ResumeScale - Invisible Resume Optimization That Beats ATS and Lands Interviews

Upload your resume and a job description. AI rewrites your resume to match the role's keywords and values without lying, then shows you a match score and the exact changes made.

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Difficulty

beginner

What is it?

Job seekers apply to 50+ roles but get ghosted because their resume doesn't pass ATS (applicant tracking system) filters. Recruiters use keyword matching to screen resumes before humans see them. ResumeScale solves this: upload a resume, paste a job description, and AI rewrites the resume to match the role's keywords while keeping all your actual accomplishments truthful. It shows you: match score (e.g., '78% match'), which keywords were missing, and a diff view of what changed. Why 100% buildable right now: Claude's API can rewrite text with constraints (be truthful, add keywords), PDF parsing is solved (pdfjs), and ATS scoring can be simulated with keyword matching. Ship in 10 days with no custom ML.

Why now?

▸Resume parser (PDF/Word/text)
▸Job description input (paste or URL)
▸AI resume rewrite (truthful, keyword-optimized)
▸ATS match score
▸Diff view showing what changed and why
▸Download optimized resume (PDF/Word)
▸Multiple versions (A/B test different rewrites)

Target Audience

Job seekers, career changers, recent graduates. Est. 5M+ people per year actively job hunting.

Example Use Case

Marcus applied to 30 tech jobs, got 0 interviews. ResumeScale shows his resume is only 45% match for most postings. He uploads his resume and a job description for a dream role. AI optimizes his resume to 89% match by highlighting relevant keywords (e.g., swapping 'managed database' for 'optimized PostgreSQL queries', adding missing tech stack names). He resubmits, gets called in 2 days.

User Stories

Acceptance Criteria

Is it worth building?

$4.99 per optimization × 500 users/month = $2,495 MRR. Monthly plan: $19/month × 100 users = $1,900/month. Path to $5k MRR at month 4.

Unit Economics

Business Model

Pay-per-use + monthly subscription

Monetization Path

Free tier: 1 optimization with watermark. Pay-as-you-go: $4.99 per optimization. Monthly: $19/month for unlimited optimizations.

Revenue Timeline

First dollar: day 4 via $4.99 optimization. $1k MRR: month 2 (200 optimizations). $5k MRR: month 5 (1k+ optimizations + monthly subscriptions). $10k MRR: month 8.

Estimated Monthly Cost

Claude API: $25, pdfjs: $0, Supabase: $25, Vercel: $20, Stripe fees: ~$80 (at $2.5k MRR). Total: ~$150/month.

Profit Potential

Full-time viable at $3k–$10k MRR.

Scalability

Medium — add cover letter optimization, interview prep, salary negotiation coaching.

Success Metrics

Week 1: 300 signups. Week 2: 50 paid optimizations. Month 2: 1k+ optimizations, 65% upgrade to monthly plan.

Launch & Validation Plan

Survey 30 job seekers about resume anxiety. Build landing page. Recruit 10 beta testers seeking jobs actively. Test pricing ($4.99 vs. $9.99).

Customer Acquisition Strategy

First customer: Post in r/resumes and r/careerguidance with subject 'Free resume optimization for ATS (AI-powered)' and link to landing page. Email 50 recent college alumni networks. Ongoing: ProductHunt, LinkedIn content about job search, Reddit r/jobs, TikTok/YouTube shorts about resume hacks.

What's the competition?

Competition Level

High

What's the roadmap?

Feature Roadmap

Milestone Plan

How do you build it?

Tech Stack

Next.js, Claude API for resume rewriting, pdfjs for PDF parsing, Supabase for storage, Stripe for billing — build with Lovable for UI, Cursor for Claude integration.

Suggested Frameworks

Time to Ship

10 days

Required Skills

Claude API, PDF parsing, diff algorithm (for showing changes).

Resources

Claude API docs, pdfjs docs, diff2html for change visualization.

MVP Scope

Landing page, resume upload form, job description input, Claude API integration (rewrite prompt), ATS score calculator, diff view component, export to PDF. 5 pages.

Core User Journey

Upload resume -> paste job description -> see 30-second optimization preview -> pay $4.99 -> download optimized resume -> apply to job.

Architecture Pattern

User uploads resume (S3) -> pdfjs parses -> user pastes JD -> Claude API generates optimized resume + ATS score -> diff calculated -> downloadable PDF generated -> invoice sent via Stripe.

Data Model

User has many Optimizations. Optimization has original_resume, job_description, optimized_resume, ats_score, changes_json, timestamp.

Integration Points

Claude API for rewriting, pdfjs for PDF parsing, Stripe for payments, Supabase for resume storage, S3 for temp file storage.

V1 Scope Boundaries

V1 excludes: cover letter optimization, interview prep, salary data, job recommendations, LinkedIn integration, bulk optimization API.

Success Definition

A paying user optimizes their resume, gets called for an interview within 2 weeks, and leaves a testimonial.

Challenges

Users may expect the AI to lie or exaggerate. Need clear messaging: 'We never fabricate skills or experience. We highlight and reword what you actually know.'

Avoid These Pitfalls

Do not let Claude add fake skills — use a constraint in the prompt: 'Only reword skills that are already mentioned in the resume. Do not add new skills.' Do not optimize for keyword stuffing; it hurts credibility. Do not store resumes longer than 7 days. Be clear about liability: user is responsible for truthfulness.

Security Requirements

Infrastructure Plan

Performance Targets

Go-Live Checklist

How to build it, step by step

1. Run npx create-next-app@latest resumescale --typescript --tailwind. 2. npm install @supabase/supabase-js stripe @stripe/react-js pdfjs-dist diff-match-patch. 3. Create .env.local. 4. Create lib/supabase.ts, lib/pdf.ts, lib/claude.ts, lib/diff.ts. 5. Create pages/index.tsx. 6. Create pages/auth/signup.tsx. 7. Create pages/optimizer.tsx. 8. Create pages/optimizer/results.tsx. 9. Create pages/api/optimize.ts. 10. Deploy to Vercel.

Generated

March 21, 2026

Model

claude-haiku-4-5-20251001

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