Writing and content decision

unflag

A technical user can recreate the core detection and humanize workflow using existing open-source projects and LLM APIs, but the full mobile App Store product experience (native iOS polish, in-app billing, and any proprietary detection/model tuning) is not fully replaceable without more effort or proprietary models.

View on the App Store
You pay

$6.99/mo

$84/yr

Read off the official pricing page.

You’d pay instead

$100one-off72 h to build

$20/mo4 h/mo upkeep

On cash alone, building overtakes the subscription at 5 seats.

Open-source builds that already do this

Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All unflag alternatives, with the arithmetic →

What a replacement has to do

  • User pastes text → run AI-detection → rewrite/humanize text → optional translate → present results in mobile UI and allow copy/export.

What it still won’t have

  • Polished native iOS App Store experience and discovery
  • Any proprietary detection model or tuned classifier the vendor may use
  • Integrated in-app subscription handling via App Store
  • Mobile polish, analytics, and aggregated usage optimizations

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

Keep paying

Paying is—cheaper in year one.

On cash alone, building overtakes the subscription at 5 seats.

Paid seatsseats

Money you would actually spend

Keep paying
—

Subscription price × seats × 12

Build it
—

AI build —APIs + hosting —

Time you would spend

—

—

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 minimal web+API replacement for UnFlag using Next.js (React) for frontend, Node.js/Express for API, Postgres (or SQLite) for storage, and OpenAI (or compatible) LLMs for detection and rewrites. Core features in scope: paste/import text, run an AI-detection prompt to produce a per-segment confidence score, run a paraphrase/humanize prompt that returns 2 rewrite styles, optional translate using the same LLM, subscription gating (badge or feature-flag only for paid flows), copy/export results, and simple usage analytics. Out of scope: native App Store in-app purchases, custom proprietary detection models, and large-scale mobile optimizations. Include error handling, rate-limiting, retry logic for API calls, and automated tests for the API endpoints and core prompt flows.
How we checked3 sources · 2/3 runs agreed · evidence score 63

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 3 cited sources+3
  • Price verified on pricing page+3
  • Evidence score63

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 · 3

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ Price read off the page! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 1 moat recorded