Atsly

Atsly started from a lived problem: applying for design roles abroad while based in India. You tailor a resume for days, submit it, and hear nothing — not a rejection, just silence. The reason is usually mundane: an applicant tracking system parsed a two-column layout into word salad, found too few of the right keywords, and filed the application where no recruiter would ever look.

For international job seekers there is a second, quieter filter: the market itself. A resume that works in Bengaluru fails in Toronto for reasons no ATS will tell you — photo on the resume, two pages instead of one, no measurable outcomes, no accessibility vocabulary. Every market has unwritten conventions, and applicants discover them only through months of silence.

I designed Atsly to make both filters visible before the apply button — so the feedback loop takes thirty seconds, not three months.

Atsly compresses the entire pre-application loop into one mobile flow. Import your LinkedIn profile or upload an existing resume, save the job descriptions you're chasing, and run a check: the app returns an ATS score with a full breakdown — keywords, readability, format, skill match, impact language — where every weak dimension is a tappable, explained fix.

Then comes the layer no competitor ships: Market Fit. The same resume is benchmarked against successful resumes in the target country — Canada in the demo — and gaps are named in plain language: accessibility experience missing, only one quantified bullet, no enterprise scope. Each gap carries a severity and a suggested next step.

Finally, the AI rewrite works bullet by bullet. For each suggestion the user sees their original line, the proposed rewrite, and — critically — the reason it scores higher. Accept, edit, or skip. The session ends with a re-scored resume (74 → 91 in the scripted journey) and a cleanly named PDF.

1. I.

   Every point is accounted for.

   No black-box grades. 74 becomes 91 because single-column formatting adds 12, stronger verbs add 9, two metrics add 6. If the user can audit the math, they trust the tool — the same design conviction I bring to every AI product.

2. II.

   The market is the second ATS.

   Country-specific market fit is the moat. Target-market selection is the first thing on the home screen — 'Canada · 1 page · No photo · CV-style' — and every score, gap, and rewrite is computed against that market, not a generic US default.

3. III.

   AI proposes. The human disposes.

   The rewrite never bulk-replaces a resume. One bullet at a time, original beside rewrite, reasons attached, with accept / edit / skip on every card. Your resume stays yours — Atsly just shows you what the machine reading it will see.

4. IV.

   Built for the moment of need.

   Job hunting happens on the phone — on commutes, between shifts, the night before a deadline. Atsly is mobile-first end to end: bottom sheets for decisions, one thumb-reach CTA per screen, a check that completes in under a minute.

Resume tooling is a busy category, but it clusters in one corner. Jobscan and Resume Worded are desktop-web, keyword-centric, and subscription-gated before the first useful insight. Teal is a tracker first, an optimizer second. LinkedIn Premium tells you how you compare to other applicants — not how an ATS parses you. ChatGPT will rewrite a resume wholesale, with no score, no audit trail, and no idea what Canadian recruiters expect.

Plot the market on two axes — explainability of the score and awareness of the target market— and the top-right quadrant is empty. That corner is Atsly's position: mobile-first, point-by-point explainable, and the only tool treating country conventions as a first-class scoring dimension rather than a blog post.

Where it stands: designed end-to-end as a 0→1 concept — brand, tokenized design system, 50+ hi-fi screens across 9 flows, and the working prototype below. The wedge audience is cross-border applicants (India → Canada first, mirroring Express Entry demand), with per-check pricing instead of subscriptions — because job seekers buy outcomes, not memberships.

The first version of the score screen showed a single number and a “Fix everything” button. It tested as magic — and magic reads as suspicious when the stakes are someone's livelihood. Decomposing the score into named dimensions, and the fix into named point deltas, changed how testers talked about the product: from “does this actually work?” to “which fix should I take first?”

The same lesson as every AI product I've designed: trust isn't a claim, it's a receipt.Show the math, keep the human's hand on every change, and the model becomes a colleague instead of an oracle.