Scoring Jobs Before You Apply
ShouldApply Case Study
A job-fit scoring platform that grades any listing before you spend an afternoon on it, flags ghost jobs, and rides a programmatic SEO layer built on real H-1B sponsorship data.
0-100
Fit Score
4
Free Tools
H-1B Data
SEO Layer
Next.js
Stack
Before & After Optimization Telemetry
Direct before-and-after performance metrics comparing unoptimized baselines against our production system.
Application Conversion Rate
Replacing generic lead forms with an instant interactive resume-to-listing fit score lifted visitor-to-scoring conversion by nearly 5x.
Ghost Job Detection Precision
Continuous ATS verification against Greenhouse, Lever, and Ashby endpoints strips stale listings before candidates spend hours on tailored applications.
Organic Landing Page Footprint
Automated data ingestion from the US Department of Labor generated structured, high-ranking pages for every employer and visa job title.
End-to-End System Topography
ShouldApply Distributed Programmatic & Scoring Pipeline
A multi-tier architecture uniting automated public dataset ETL, Next.js ISR edge caching, and semantic LLM resume scoring. Click any node below to inspect throughput, caching strategy, and latency telemetry.
Execution & Request Flow
Public Dataset ETL Ingestion
Extracts and sanitizes 1.2M+ annual Department of Labor filings
Need this component built for your stack? Select it in the B2B intake below for a scoped architecture estimate.
Verified Production Tech Stack
Frontend & Edge
- Next.js 16 (App Router)
- React 19
- Tailwind CSS 4
- Vercel Edge Network
Database & Storage
- Supabase (PostgreSQL)
- Prisma ORM
- Edge Vector Embeddings
AI & Ingestion
- Google Gemini API
- Node Streaming Parser
- Cheerio / Puppeteer
Monetization & CRM
- Stripe Elements
- Resend Email API
- Formspree Webhooks
The Challenge
Most people apply to jobs blind. They read a listing, feel roughly qualified, and burn an afternoon on an application with no idea what their real odds are. Worse, a large share of postings are ghost listings with no live hiring intent behind them. Nothing on the job boards tells you whether a role is worth your time before you commit to it.
The Solution: A Fit Score Plus a Data Moat
The core product scores a listing against your resume and returns a 0 to 100 fit number, the exact skill gaps behind it, and a read on whether the posting looks real. Around that engine sits a set of free tools that pull in search traffic and convert readers into scored signups.
- •Job-fit scoring that grades any listing against your resume and shows the gaps, not just a number
- •Ghost-job detection that flags postings with no live hiring signal so you skip the dead ends
- •Free tool suite including a job authenticity checker, an offer comparison tool, and a salary estimator
- •Programmatic SEO layer generating H-1B sponsorship, salary, and role pages from real datasets, each funneling into the scoring tools
Technical Architecture
Built on Next.js and deployed on Vercel, with Supabase for data and auth and Stripe for billing. The H-1B sponsorship database is the engine of the SEO strategy: thousands of employer and role pages generated from public datasets, each one a landing page that answers a specific search and routes toward the scoring tools.
- •Next.js on Vercel for server-rendered, indexable pages at scale
- •Supabase for the database, auth, and scored-user records
- •Stripe for subscription billing on the paid tiers
- •Dataset-driven page generation so the H-1B and salary pages scale without manual work
Key Takeaways
A useful free tool is a better top of funnel than a blog post, because it earns the signup instead of asking for it. The scoring engine gives people a reason to come back, and the programmatic pages give search engines thousands of specific queries to rank for. You can score any job listing on ShouldApply for free, which is exactly the on-ramp the SEO layer is built to feed.