AI assistants and search decision

Dooken

A small, single-purpose static ad generator is realistic for one capable developer to build and maintain; there are no evident durable moats on the published page and the core features map to standard APIs and libraries.

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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

$100one-off32 h to build

$50/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 enters campaign brief → AI generates ad copy and assets → assemble into static ad → export/save

What it still won’t have

  • Design and UX polish of the hosted product
  • Proprietary integrations or bundled API keys the vendor may provide
  • Any server-side scale, monitoring, and enterprise features not implemented

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

No published price

Dooken 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

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 static ad generator web app using React (Vite), Node.js (Express), Postgres, and Docker. Core features in scope: prompt form for campaign brief, call an LLM API to generate multiple ad copy variants, call an image-generation API to create creative assets, server-side composition of text+image into downloadable PNG/JPEG, simple user session and Postgres-backed draft storage, and an exports history. Out of scope: training custom models, multi-tenant billing, analytics dashboards, and large-scale autoscaling. Include retry and error handling for API calls, input validation, and unit/integration tests for backend endpoints. Provide a Docker Compose dev setup and deployment instructions.
How we checked1 sources · 2/3 runs agreed · evidence score 78

How the score was reached

  • Build verdict base78
  • Evidence score78

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 · 1

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

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