PayFlow —First Payment Management System Submission for the Xano AI-Powered Backend Challenge

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

What I Built

PayFlow is a modern, AI-first payment management system that allows users to:

Create accounts and authenticate securely

Manage digital wallets

Top up balances using real Stripe payments (test mode)

Send money to other users instantly

Track transactions with a full audit trail

Operate in a multi-account/team environment

The problem PayFlow solves is building a scalable, production-ready payment backend quickly, while still applying human engineering judgment on security, data modeling, and workflows.

Instead of relying purely on AI output, I used XanoScript to generate an initial backend, then refined and hardened it inside Xano to meet real-world payment and security expectations.

Demo

🔗 Live App:
Payflow system

Test Credentials
``
Email: [e-mail]
Password: password123

Stripe test card
4242 4242 4242 4242

Expiry date:

Any future date (e.g. 12 / 34)

CVC:

Any 3 digits (e.g. 123)

ZIP / Postal code:

Any 5 digits (e.g. 10001)
`

The AI Prompt I Used

`
Generate a backend system for an AI-enhanced Wallet & Payment Platform using Stripe.

Include the following:

Database Tables:

• Users (Auth-enabled)
• Accounts (team/multi-user support)
• Wallets: user_id, balance, status, created_at
• Transactions: wallet_id, type (credit/debit), amount, status, reference, description, created_at
• Transfers: from_wallet_id, to_wallet_id, amount, status, description, created_at
• Event Logs: user_id, account_id, action, metadata, created_at

API Endpoints:

1. User authentication (signup, login, me)
2. Create and fetch wallets
3. Create Stripe payment flow to top up wallet balance
4. Process Stripe payment and record transactions
5. Transfer money between wallets
6. Fetch transaction history
7. Account and team management
8. Event logging for auditing actions

Logic Requirements:

• Secure JWT authentication
• Input validation for all monetary values
• Prevent negative balances
• Create pending transactions before confirmation
• Update transaction status on success or failure
• Stripe API integration with environment variables
• Clean, modular backend logic with comments

Output:

• All database schemas
• API endpoints with example logic
• Secure and maintainable structure

`

How I Refined the AI-Generated Code

After importing the generated backend into Xano, I manually refined and hardened it to make it production-ready.

🔧 Key Improvements I Made

1. Database Normalization

Before (AI-generated):

Loose field typing

No clear status enums

Redundant relationships

After (Refined):

Strongly typed fields (decimal balances, enums for status)

Clear wallet → transaction → transfer relationships

Centralized EVENT_LOG table for auditing

1. Secure Payment Flow

Before:

Direct charge logic without full validation

After:

Stripe tokenization via frontend

Backend-only Stripe secret usage

Explicit success/failure handling

Wallet credited only after verified payment

Transaction references stored for traceability

1. Wallet & Transfer Safety

Before:

Basic debit/credit logic

After:

Balance checks before transfers

Atomic wallet updates

Sender + receiver transaction records

Clear transfer lifecycle (pending → completed)

1. Event Logging & Audit Trail

Before:

No reliable audit history

After:

Centralized EVENT_LOG

Every critical action logged (payment, transfer, login)

Enables observability and debugging

1. Cleanup & Maintainability

Removed unused AI-generated agent tables

Deleted non-existent endpoints

Consolidated documentation into:

My Experience with Xano

Xano made it possible to move from idea → production-grade backend extremely fast.

What stood out most:

• XanoScript AI generation saved hours of boilerplate work
• Visual workflows made logic easy to reason about
• Built-in auth, background jobs, and integrations simplified complex flows
• Refining AI output inside Xano felt natural and powerful

Challenges & Learnings

While working with the XanoScript extension inside VS Code, I learned that backend generation is not a live-sync process.

The AI-generated output serves as a starting blueprint, but API groups and endpoint wiring must be finalized inside the Xano dashboard. I had to manually create API groups and attach refined logic to ensure correct authentication, validation, and Stripe handling.

Additionally, the API base URL changed during iteration due to Xano’s environment-based deployment and API versioning model. This required updating frontend environment variables during testing, reinforcing the importance of environment management in real production systems.

This process highlighted the distinction between AI-assisted generation and human-led system refinement, which ultimately resulted in a more secure and maintainable backend.

Tech Stack Summary
Frontend

Next.js 16 (Turbopack)

React 19

Tailwind CSS + shadcn/ui

Stripe.js & React Stripe Elements

React Hook Form + Zod

Deployed on Vercel

Backend

Xano (Database, Auth, APIs, Logic)

XanoScript (AI backend generation)

Stripe API (test mode)

JWT Authentication

RESTful architecture

PayFlow demonstrates how AI-generated backend code can be transformed into a secure, scalable, real-world payment system through thoughtful refinement using Xano.

Thanks to the Xano team for building such a powerful platform

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