Task

Revisitor addresses a practical task: a service for repeat visits and re-engagement built with TypeScript. It features a Node API for scheduling, light analytics, and integrations.

The project is part of commercial development and is actively being developed. It is related to services such as SEO, AEO, and GEO: preparing websites for AI search, growth engineering, and content platforms.

This case study demonstrates the approach to tasks in the area of "SEO, AEO, and GEO: preparing websites for AI search": from setting objectives to delivery with a clear module structure.

Before starting, we established readiness criteria: a clear first screen, predictable form errors, and the ability to make improvements without rewriting half of the codebase.

Solution

A learning yet fully functional implementation has been created with a focus on code readability and reproducible builds.

The scenarios are designed so that the main actions fit within one or two screens: fewer switches, fewer input errors. Navigation and field labels are aligned with familiar patterns—users do not need to "learn the interface."

Particular attention is given to loading states and empty lists: skeleton/spinner where data is retrieved asynchronously, and clear empty-state texts instead of an empty table.

Architecture and stack

Server and data: Node.js.

Infrastructure and utilities: TypeScript.

Language distribution in the repository: TypeScript (68.7%), JavaScript (16.1%), HTML (14.9%), Dockerfile (0.3%).

The layers are separated so that the UI does not know the transport details: API/storage adapters, domain types, presentation components. This simplifies backend replacement and targeted UX tweaks.

Key features

• TypeScript — utilized in product logic: reduces the amount of custom code and speeds up the addition of new screens.
• Node.js — utilized in product logic: reduces the amount of custom code and speeds up the addition of new screens.

Additionally:

• Integration and use of TypeScript in product logic and interface.
• Integration and use of Node.js in product logic and interface.
• User input validation and clear error messages.

Result

The MVP already covers the key scenario; next steps include expanding reporting, tests, and monitoring.

The source code is open: https://github.com/astropsy999/revisitor — you can evaluate the module structure and commit style.

What I would improve

The next step is observability (logs/metrics), regression auto-tests, and API documentation for integrators. For high-load scenarios, profiling bottlenecks and caching reference data would be necessary.