A retailer with no developers shipped six or more changes in weeks, work that had sat in an agency queue.
Carsncards Revenue Operations Agent
Four recurring workflows run against the live store, lifting conversion from 0.8% to 1.1% in 30 days.
Four separate revenue workflows run against the live store on a recurring schedule, not one fix at a time.
What the agent does
Runs recurring error, conversion, performance, and ad-spend workflows against the live store and drafts each fix as a pull request for approval.
- Brand
- Carsncards
- Built by
- Carsncards, in-house
- Contributed by
- Noibu (AI for ecommerce operations)
- Industry
- Retail, Technology
- Audience
- B2C
01
The problem
Why this exists
A single-founder retailer past 1 million dollars in GMV and growing 300% a year has no spare hours and no team. Every improvement competes with running the business.
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An agent without grounding does not help, because it will guess. The question is not what could be improved on a storefront in general; it is which specific thing on this storefront is costing money right now, and whether the last change actually worked.
02
Outcomes
What changes when this is in place
- In a 30-day window the agent cut homepage load time by 25% and raised site-wide conversion from 0.8% to 1.1%, a 37.5% increase, with no added staff.
- Every action is tied to a measured revenue impact on the store, because the agent prioritizes on errors ranked by projected revenue loss, funnel drop-off, Core Web Vitals by page, and revenue per session by traffic source.
- Recurring workflows cover error, conversion, performance, and ad-spend, so the improvement loop runs without being started each time.
Read more
- The founder approves every change before it ships.
03
How it works
Architecture
The agent connects to Noibu over the Model Context Protocol through Noibu's hosted MCP server. It authenticates with the brand's own Noibu credentials and queries the data directly through the open endpoint, with no custom integration built.
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Noibu is the data layer the agent reasons from: errors ranked by projected revenue loss, funnel drop-off, Core Web Vitals by page, and revenue per session by traffic source. Without that grounding the agent would be guessing, and the case study says so directly.
Founder Donny Araujo built and operates the agent himself on his own Claude subscription, running against his own GitHub repository and ad accounts. It drafts fixes as pull requests and his approval is required before anything ships. He assembled it on Noibu's connector and prebuilt agents and added his own recurring workflows on top.
Open standards used
Model Context Protocol (MCP)
Tech stack
Noibu hosted MCP serverNoibu prebuilt agentsGitHubClaude
Resources
Next step
Request an intro
Noibu is behind this solution. Ask about turning error data into revenue fixes, and the MACH Alliance will put you in touch with the person who can answer.
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Everyone credited on this solution
- NoibuAI for ecommerce operations
Every organization named here has a page listing the rest of its work on the Hub.
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Verification
Published case study
The detail on this page is drawn from a case study published by the contributor or the MACH Alliance. Any figures on this page are the ones that case study states.
Where this came from
Solution detail is published from the contributor's 2026 Agent Ready submission to the MACH Alliance. Submitter names and contact details are not published.
Status: Live in productionAdded Jul 8, 2026A listing is an editorial decision, not a MACH Alliance certification.
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