AI-powered resume review with brutally honest feedback
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Startup
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Based in
FR
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Pricing model
Freemium
Freemium
Pricing
Free: one practice interview with score and transcript. Paid: 1 interview for $9 USD (€8 EU), 5 interviews for $35 USD (€31 EU), or 10 interviews for $59 USD (€52 EU). Coaching for additional insights costs $4.99 per interview. No subscription; one-time payments. Full refund within 14 days if not st
Free plan includes editor, templates and PDF export. Hunter Pass subscription: $15/month or $39 for 3 months (billed once, save 13%), includes 200 credits a month for approximately 50 applications with tailored resumes and cover letters. One-time resume fix option: $19. Credit packs: Boost 100 credi
Key features
Free four-question practice interview with timing
Score and feedback for each answer
Employer-specific question packs
Coaching with specific fixes for each answer
One-take format matching real interviews
30 seconds prepare, 2 minutes record per question
Multiple pack options for continued practice
AI-powered resume analysis with scoring and feedback
Resume tailoring for specific job postings
ATS compatibility checking
Multiple language support
AI resume builder with chat editing
Tailored cover letter generation
Live job search with intelligent filtering
Interview preparation with flashcards
MCP agent and CLI integration
What makes it different
Employer-verified question banks for real interview preparation
Safe to use: no extensions, overlays or monitoring during actual interviews
Detailed coaching on specific fixes rather than just scoring
One-time payment packs, no recurring subscription required
Addressed HireVue concerns with factual guidance on scoring and bias
Built on real hiring data from 12,000+ job postings, 3,200+ Reddit threads, and 850+ recruiter feedback pieces rather than generic resume advice
Enforced honest scoring and brutal feedback instead of flattery
Autopilot automation works across multiple AI agents and interfaces including Claude Code, CLI, and MCP servers
Tested resume rewrites that maintain factual accuracy while optimizing for ATS and screening models