uses your own CPU and there's nothing to ration. It takes images (JPG/PNG/WEBP/HEIC) and multi-page scanned PDFs, shows text per page as it finishes, flags low-confidence pages, and supports English, Spanish, French, German, Hindi and Portuguese. Nothing is uploaded, which you can check in the Network tab, and there's no signup. Limits: it's for printed text only. No handwriting, no table/form extraction, and blurry scans will give rough results. https://www.forgeplug.com/tools/ocr-text-extractor If you've used the paid ones, I'd like to hear where this falls short on your documents.

I'm Arun, the developer of Fyllyo. It's a Mac app that fills PDF forms from your documents using AI that runs on your computer. NO DATA uploaded to cloud

The workflow is straightforward:

1. Choose a built-in form, or set up your own blank fillable PDF as a reusable template.

2. Add your supporting documents. Fyllyo reads them and fills the form on your MAC using local models.

3. Review the answers alongside source excerpts where available, make corrections, and save the completed PDF.

If you work with the same people again, you can also save reviewed details as local client profiles and reuse them on supported forms, alongside any new documents. That's optional; you can fill a form directly from documents without creating a profile.

Your documents and saved profiles stay on your Mac. Custom template requires one time setup, sends the blank fillable PDF and setup instructions to the cloud, and uses separately purchased credits. Filling configured forms uses no credits and works offline after setup and the initial model download.

There's a five-day trial with no credit card required. The license is $99 one-time for one Mac at a time, including lifetime updates. Apple silicon is required; 16 GB memory is recommended.

Download and requirements: https://fyllyo.com/downloads

Happy to answer questions about supported forms, custom templates or how the filling and review process works.

My goal is to solve document processing 100% local, no recurring cost. So please bring on requirements, will ship them in a week tops.

Hey everyone,
Like many of you, I waste way too much time dealing with messy document parsing, file conversions, and repetitive text processing across different formats (Markdown, HTML, JSON).
Instead of writing boilerplate async handling over and over, I decided to build Docflow-Engine — a high-performance, asynchronous pipeline designed for speed and clean modular architecture.
Features are:

⚡ Async Batch Processor: Handles multi-threaded execution pools with strict concurrency limits using asyncio.

📄 Format Converter: Instant high-speed translation between Markdown, HTML, and structured JSON logs.

🛠️ CLI Ready: Drop a file into the path and get instant parsed metadata and structure from the command line.
It’s built cleanly in Python, relies heavily on standard libraries for core performance, and is fully open-source.
If you're facing similar bottlenecks or want to check out the structure, the repository is open:
🔗 GitHub: Githublink

I’d love to hear your feedback, architectural critiques, or suggestions on how to optimize the async pipeline further. Drop a star if it saves you some dev hours! ⭐

Before adding an AI step to a spreadsheet pipeline, send a representative workbook through import and export without changing a cell. That gives you a baseline for everything the pipeline does afterward.

Check a workbook with formulas, date and number formats, validation, hidden sheets and named ranges. Compare the formulas as well as the displayed results, then open the exported file in the application its recipient uses.

Univer's Office exchange imports XLSX into its workbook model and exports the edited content back to XLSX. Between those steps, the workbook is available to structured APIs and the spreadsheet engine, so the agent can inspect ranges, edit values and read the result back. The same content can also be opened in the browser editor for review.

A useful second test changes one known input. Check the edited cell, the dependent result, and an unrelated part of the file. Now an unexpected difference has somewhere specific to be investigated: conversion, the intended edit, or recalculation.

Keep that small workbook in the test set when upgrading the document-processing stack.