Xano AI-Powered Backend Challenge: Full-Stack Submission

This is a submission for the Xano AI-Powered Backend Challenge: Full-Stack, AI-First Application

What I Built

I developed a full-stack web application that solves a common problem for students preparing for campus placements: disorganized tracking of applications and preparation tasks. The app provides structured workflows for adding companies, recording job applications, monitoring deadlines, and managing daily preparation tasks. A dynamic dashboard highlights urgent tasks and upcoming application due dates, helping users clearly understand what they should focus on today.

The system is built with a clean data model, responsive UI, and thoughtful workflow design to support both simplicity and future scalability.

Demo

The AI Prompt I Used

Create a web app called “Placement Prep Coach” for final year students preparing for campus placements. Features:

• Dashboard with today’s tasks and upcoming application deadlines.
• Pages: Companies, Applications, Tasks.
• Data model: Company, Application, Task, as described.
• Include forms to add/edit each, and simple lists/tables to view them.

https://placement-pathfinder.onrender.com/

How I Refined the AI-Generated Code

I intentionally used the same prompt for both Lovable and Xano, so the AI generated similar data models across the frontend and backend. As a result, Xano already provided all the CRUD endpoints I needed. My main work was refining the backend models—adjusting field names, relationships, and response formats—to match the frontend expectations. This alignment ensured that both systems communicated reliably without additional API restructuring.

My Experience with Xano

Using Xano was a smooth experience, especially for rapidly building and iterating on backend logic without managing infrastructure. The AI-generated data model was highly aligned with my frontend, which minimized setup time. I found Xano’s automatic endpoint generation, database tools, and no-code function logic particularly helpful for accelerating development.

The main challenge was refining the AI-generated schema to match real frontend requirements—such as adjusting field types, renaming attributes, and reorganizing relationships. Once these adjustments were made, the API became fully compatible with my frontend workflows. Overall, Xano provided a fast, reliable backend foundation and significantly sped up the development process.