How I Built Pathway AI: A Full-Stack SaaS Platform in 1 Month

A developer built Pathway AI, a full-stack SaaS platform for international students, in one month using Next.js, Supabase, and TypeScript. The platform features student profiles, visa checklists, SOP generation, resume building, and destination comparison, with rule-based guidance and optional AI enrichment via Gemini API in development mode.

I built Pathway AI , a SaaS platform for international students planning their study abroad journey, in roughly one month. Here's how I went from concept to live product using Next.js, Supabase, and TypeScript. International students face overwhelming challenges when planning to study abroad: fragmented information, biased agents, unclear visa requirements, and no clear way to compare destinations. I wanted to build something that solved this. Used Supabase Auth with Google and GitHub OAuth. This approach prevents spam and ensures data quality: js // lib/supabase.ts import { createClient } from '@supabase/supabase-js'; export const supabase = createClient process.env.NEXT PUBLIC SUPABASE URL , process.env.NEXT PUBLIC SUPABASE PUBLISHABLE KEY ; Built around student profiles and their journey: profiles → stores all student data destination, budget, course, goals visa checklists → tracks document completion sops → stores Statement of Purpose drafts resumes → resume builder data countries / cities / courses → reference data for exploration Row-Level Security RLS ensures students only see their own data. Students fill their profile → system generates a Statement of Purpose draft using structured templates. // lib/sop-generator.ts export function generateSOP profile: Profile : string { return Dear Admissions Committee, I am applying to ${profile.course name} at ${profile.preferred university} in ${profile.target destination} because... ; } Similar approach — profile data → formatted resume sections → PDF export. Core system provides personalized guidance using rule-based logic tailored to student profile + target destination. Production: Always uses reliable rule-based engine — fast, cost-free, predictable responses. Self-hosted / Development: Optional Gemini API integration available. If you run this locally and add a Gemini API key, the Advisor can enrich responses with real AI personalization. This is gated behind a NODE ENV=development flag — production deployments never call AI APIs. Students track required documents for their target country. Sections stored as JSON, items marked complete as they prepare. Browse destinations side-by-side, compare costs, safety scores, job prospects, PR pathways. Why Supabase over Firebase? Why Vercel? Why TypeScript everywhere? Vercel: Automatic deployments on git push. Environment variables stored securely. Sentry: Catches errors in production. Configured at build time. Upstash Redis: Rate limits API calls contact form, future exports . Challenge 1: Handling complex JSONB data Storing skills, experiences, languages as JSON meant flexible schema but harder migrations. Solved by keeping schema versions in mind from day one. Challenge 2: Ensuring data consistency With RLS policies, had to test extensively to prevent data leaks. Built a checklist: test as authenticated user, test as different user, test with no auth. Check it out: pathway-ai-olive.vercel.app https://pathway-ai-olive.vercel.app/ GitHub: github.com/krishna-builds-dev/pathway-ai https://github.com/krishna-builds-dev/pathway-ai Have you built a SaaS from scratch? Drop your stack and lessons in the comments below I'm a full-stack developer focused on Next.js + Supabase. Open for freelance work. Follow on GitHub https://github.com/krishna-builds-dev or LinkedIn https://www.linkedin.com/in/krishna-builds-dev/