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Businesses build products with AI in these 4 sourced examples by shipping their own apps with AI coding tools and testing products with agents: a gym replaced a SaaS with its own app, scheduled routines shipped pull requests to an older SaaS, and a startup’s Bot signed up for competitors’ products. Of these, 2 state a time or money result.
2 of these 4 examples state a time or money result. Each figure is the source's own, strongest evidence first. The other 2 describe the change without a figure.
| Use case | Stated result | Evidence |
|---|---|---|
| Ship a product in an afternoon with an AI code editor, then sell sponsor slots | Money$1M in annualized ad revenue reported 17 days later | Strong evidence (evidence grade B) |
| A 112-Member Gym Swapped a $3,600 SaaS for Its Own App With an AI Coach | MoneyA $3,600-a-year subscription replaced by about $300 a year in API calls | Light evidence (evidence grade D) |
Newest first. Each card shows the cost, setup time and skill needed.
Showing all 4
ProductStrong evidence (evidence grade B)
Solo-owned SaaS (Preceden, a timeline maker)
In about a month, scheduled Claude Code routines shipped 1,531 PRs to Preceden, a 17-year-old SaaS, across 20 batched deploys with none reverted. The owner says MRR impact isn't visible yet.
ProductLight evidence (evidence grade D)
Crypto AI-agent startup (MinChi; his X bio: building CoinFello)
A startup founder says Grok Bot's "stalk bot" template signed up for competitors' products, went through their onboarding, compared it with his product's and pointed to where users would drop off.
ProductStrong evidence (evidence grade B)
Pieter told a coding tool to make a flying game, then sold ads in it.
ProductLight evidence (evidence grade D)
Gym (Persistence Athletics, Seattle, 112 members)
Persistence Athletics in Seattle replaced its $3,600-a-year Wodify subscription with a gym app built in Claude Code that runs on about $300 a year in Anthropic API calls and adds photo meal logging and an after-hours AI coach.
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Evidence labels, strongest first:Verified results (evidence grade A)Strong evidence (evidence grade B)Vendor or secondhand (evidence grade C)Light evidence (evidence grade D)How we grade
All 4 examples in one table. Input data is what each example's prompt asks you to fill in (its [brackets]). The last column is the time or money result the source states, or "Not published" when it gives none.
| Workflow / use case | Primary AI role / tool slot | Input data | Expected business output / time saved |
|---|---|---|---|
| Scheduled Claude Code Routines Shipped 1,531 PRs to a 17-Year-Old SaaS in a Month | Claude Code, Codex, GitHub +5 (Autopilot) | [repository]; [dev branch]; [main branch] | Time savedNot published: the source gives no time or money figure |
| A Startup's Grok Bot Signed Up for Competitors' Products and Found Where Users Would Drop Off | Grok Bot (Autopilot) | [5] competitors; [product]; [test email]; [link or notes] | Time savedNot published: the source gives no time or money figure |
| Ship a product in an afternoon with an AI code editor, then sell sponsor slots | Cursor (Assisted) | [product] | Money$1M in annualized ad revenue reported 17 days later |
| A 112-Member Gym Swapped a $3,600 SaaS for Its Own App With an AI Coach | Claude Code, Claude, Expo +2 (Autopilot) | None: the prompt runs as written | MoneyA $3,600-a-year subscription replaced by about $300 a year in API calls |
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