Resumatic

Job seekers today apply to dozens of postings a week, and nearly every one demands a resume tailored to that specific role. Doing this by hand — rereading the posting, hunting for the right keywords, rewriting bullet points to match the language of the job — takes 45 minutes or more per application, and most candidates simply stop bothering. The result is a flood of generic resumes that never make it past the first filter.

That first filter is rarely a human. Applicant tracking systems now screen the large majority of corporate applications before a recruiter ever opens them, scoring resumes on keyword matching, formatting, and section structure. A strong candidate with the wrong phrasing can be rejected in milliseconds, while a weaker candidate who happens to mirror the posting's language advances. Resumatic's founders, two former recruiting-tech operators, wanted to close that gap for everyday job seekers rather than just enterprise HR teams.

They came to iMobdev with a clear brief: a mobile-first app that could take any resume and any job posting and produce a tailored, ATS-ready version in minutes, not hours — plus a matching cover letter, without the process feeling robotic or generic.

The hardest part was doing this well on a phone. Resume editing has historically been a desktop task, dense with formatting decisions. We needed a touch-friendly editor that could hold real structure — experience, skills, education — while layering in AI rewriting suggestions without overwhelming the screen or making the output feel like a form letter.

Resumatic launched into a crowded resume-building category but stood out immediately for speed: early users tailor a resume to a new posting in under two minutes, down from the 45-plus minutes they reported spending before. The ATS Score screen has become the most-used feature in the app, with most candidates iterating on a resume two to three times before applying.

Looking ahead, iMobdev and the Resumatic team are scoping a browser extension that pulls job postings directly from LinkedIn and Indeed, plus an interview-prep module that uses the same AI stack to generate likely interview questions from a posting. As the resume library grows, the matching engine is expected to get sharper with every application it helps tailor.