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Indom
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Indom

Tailor your CV to real jobs, without keyword stuffing

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Indom helps job seekers tailor their CV to the jobs they actually want. It compares a job description with your real skills and experience, shows what matches, what needs improvement, and what doesn't fit. Instead of blindly stuffing keywords or adding skills you don't have, Indom helps you make your CV more relevant while keeping it honest and true to your background.

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Features

  • Job-specific CV tailoring
  • CV and job match score
  • Real skill matching
  • ATS-friendly optimization
  • Clear AI explanations
  • Missing skill detection

Use Cases

  • Tailoring CVs for specific jobs
  • Improving ATS compatibility
  • Preparing better job applications
  • Finding gaps between your CV and a job description

Comments

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Student & Indie Hacker building AI tools...

I built Indom because tailoring a CV for every job shouldn't mean blindly copying keywords from the job description. Indom compares the job with your real skills and experience, then shows what matches and what needs work. The goal is simple: help people make better, more honest CVs and job applications with AI.

that's great to see your procudt

The "no keyword stuffing" angle is the right call for ATS systems that now score on semantic fit. Curious whether Indom rewrites whole bullets or only adjusts keywords, and if there's a way to lock certain phrasing (certifications, metrics) so the tailored version still matches the real experience.

I like the focus on honest CV tailoring without keyword stuffing. How does Indom identify transferable skills that aren’t explicitly listed in a job description?

Congrats on the launch! The match/gap breakdown is a smart angle - tailoring a CV without keyword stuffing is exactly what most tools get wrong. Does it flag sections that are overstuffed too?

The missing-skill detection paired with clear AI explanations is the right combination — most CV tools either rewrite blindly or dump a score without saying why. Explaining which skills are genuinely transferable versus actually missing helps people apply strategically instead of keyword-stuffing, which is also a better signal to hiring managers in the long run.

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Founder of BuyHub, a search engine for c...

Thanks for sharing, it's a great website

Tailoring a CV per job is exactly where generic advice falls apart, so the per-listing approach makes sense. Curious how you handle the honesty problem — auto-rewriting can drift into overclaiming. Do you flag phrases the user can't actually back up in an interview?

The "what doesn't fit" part is the most useful idea here. Most CV tools only push you to add keywords, but in many public-sector applications a mismatched claim is exactly what gets a file rejected at screening. Two questions: does Indom handle job descriptions written in French (very common in North Africa), and can it compare against a formal list of required diplomas or conditions rather than only skills?

Love the "no keyword stuffing" stance. I'm on the recruiter side of this problem, and the CVs that stand out are the ones where the match is real, not just repeated keywords. Does Indom ever tell someone a job just isn't a fit and isn't worth applying to? That would save both sides a lot of time.

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Comments

custom-img
Student & Indie Hacker building AI tools...

I built Indom because tailoring a CV for every job shouldn't mean blindly copying keywords from the job description. Indom compares the job with your real skills and experience, then shows what matches and what needs work. The goal is simple: help people make better, more honest CVs and job applications with AI.

that's great to see your procudt

The "no keyword stuffing" angle is the right call for ATS systems that now score on semantic fit. Curious whether Indom rewrites whole bullets or only adjusts keywords, and if there's a way to lock certain phrasing (certifications, metrics) so the tailored version still matches the real experience.

I like the focus on honest CV tailoring without keyword stuffing. How does Indom identify transferable skills that aren’t explicitly listed in a job description?

Congrats on the launch! The match/gap breakdown is a smart angle - tailoring a CV without keyword stuffing is exactly what most tools get wrong. Does it flag sections that are overstuffed too?

The missing-skill detection paired with clear AI explanations is the right combination — most CV tools either rewrite blindly or dump a score without saying why. Explaining which skills are genuinely transferable versus actually missing helps people apply strategically instead of keyword-stuffing, which is also a better signal to hiring managers in the long run.

custom-img
Founder of BuyHub, a search engine for c...

Thanks for sharing, it's a great website

Tailoring a CV per job is exactly where generic advice falls apart, so the per-listing approach makes sense. Curious how you handle the honesty problem — auto-rewriting can drift into overclaiming. Do you flag phrases the user can't actually back up in an interview?

The "what doesn't fit" part is the most useful idea here. Most CV tools only push you to add keywords, but in many public-sector applications a mismatched claim is exactly what gets a file rejected at screening. Two questions: does Indom handle job descriptions written in French (very common in North Africa), and can it compare against a formal list of required diplomas or conditions rather than only skills?

Love the "no keyword stuffing" stance. I'm on the recruiter side of this problem, and the CVs that stand out are the ones where the match is real, not just repeated keywords. Does Indom ever tell someone a job just isn't a fit and isn't worth applying to? That would save both sides a lot of time.

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