Spotlight: JobHunter — the AI only tailors, it never writes, sends, or lies
Most "AI job apply" tools sell you on volume: hundreds of applications a day, sent while you sleep. JobHunter is built on the opposite bet, that volume without control is how you end up with a resume claiming you led a team you never worked on, sent to a company you didn't even mean to target.
The pipeline is simple. You fill out a questionnaire (skills, target role, location, salary, experience). A rule-based scraper pulls listings from Naukri and Indeed today, with LinkedIn coming in Phase 1. You browse the matches and select which ones you actually want, one by one or in bulk. Then, and only then, does AI enter the picture: it reads the job description against your existing resume and reorders and reframes your real experience to fit. It does not invent a skill, a title, or a project. Every tailored draft sits in an approval queue, and nothing gets sent until you click yes. The actual sending is done by a deterministic, rule-based mailer, not a model deciding on its own what to fire off.
The smart decision here is scope. JobHunter didn't try to make AI do everything. It confined the model to the one job it's actually good at, rewriting and reordering content you already have, and kept the two riskiest steps (scraping and sending) boring and predictable. That's a real position in a category full of "fully autonomous" pitches. It also means replies land in your own inbox, since the sender uses your email, not theirs, so you're not relying on a third party to forward you responses.
A few other details worth noting: the scraped job dataset stays in a private database with no export endpoints, which is a specific and checkable claim rather than a vague privacy promise. There's also a placement guarantee tied to a separate Skill Development Program, refunded if you enroll and don't get placed, though that's a distinct offering from the core apply-loop and worth reading the terms on before assuming it applies to you.
Who should try this: people applying to a real volume of roles who are tired of either sending the same generic resume everywhere, or spending an hour hand-tailoring each one. If you want more shots on goal without your resume drifting into fiction, and you're fine reviewing each draft before it goes out, this is a genuinely sensible middle ground. It's also a good fit if you specifically don't trust auto-send tools and want a paper trail of what was sent where.
Who should skip it: if you want true set-it-and-forget-it automation, this isn't it by design, you're still the approval gate on every application, which takes time even if it's less time than writing from scratch. It's also early. The product says outright it's in "Phase 1," proving the core scrape-to-application loop before shipping a full user-facing product, and job coverage is currently limited to Naukri and Indeed. If your target roles live mostly on LinkedIn or niche boards, wait for the later phases. Pricing isn't published yet either, it's free during beta, so budget-conscious job seekers can try it now but should watch for what the paid tier looks like.
Where this goes next, by my read: the interesting test is whether the "AI curates, never fabricates" boundary holds up as they add more platforms and presumably face pressure to speed up the loop. The compliance roadmap (scam detection, GDPR/DPDPA workflows) is listed as upcoming rather than shipped, so it's worth checking back on before trusting it with sensitive data at scale. For now, the core idea, that the two riskiest steps in job automation are exactly the two that should not be left to a model, is a clean piece of positioning and one that's easy to verify simply by watching what actually gets sent.
Try JobHunter: myjobhunter.in
See the launch: JobHunter on welaunch.sh