Opportunities › Mobile app testing for development teams › Proposed app
Plainrun
Plain-English mobile tests that live in your repo and run by sight
- Score
- 45
- confidence 49
- Evidence
- 7
- items on the page
- Rivals
- 0
- named in sources
- First version
- 45 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2011
- Mature market · AI estimate
ScreensConcept
Desktop app: QA and development teams write, review, and debug tests in long sessions at their desks using repositories and CI dashboards. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
checkout.spec
Saved in repo- Step 1Open the cart
About this appAI proposal
Plainrun lets mobile teams write tests as short plain-English specs that sit in the app repository next to the code. A vision-based agent runs them on Android and iOS by looking at the screen, so there are no XPath strings or accessibility IDs to repair after a UI change.
When a pull request changes a screen, Plainrun reads the diff and proposes spec updates for review. Specs are versioned with the code, so tests stay in step with the app instead of drifting in a separate document.
It is built for mobile developers and QA engineers, including teams whose features are partly written by AI agents. Generation works from the changed code only, to limit token use and keep it quick.
- Tagline
- Say what the app should do. Plainrun checks it on every build.
- Voice
- PlainCalmPrecise
- Why this name
- Plain describes the English specs; run is what they do on a device. Two short, pronounceable words with a clear meaning.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Specs in your repo
Tests are plain-English files committed beside the code, reviewed in pull requests and versioned with each release.
No member yet
Runs by sight, not selectors
A vision agent reads the screen and acts on intent, so renamed IDs and moved buttons do not break the run.
No member yet
Diff-based test proposals
On each pull request, Plainrun reads only the changed code and suggests new or updated specs to approve.
No member yet
Android and iOS, one spec
Write a flow once in English and run it on both platforms without separate scripts.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#1F6B4FFern · primary#F7F4ECPaper · surface#1F2A24Ink · text#E8833AEmber · accent#2F5D8CSlate Blue · data
Typography
Aa
Plainrun
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Say what the app should do. Plainrun checks it on every build.
PricingAI proposal
The material gives no competitor prices. These tiers are a starting point to test, following the idea of pricing by app size and monthly test runs.
Solo
Free
- 1 app
- Limited monthly runs
- Specs in your repo
Team
$99 a month
- Up to 5 apps
- Pull request proposals
- CI status check
- Android and iOS runs
Scale
Contact us
- Many apps and high run volume
- Private device options
- Priority support
Would you pay Team · $99 a month?
Your answer tells PWAS which prices people accept. It changes no score.
No answers yet.
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$190,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- No loop in the product. Plainrun is used inside a team's own repo and CI. Its results are seen by that team only, so use does not spread by itself. Reach comes from developer content, open demos and direct outreach.
- Do they become customers?
- Few outsiders see it, so growth must be tested through sales and content. An open example repo with specs could be tested as a way to bring in developers who then try it.
- First customers
- Small teams of 2 to 10 developers shipping one Android and iOS app, who already use AI coding tools and have no stable UI test suite.
- Where to reach them
- Hacker News and dev forums: Post a demo showing a spec repaired after a UI change, with a public sample repo.
- Mobile developer communities: Offer free setup to teams in mobile Slack and Discord groups in return for feedback.
- Direct outreach: Message founders and mobile leads of small app studios with a short recorded run on their app.
- First milestone
- Ten teams run Plainrun on a real app for four weeks, and five of them pay for the Team tier.
Why people already pay for this
- VerifiedHacker News post received 28 points and 15 comments
- Public claimTest flows kept outside the codebase go out of sync with the app and lose reliability over time, requiring constant effort to keep them updated.
- Public claimTraditional selector-based testing using XPath or accessibility IDs is brittle and unreliable.
- Public claimVision-based agents can successfully understand app screens, understand intent, and perform actions across Android and iOS.
- Public claim
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Vision modelHosted multimodal model APIThe agent must read screenshots and decide on actions.
- This app · Device executionEmulators and real-device cloudTests need Android and iOS devices to run on.
RisksAI inference
- TechnicalVision agents may be slow or flaky on long flows, and run results may differ between runs. Teams will not trust a suite that fails at random.
- AIGenerating specs from code context costs tokens and time, and may propose wrong or redundant tests that reviewers must filter.
- CommercialRun costs for model calls and devices may exceed what teams will pay per month, so margins at the proposed prices are untested.
- PlatformiOS device access is restricted, and running on real iPhones at scale depends on third-party device clouds.
- DataReading private source code and app screens raises security concerns that may block adoption in larger companies.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.