ReceiptGenie

"An iOS app that organizes and tracks receipts using AI-powered scanning, born from the creator's personal receipt management tool."
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receipt-genie.com
Maker: lhr0909
Over $200 MRR; exceeded $500 in December; launched in May of this year

Marketing Channels

Primary

Apple App Store

Listed on the iOS App Store, providing organic discovery through search

Secondary

Hacker News

Shared in HN thread to reach tech-savvy users who track receipts

Ongoing

Product website

receipt-genie.com serves as a landing page for the app

Growth Levers

  • Migrate to open-source VLMs (e.g., Qwen3-VL) to reduce per-scan costs and improve margins
  • Build an Android version to double the addressable market
  • Target small business owners and freelancers who need receipt tracking for tax deductions
  • Add export features (CSV, accounting software integration) to capture professional users
  • Leverage the multi-language and currency detection capabilities for international market expansion
  • Create content marketing around tax season receipt organization to capture seasonal search traffic

First Customer Strategy

The creator built a private receipt scanning website for personal use, then realized others would benefit from a mobile app version. The transition from personal tool to iOS app in May provided a natural product-market validation, with the App Store serving as the primary discovery channel.

Pricing Insight

Subscription-based model (MRR indicates recurring revenue). The app reached $200 MRR within months of launch and crossed $500 in December, suggesting growing traction and reasonable price point for individual users.

New Market Opportunities

  • International users The LLM-based scanning can guess currency and timezone and translate receipts, opening up non-English markets
  • Cost-conscious automation builders Interest in the OCR/LLM approach suggests others building similar tools want scalable, affordable receipt processing

Key Takeaways

  • • Building a tool for your own pain point (receipt tracking) and then packaging it as a consumer app is a reliable path to product-market fit
  • • LLM-based processing can be cheaper and more capable than traditional OCR services, especially when you extract multiple data points in a single prompt
  • • Open-source vision-language models (Qwen3-VL) are approaching commercial model quality, offering a path to further cost reduction
  • • Providing a UI for users to edit AI-extracted data keeps users satisfied even when accuracy is not perfect
  • • Mobile-first apps with subscription pricing can reach meaningful MRR quickly when solving a universal pain point like receipt management

Sentiment Analysis

1 Neu

Notable Quotes

"What do you use for OCR, I wonder. If it's LLM isn't that too expensive (or flaky if cheap) to be scalable? — aristofun"
"It is actually cheaper and more scalable because it is accurate enough and I can also get more information with a single prompt. — lhr0909"

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