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Generate polished reports/docs automatically from messy inputs

Hacker News

Generate polished reports/docs automatically from messy inputs

Hi HN, We’ve been working on a project to automate one of the most painful but universal tasks: turning scattered data and notes into polished, professional documents. The idea is simple: You upload the final document you normally produce (e.g., a monthly report, project update, or client deliverable). You also upload the raw inputs you usually work from (Excel sheets, PDFs, Word notes, etc.). The system learns the mapping, so next time you can just drop in the new raw inputs and instantly get the finished document. Example workflow I’ve been testing: Raw inputs: a spreadsheet of KPIs, a staffing note in Word, and a PDF of receivables. Output: a clean monthly business performance report in Word. The goal is to make this general-purpose (works for consultants, engineers, businesses — anyone who spends hours creating repetitive docs/reports). Right now this works best with small–medium files (big inputs may time out). I’m still improving performance, but wanted to share early to get feedback.” I’d love feedback on: What kinds of documents you’d actually want automated. Whether the “teach it once, reuse forever” approach makes sense to you. Any must-have features you’d expect before using something like this. You can try it here: https://gridfusion.ai/ Below are some automations we built for a couple of different consulting companies: Report Generation: https://www.youtube.com/watch?v=wvOiQtIW5P8 Google Earth File Generation: https://www.youtube.com/watch?v=cU0VMgdB06Y Would love to hear your thoughts and critiques!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, new, tasks · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, google, monthly · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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