Health, home and travel decision

MamaSkin Pregnancy Skincare

A competent developer can recreate the core checker (paste-in ingredient lists, normalisation and rule-based scoring) in about a week, but reproducing MamaSkin's large product catalogue, mobile scanner UX, syncing and alerts is larger operational work best left to the vendor.

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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

$50one-off20 h to build

$0/mo3 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 pastes or scans an ingredient list → app normalises ingredients → rule-based scorer assigns a risk band and score → UI shows flagged ingredients and evidence notes.

What it still won’t have

  • 115,000+ product catalogue and unlimited product search
  • Mobile apps (iOS/Android) and barcode/label scanner UX
  • Saved shelves, syncing across devices and saved-product change alerts
  • Curated product pages and breadth of evidence review across many sources
  • Pro features such as automated alerts and historic change reviews

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

No published price

MamaSkin Pregnancy Skincare 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

—

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What you would spend

What we assumed

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

Runnable build prompt

Not run yet
Build a web-only pregnancy-safe skincare checker using Node.js (Express) + React, Postgres for the ingredient/evidence store, and optional OCR via Tesseract. In scope: a REST endpoint that accepts a pasted INCI string or uploaded photo (OCR), ingredient normalisation (synonym mapping), a rule-based scoring engine that implements band-first logic and returns a 0–100 safety score plus flagged ingredients and short evidence notes, and a responsive React UI to submit input and view results. Out of scope: large product catalogue import, mobile app wrappers, account or syncing features, alerts, or paid-tier gating. Include input validation, error handling, unit tests for parsing and scoring logic, basic integration tests for OCR flow, and a Dockerfile for deployment.
How we checked2 sources · 3/3 runs agreed · evidence score 57

How the score was reached

  • Partly verdict base52
  • 2 cited sources+1
  • 3/3 assessment runs agreed+4
  • Evidence score57

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 · 2

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