Finance and accounting decision

Receipt Genie

A capable developer can build a useful self-hosted receipt scanner (uploads, OCR parsing, tagging, exports) within a multi-week effort, but reproducing the vendor's claimed accuracy, multi-language coverage, and native iOS polish at product quality is unlikely without their ongoing models/data and mobile UX work.

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Built by Simon Liang, who ships 3 products in this index

You pay

Not priced

No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.

You’d pay instead

$100one-off60 h to build

$15/mo6 h/mo upkeep

No published price to break even against.

No open-source build does this yet

Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.

What a replacement has to do

User uploads or photographs a receipt → OCR extracts structured line items and totals → automatic currency/tax detection and tag suggestions → store receipt and allow CSV/PDF export

What it still won’t have

Vendor-claimed 95%+ OCR accuracy and ongoing model improvements
Multi-language and 50+ currency detection coverage as advertised
Polished iOS app and App Store distribution (native UX and permissions)
Commercial support, soft-limit handling, and account review workflows
Built-in audits/enterprise polish and any proprietary training data

What remains hard

Product polish and ongoing maintenance

Read the build prompt

First-year cost

No published price

Receipt Genie does not publish a price we could read, so there is nothing to compare against. What building costs is below.

Money you would actually spend

Keep paying

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Subscription price × seats × 12

Build it

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AI build —APIs + hosting —

Time you would spend

—

What you would spend

Your monthly price$/moAI to build it$ onceAPIs + hosting$/mo

What we assumed

Initial buildhrsMonthly upkeephrsYour rate$/hr

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet

Build a minimal self-hosted receipt scanner and tracker as a web app using React (frontend), Node.js + Express (API), Postgres (storage), and Google Cloud Vision (or Tesseract) for OCR. In scope: image upload with auto-crop, OCR-to-structured-line-item parsing, currency and regional tax detection, simple rule-based tag suggestions plus a small ML classifier endpoint, store receipt images and structured items in Postgres, CSV and PDF export endpoints, user auth (email) and a single-user UI for viewing/searching receipts and generating reports. Out of scope: native iOS App Store packaging, enterprise account management, training large proprietary OCR models, multi-tenant billing. Include input validation, retries for OCR API calls, error handling, automated tests for parsing and export features, Docker deployment, and a README with setup and maintenance steps.

How we checked4 sources · 3/3 runs agreed · evidence score 59

How the score was reached

Partly verdict base52
4 cited sources+3
3/3 assessment runs agreed+4
Evidence score59

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.

How scoring works →

Cited sources · 4

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ 3 independent runs, one answer✓ Citations limited to fetched pages! 1 moat recorded