The Founder
Why I Built the Best AI for Resume Tailoring
I applied to hundreds of jobs and was rapidly losing hope. The callbacks weren't coming — not because my experience was wrong for the roles, but because the process was broken.
I was spending hours on each application: tailoring the resume, writing the cover letter, second-guessing every word. Then one night, somewhere around the fourth application of the day, it hit me. I was doing the same task over and over. The inputs barely changed. The effort was identical every time. And it was still eating 30 minutes per application.
That's when I decided to build something better.
Everything I Tried First
Before building my own tool, I tried what everyone tries. Each one fell short in a different way.
A full side-by-side comparison exists if you want the breakdown. But the pattern was the same across all of them: they wanted to change my content. All I needed was a ranked list of my best bullets for a specific role. That was it. Nothing I found did that. If you've gone down the ATS score rabbit hole, you'll know exactly what I mean.
The Google Doc Method (and Why It Broke Down)
Before the tool existed, I built a manual version.
I kept a long Google Doc with every bullet point I'd ever written — 40+ entries covering data analysis, product strategy, stakeholder management, growth experiments, AI implementations, and more. For each application, I'd paste the job description into one window, open the Doc in another, and go through every single bullet deciding what stayed and what got cut.
It took 15–30 minutes of pure decision fatigue per application. At 30+ jobs a week, that's the better part of two working days just on copy-paste decisions. It was accurate — my bullets were real, my voice stayed intact — but it was unsustainable.
The fix wasn't a smarter AI. It was a faster version of the process I was already doing manually.
What Actually Changed
Once I built the matching system — paste a job description, get your bullets ranked by relevance, select and export — the numbers shifted immediately.
30 minutes per application became 60 seconds.
My interview rate went from 4% to 8%. Across 100+ applications spanning product management roles in data, AI, growth, marketing, fintech, and healthtech — roles that look similar on paper but require completely different emphasis — the tool consistently surfaced the right bullets for each context.
The difference wasn't better writing. It was better matching.
What I Built
LandThisJob is the tool I built for my own job search, now available to anyone running the same gauntlet.
The core method is the bullet library: you write your bullet points once, and AI ranks them against any job description you paste. You select the best matches, export your tailored resume, and move on. No rewriting. No hallucinations. Your words, matched to the role.
It's free to start — 5 tailored resumes a month, no credit card required. The Chrome extension imports any LinkedIn job posting in one click, so the 60-second clock starts the moment you find a role worth applying to.
If you're evaluating options, the best resume tools page has a full side-by-side comparison. If you're ready to try the bullet library method, start below.
Try the Bullet Library Method
Free to start. 60 seconds per application. Your words, matched to every job.
Frequently Asked Questions
Questions about plans and pricing? See the pricing page. More on resume tailoring strategy on the blog.
Stop spending 30 minutes per application
Build your bullet library once. Tailor every resume in 60 seconds from here on out.