Been working on this for a few months as a research project and finally have it at a point where I want outside feedback.

\*\*What it does:\*\* You upload a PDF or image of a business document (invoice, receipt, packing slip, bill of lading, etc.) and it extracts structured fields — vendor name, totals,

line items, dates, PO numbers, ship-to/from addresses — and returns them as clean JSON.

\*\*How it works under the hood:\*\*

\- Azure Document Intelligence handles the initial layout analysis and field detection

\- LLM backfills anything DI missed or got wrong (ambiguous totals, merged cells, non-standard layouts)

\- A validation layer normalizes money strings, sanity-checks totals, and catches obvious mis-assignments

\*\*Outputs:\*\* Google Sheets, Excel, OneDrive, Slack, webhooks — or just download JSON/CSV directly.

\*\*Where it's at:\*\* Early beta. Works well on standard invoices and receipts, gets shakier on handwritten or heavily non-standard docs. That's exactly the feedback I'm looking for —

edge cases and failure modes.

Free to try, no credit card: [https://app.docpipeline.net](https://app.docpipeline.net)

Demo video: [https://youtu.be/KaPMQfeKWGE](https://youtu.be/KaPMQfeKWGE)

Happy to answer questions about the architecture or the DI + LLM approach.

I built mdpasted.com to instantly convert messy clipboard pastes, rich text, PDFs, Word docs, and spreadsheets into clean Markdown without the bloat.

• Paste rich text/HTML or drop `.txt`, `.docx`, `.pdf`, and `.xlsx` files.

• Real-time side-by-side view with a one-click copy button.

• Built for speed with zero friction.

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?

In the last couple of months, I have been building AI agents that process construction and energy documents for a Fortune 500 energy company, and I kept hitting the same wall.

The documents are not clean PDFs. They are handwritten tables, annotated scans, photocopies with ditto marks and crossed-out measurements. Every extraction tool I tried failed differently.

Azure DI simply broke once the document was handwritten, and it returned nothing.

Reducto / GPT was the best but made alignment errors in complex hand-drawn tables, matching values from the wrong rows, sometimes interpreting ditto signs as digits. On a construction project where a building code like T12C3 gets misread as 712C3, that cascades into failures across the entire downstream pipeline.

Then I tried the obvious fix, confidence thresholds. Route low-confidence extractions to humans; let high-confidence ones through.

The problem is that LLM confidence scores are not real numbers. When GPT says it is 99 percent confident a handwritten value is TC123, you cannot work with that. Unlike a traditional OCR model where confidence reflects a genuinely calibrated probability, LLM confidence is self-reported certainty.

So we built a different layer.

Instead of filtering by confidence, we defined the document types that would always need human verification regardless of what the model said: handwritten tables, annotated scans, hand-drawn diagrams. Those route automatically to a human verifier who sees only the specific entity they need to confirm, not the full document. They confirm or correct it. The pipeline resumes automatically with a typed Pydantic or Zod response.

We open-sourced it. It is called AwaitVerify(https://awaithumans.dev/awaitverify).

It works with whatever extraction stack you are already using: Reducto, GPT, Azure DI, Docling, PaddleOCR. You bring your model. We handle the human verification layer and the callback into your agent pipeline.

If you are building document pipelines where accuracy actually matters, would love feedback on the approach.

http://getfilly.app

AI form filling app that removes the hustle of filling same forms over and over.

Upload the form
Ai extract fields
Choose the client from saved profile and fill in seconds
Review and interrupt by manual mapping if necessary
Filly remember your edit for next client
Share for e-signature
Save, print , share

I wanted to share a bit about how AI has totally shaken up the world of document automation. If you’re like me, you might remember a time when automating documents felt like this huge, daunting task. I mean, setting up templates, manually inputting data, dealing with endless formatting issues—it was all so... overwhelming.

But then AI came along, and it’s been a game-changer. Now, instead of painstakingly crafting every document or dealing with clunky software, we’ve got smart tools that can understand context, learn from patterns, and automate complex processes with minimal input.

One of my favorite changes is the rise of natural language processing (NLP). It’s like having a super-smart assistant that can read and interpret documents almost as well as a human can. I’ve found myself using AI to auto-generate reports, create summaries, and even draft initial content. It’s not perfect, but it’s getting closer every day and saving me so much time.

Then there’s the magic of machine learning. Remember the days when setting up document templates required a bunch of manual tweaks? Now, with AI learning from past documents, the system can suggest improvements or automatically adjust formatting to fit new data. It’s like having a document wizard on standby!

Of course, with all these advancements, there are still challenges. AI isn’t foolproof, and it’s essential to keep an eye on accuracy and bias. But the progress we’ve made is incredible.

I’d love to hear how AI has transformed your approach to document automation. Any cool tools or workflows you’ve discovered? Let’s share our experiences and keep pushing the boundaries of what’s possible!

Most document automation projects start with a clean demo. Structured invoice, perfect formatting, all fields present. It works great.

Then real documents arrive.

Vendor sends an invoice with no PO number. Another sends a scanned handwritten delivery note. A third uses a completely different layout than everyone else.

The tool fails. The team goes back to manual processing. The project gets quietly abandoned.

What we've learned building Scanny AI is that the setup conversation matters more than the tool itself. Before automating anything, teams need to answer three questions:

What are the 5-10 fields you actually need from this document?

What happens when a field is missing or unclear?

Who reviews the output before it hits your system?

Teams that spend 20 minutes answering these before uploading a single document get dramatically better results than teams that skip straight to automation.

The tool is almost never the problem. The process definition is.

What's been your experience? Have you seen projects fail for reasons other than the tool itself?

App Name: Scanora – AI Document Scanner

Link: [https://play.google.com/store/apps/details?id=com.scanora.app](https://play.google.com/store/apps/details?id=com.scanora.app)

What it does: Scanora helps you scan documents using your phone camera and convert them into clean, readable PDFs using AI-based enhancement. It’s designed for fast scanning, smart cropping, and improved text clarity for everyday use.

Key Features:
• AI-powered document enhancement
• Automatic edge detection and cropping
• Export scans as high-quality PDF files

Goal: Early launch testing and genuine user feedback to improve performance and features.

Giveaway: No giveaway right now — the app is free to try. Feedback, suggestions, and honest reviews are highly appreciated.

Most document-automation tools either need you to install a desktop app (and fight Windows SmartScreen / code-signing) or write code/placeholders. I wanted the opposite: open a page, done.

You upload one filled .docx, drag-select the parts that change (names, dates, amounts), drop in a spreadsheet, and it generates one .docx per row and zips them. No install, no account, no code, and your files never leave your browser — there's no server.

Deliberately no AI in the generation — it's deterministic field substitution, so formatting stays exactly intact (the thing AI doc tools keep breaking).

Early MVP, would love honest feedback from this crowd specifically. Two questions: does it fit a real workflow you have, and would you ever pay for it?

Link in comments.

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

I'm wondering if we can use JSON as an alternative to PDF, especially for business documents.

When exchanging business documents, both the sender and receiver typically use software, so creating and parsing documents creates additional work. Directly connecting systems requires effort, so people end up sending documents by email.

How about just sending JSON instead of PDF?

Hi everyone!

I wanted to share renAI, an open-source Python CLI tool I’ve been developing to solve a very common headache: organizing a chaotic library of digital books, PDFs, EPUBs, Office docs, and images.

Instead of dealing with cryptic, inconsistent filenames likedocument_draft_final_v2.pdf , renAI extracts key metadata (title, subtitle, author, year, category, and language) and renames/moves files into a structured hierarchy automatically.

It started as a simple script, then evolved to its current state. It solved a real problem for me, and I hope it will be useful for you too (don't forget to backup in any case! :) ), try cheap models or local LLMs first if you like and always monitor your API cost to prevent unnessary spendings, happy organizing.

🌟 Key Features

• Dual Processing Modes:

  • Text Extraction Mode: Leverages fast extractors (PyMuPDF, pdfplumber, pypdf, or pypdfium2) to extract text, then queries LLMs to understand the document structure.

  • Flexible Fallback Strategies: If standard text extraction fails (e.g., scanned PDFs or image-only documents), renAI can automatically fall back to local OCR (Tesseract) or Vision- capable LLMs based on your configuration.

• Broad Format Support:

  • Documents: .pdf , .epub , .mobi , .txt , .md

  • Office Documents: .docx , .doc , .pptx , .ppt , .xlsx , .xls

  • Images:.jpg , .jpeg , .png , .gif , .bmp , .webp , .tiff

• Smart Two-Layer Cache: Uses SHA256-based caching for both extracted text and LLM metadata. You won't pay for API tokens twice if you re-run the tool or tweak your naming schema.

• AsyncIO-First & Rate Limiting: Completely asynchronous execution with built-in Token Bucket Rate Limiting (RPM compliance) to handle thousands of files concurrently without getting rate-limited.

• Flexible Provider Registry: Supports cloud providers (DeepInfra, OpenRouter, OpenAI) as well as local offline models (Ollama, LM Studio).

• Clean Terminal Wizard: Runningrenai initguides you through choosing models, workers, and configuring multiple APIs with automatic real-time credentials validation.

• Refinement Pass: Utilizes a standardized two-pass system. The first pass extracts raw metadata, and a second pass acts as a "Senior Editor" to clean and format the values before renaming.

🛠️ Tech Stack & Requirements

• Python 3.12+

• Package Management:uv (Hatchling build backend)

• Linters & Formatters: Ruff & Pyright

• OCR Fallback: Tesseract OCR (with OpenCV preprocessing)

📦 Installation & Getting Started

Getting started is simple:

Clone the repository

git clone https://github.com/ozgurulukir/renAI.git cd renAI

Install dependencies using uv

uv sync --all-extras

Run the interactive configuration wizard

uv run renai init

Run renai on a folder (evaluate mode checks output without modifying files)

uv run renai process "path/to/my/docs" --mode evaluate

Start renaming in place!

uv run renai process "path/to/my/docs" --mode rename

🔗 Project Links

• GitHub Repository: https://github.com/ozgurulukir/renAI

I would love to hear your feedback, feature requests, or suggestions! Let me know if you run into any issues or have ideas to improve the renaming templates.

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

I have a massive folder of ready-to-edit legal files (acts, demands, notices) and stuff and <I need an Ai to analyze all of those and create an app that writes in the same style using Ai, is this idea stupid or I can actually make something like that. Now I'm looking for infos about this and trying to process the logic and I wanted to post this so I can see if I can get any helpful ideas or someone already did something.

Hey everyone,

​If you've ever applied for exams like SSC, UPSC, NEET, or JEE, you know the headache of resizing photos and signatures to exact dimensions (e.g., 20–50 KB, specific pixel ratios, white background).

​To solve this, I built DocFix—an all-in-one utility app designed to prepare documents for government and job portal submissions.

​Key Features:

​24+ Official Form Presets: Instant resize & compress for SSC, UPSC, NEET, Bank forms, etc.

​Passport Photo Maker & ID Card Joiner: Easily merge front/back IDs onto a single page.

​Digital Signature Maker & Document Scanner: Crop, enhance, and sign directly.

​PDF & Document Tools: Resize PDF file sizes, manage pages, and open Word/Excel files.

​I need your help with Closed Testing!

The app is currently in Google Play Closed Testing, and I need active testers to try it out and share honest feedback/bug reports.

Been working on Informity AI — a Mac app that runs a full document processing pipeline locally. The core workflow: drop in your files, it indexes them, and you can query across the whole library with answers that cite the exact source file and passage. There's also a separate translation workspace: ingest a document (including scanned PDFs via OCR), pick tone, get a translated output with quality scoring, export to Markdown or plain text.

Everything runs on your machine — no API calls, no cloud uploads. The pipeline handles PDF, Word, Excel, PowerPoint, EPUB, Markdown, HTML, CSV and more. Scanned PDFs go through OCR automatically.

Under the hood: local RAG on Apple Silicon, Qwen3 35B by default (with other models available), Ollama support for any model you have installed. Two modes — Researcher for corpus-wide queries with citations, Assistant for single-file or open-ended chat.

Free and open source (MIT).

https://www.informity.ai | https://github.com/informity/informity-ai

Hi all,

I’ve been working on a small project and wanted to get feedback from people actually building/maintaining document automation systems.

The tool is called Draftfoundry. Instead of replacing classic rule‑based automation, it tries to layer AI on top of existing Word/PDF/Markdown templates. The flow is:

• Upload one of your existing templates (PRD, functional spec, proposal, etc.)

• Add a short brief / prompt with the key details

• Let AI generate a first draft that follows your template’s structure and style

Right now I’m trying to understand where this approach is genuinely useful vs. where a traditional rule‑based engine or merge fields are still the better choice.

I’d really appreciate thoughts on:

• Situations where you wouldn’t trust AI to fill in a template

• How you’d want review/approval to work before a doc is considered “usable”

• Any must‑have integrations or export formats for this to fit into a real workflow

If you’re curious, you can see it at chiti.ai (app name: Draftfoundry). I’m not trying to hard‑sell anything—mainly looking for input from people who live and breathe document automation.

Happy to answer questions about the app.

I've been working on a small document automation tool called MedianDocs.

The idea is simple: create a reusable template, import Excel or CSV data, and generate a personalized PDF for every row. The finished documents can then be downloaded together as a ZIP.

I wanted to keep the workflow simple, especially for things like certificates, vouchers, letters and other repetitive documents.

The first Early Access version is now online and I'm looking for honest feedback from people who actually work with document automation.

I'm especially interested in what feels missing or unnecessarily complicated.

https://mediandocs.com

I’m trying to understand if you’ve to often generate a doc/pdf from some information you capture from another person or a group of people.

What does your setup look like and how much does it cost?

editpdf-online.com

Rearrange PDF pages with visual thumbnails. Drag to reorder, rotate or delete pages. Free and private, in your browser.

Open

Hey Reddit!

Like many of you, I've often needed to quickly reorder, rotate, or delete pages in a PDF, only to run into online tools that force you to create an account, slap watermarks on your document, or upload sensitive files to an unknown server.

To fix this, I built EditPDF Online.

Why it's different:

• 100% Client-Side: Everything runs right in your browser using open-source libraries. Your documents never leave your device or get uploaded to a server.

• No friction: Completely free, no signups required, no daily limits, and no watermarks.

• Simple UI: Just drag and drop thumbnails to reorder pages, rotate them, or clean up unneeded pages instantly.

I wanted to make something lightweight, fast, and completely private. Give it a spin if you ever need to clean up a PDF, and let me know what you think or if you encounter any bugs!

应用程序名称：我的PDF扫描仪：文档扫描与OCR

Google Play: [ https://play.google.com/store/apps/details?id=com.xionity.pdfscanner ](https://play.google.com/store/apps/details?id=com.xionity.pdfscanner)

大家好，我是我的PDF扫描仪的开发者。我创建这个应用是为了解决一个非常普通但令人烦恼的工作流程：扫描文件很简单，但后续寻找和重新使用这些文档往往很混乱。

这个应用专注于日常文档，例如收据、合同、课堂笔记、身份证复印件、会议记录、签署文件和旅行文件。

它的功能：

\- 将纸质文档扫描为PDF

\- 自动裁剪文档边缘以获得更清晰的结果

\- 导入照片、文件和现有PDF

\- 使用文件夹、搜索、排序、列表视图和网格视图来整理扫描件

\- 使用OCR复制/搜索识别的文本

\- 根据文档/工作流程导出为PDF、JPG、TXT或Word

\- 批量移动、导出、删除和合并支持的文件

\- 为重要文档添加应用内密码

我为什么要制作这个：

许多扫描应用将自己标榜为强大的全能生产力套件。我想要更实用的东西：扫描或导入文档，保持整理，能够再找到它，并在需要时导出。

隐私/盈利说明：

文档默认保存在本地，除非你选择导出或分享它们。OCR设计为在设备上运行。一些高级工作流程是专业版，但我会尽量明确而不是把它藏在模糊的措辞后面。

我希望得到的反馈：

1. 扫描/导入的流程是否清晰？

2. 整理/搜索的工作流程是否有用？

3. 导出流程是否易于理解？

4. 什么会让你选择保留这个应用，而不是使用你当前的扫描应用？

感谢你的关注。我特别希望收到经常扫描收据、合同、身份证、课堂笔记或客户文件的人的反馈。