Opportunities › AI integration into development workflows › Proposed app
Rungate
Run AI review, QA and rewrite checks as repeatable steps in your pipeline
- Score
- 48
- confidence 38
- Evidence
- 3
- items on the page
- Rivals
- 0
- named in sources
- First version
- 35 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2021
- Emerging market · AI estimate
ScreensConcept
Desktop app: Setting up repeatable AI checks, managing gates, and reviewing findings is engineering infrastructure work done at a development workstation. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Checks
All checks active3 checks
About this appAI proposal
Rungate turns the AI checks your team now does by hand into saved, repeatable steps. Define a check once, such as a QA review of a pull request, a progress summary or a rewrite assessment. It then runs on every build with the same prompt and inputs, and the result is stored.
Findings go to the developer who owns the change, as a pull request comment or a task. Nobody copies notes out of a chat window. Each run keeps its prompt, model, inputs and output, so a manager can see what the AI said and why a gate passed or failed.
It is built for engineering managers and QA leads at teams that already use LLMs. Rungate does not replace your CI. It adds a layer for AI work that is reproducible, reviewable and owned by the team.
- Tagline
- AI checks your team can repeat, review and trust.
- Voice
- PlainPreciseCalm
- Why this name
- A run is one execution of a check, and a gate decides what passes. Rungate joins the two: AI work that runs on schedule and clears a gate.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Saved AI checks
Write a check once with its prompt, model and scope. Run it on every pull request or build instead of pasting code into a chat.
No member yet
Findings routed to owners
Results post to the pull request or create a task for the right developer. No manual handing over of notes.
No member yet
Run history and audit trail
Each run stores the prompt, model, inputs and output, so any result can be reviewed or repeated later.
No member yet
Review gates
Mark a check as advisory or blocking. A human can approve or override a blocked build, and the override is logged.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#12372FDeep Pine · primary#F6F3ECPaper · surface#1B1F1EInk · text#E0902BSignal Amber · accent#5E8C61Moss · data pass#B5412FBrick · data fail
Typography
Aa
Rungate
PricingAI proposal
The material gives no competitor prices. These tiers are a starting point to test, following the per-developer subscription idea in the opportunity.
Starter
Free
- 1 repository
- 3 saved checks
- 100 runs a month
- 7 days of run history
Team
$19 a month
- Per developer
- Unlimited repositories and checks
- Blocking gates and overrides
- 1 year of run history
- Task and PR routing
Enterprise
Contact us
- Single sign-on
- Unlimited audit retention
- Bring your own model keys
- Custom contract
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
- Shared outputs. Findings and run records post as pull request comments and shareable report links that developers, managers and executives open without an account. Each carries the Rungate name.
- Do they become customers?
- Viewers are often developers and managers at the same company, so some may become users. Test whether a shared run link leads a non-user to create a workspace.
- First customers
- Engineering managers and QA leads at teams of 10 to 50 developers who already use Claude or similar tools by hand for code review on GitHub.
- Where to reach them
- Hacker News: Share a write-up on replacing manual AI notes with repeatable checks, and join threads where teams describe the problem.
- Direct outreach: Message QA leads and engineering managers who post about AI use in review, and offer a free pilot on one repository.
- GitHub app listing: Publish a free-tier app so teams can try one check on a repository in minutes.
- First milestone
- Five teams run a saved check on live pull requests for four weeks, and two agree to pay for the Team tier.
Why people already pay for this
- Public claimQA teams are using Claude reactively to review code and passing notes manually to developers instead of automating the process
- Public claimTeams propose unnecessary technology rewrites based on AI claims without proper validation or business justification
- Public claimTeams invent ad-hoc solutions for basic needs like progress tracking instead of using structured tools
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 · Job runnerQueue-based background workersChecks run for minutes on each build and must retry without blocking web requests.
- This app · Git integrationGitHub and GitLab apps and webhooksNeeded to read changes and post findings to pull requests.
RisksAI inference
- AILLM output varies even with a pinned prompt, so repeatable may mean only recorded and comparable. Teams may distrust blocking gates until false positive rates are shown.
- CommercialCI vendors and code hosts may add built-in AI review, leaving a thin gap. The evidence is only public claims from one forum and needs interviews.
- DataSending source code to model providers worries security teams. Buyers will ask about retention, key ownership and what is stored.
- PlatformDepends on GitHub and GitLab app approval, API limits and model provider terms, which can change.
- TechnicalLarge repositories need careful context selection to keep runs fast and cheap.
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.