Opportunities › AI-powered end-to-end testing for pull requests › Proposed app
Flowcheck PR
AI that runs your user flows on every pull request, before merge
- Score
- 55
- confidence 42
- Evidence
- 5
- items on the page
- Rivals
- 3
- named in sources
- First version
- 45 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2025
- New market · AI estimate
ScreensConcept
Desktop app: Developers review pull request tests, watch flow recordings and approve changes—code review and merge decisions made from a desk with a keyboard. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
PR #482 analysis
Running tests4 flows
About this appAI proposal
Flowcheck PR reads your codebase and each pull request, works out which user flows the change could affect, writes end-to-end tests for them and runs them in a browser. Results and recordings are posted back to the pull request before anyone merges.
It is built for teams shipping web apps where a broken checkout, login or invoice is costly. Instead of reviewing only file diffs, reviewers see what a real customer would see, for different user types and edge cases, not just the happy path.
The agent does one job: find the affected workflows and verify them. Public claims in the material say general-purpose models miss workflows that a purpose-built QA agent finds. This product targets that gap.
- Tagline
- Test the flows your customers use, before you merge.
- Voice
- DirectCalmPrecise
- Why this name
- Flow is the user path being tested; check is the verdict; PR is where it lands. Plain and easy to say.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Diff-to-flow mapping
Reads the pull request and your code to list which user workflows the change touches, including ones far from the edited files.
No member yet
Generated end-to-end tests
Writes tests per affected flow and runs them in a real browser against a preview build. No scripts to maintain by hand.
No member yet
Recordings in the PR
Every run posts a pass or fail summary and a video of each flow, so reviewers can watch the behavior.
No member yet
User-type coverage
Runs flows as new, returning, admin or restricted users, and tries edge cases like empty carts or expired sessions.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#0F4C4ADeep Teal · primary#F7F5EFPaper · surface#1B2422Ink · text#E8A317Signal Amber · accent#C0392BFail Red · data: failure#2E8B57Pass Green · data: success
Typography
Aa
Flowcheck PR
PricingAI proposal
The material gives no competitor prices. These tiers, based on pull requests analyzed and repositories as the opportunity suggests, are a starting point to test.
Free
Free
- 1 repository
- 20 pull requests analyzed
- Recordings kept 7 days
Team
$199 a month
- 5 repositories
- 300 pull requests analyzed
- Critical flow merge checks
- Recordings kept 30 days
Scale
$599 a month
- 20 repositories
- 1,500 pull requests analyzed
- Custom user types
- Recordings kept 90 days
Would you pay Team · $199 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
$194,000
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
- Visible inside customers’ products. Every pull request comment and status check carries the product name and a link to a recording. Developers and reviewers who see it in their repository can open the report.
- Do they become customers?
- Viewers are teammates and often the buyers' peers, but a comment alone may not convert. Test whether outside contributors or other teams in a company install it after seeing a report.
- First customers
- Engineering leads at 10 to 50 person SaaS or fintech teams using GitHub, with checkout, auth or billing flows and heavy AI-assisted coding.
- Where to reach them
- Hacker News and dev forums: Share a write-up on regressions found in open-source apps' pull requests, with recordings.
- Direct outreach: Message engineering leads at payment-heavy startups offering a free run on their last 20 pull requests.
- First milestone
- Five paying teams in one vertical, each running it on most pull requests for a month.
Why people already pay for this
- VerifiedHacker News post received 58 points and 26 comments
- Public claimTeams lack testing of real user behavior before code merge, allowing regressions in critical workflows to reach production.
- Public claimA construction tech customer's invoice flow regression went undetected until Canary identified a drift of approximately $1,600 in calculated amounts.
- Public claimPurpose-built QA agents significantly outperform general-purpose foundation models on code verification tasks.
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 · Browser test runnersHeadless browser execution serviceTests need real browsers on preview builds and video capture, which Vercel functions cannot run.
- This app · Code host integrationGitHub app for PRs and checksReads pull requests and posts results and merge checks.
RisksAI inference
- AIGenerated tests may be flaky or wrong, giving false failures that teams learn to ignore. Needs a review step and measured precision on real pull requests.
- TechnicalRunning a customer's app needs preview environments, seed data and test accounts. Setup per team may be heavy.
- CommercialA purpose-built QA agent named in the material already exists, and general models improve quickly, so the edge may shrink.
- DataCustomers must grant access to source code and test credentials; security review may slow sales.
- PlatformDepends on code host APIs and model providers for cost and availability. Per-run costs could squeeze margins.
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.