Outreach OS
A job posting goes in. A full, researched application comes out.
I was spending an hour on each application and still sending something that read like a template. So in a week I built Outreach OS. It scores how my resume fits the role, researches the company, and gives me a strong starting point to write from. I take it from there, and I use it every time I apply.
I kept hitting the same wall.
So did everyone I asked.
Applying for design roles, I was spending close to an hour on each application and still sending things that read like everyone else's. When I asked around, my seniors, my friends, juniors just starting out, every one of them described the same problem. It's not specific to designers, or to students. Anyone applying for a job, anywhere, runs into it.
The usual answer is to apply to more jobs, which only makes it worse. The AI tools meant to help make it worse too. Recruiters can spot a templated cover letter on sight, so it gets skipped. Four things were actually going wrong:
AI output gives itself away
Cover letters from generic tools share the same structure and the same hollow tone. Recruiters see hundreds and filter them out before reading. The tool meant to help becomes a tell.
Scores you can't trust or trace
Existing tools score a resume without showing their work. The same resume scores 9.1, then 10, minutes later. Feedback like "strengthen your first bullet" never says which bullet, or against which requirement.
Research costs more than the application
Real personalisation means knowing something true about the company, which takes 30 to 60 minutes of digging. Most people skip it. The few who don't are the ones who get replies.
No memory by interview day
You tailor a letter, make specific claims, then forget them by the time the call comes weeks later. The posting is gone. Nothing was kept.
One input.
A full draft to work from.
You give it the job posting. It does the research, scores the fit, and writes the first version of everything. What comes out is not meant to be sent blind. It is a strong starting point you read, sharpen, and add your own judgment to, so you begin at a real draft instead of a blank page.
Where the real
design decision was.
The thing I had to get right was deciding what the model should and should not do. It is good at reading, extracting, and matching. It is unreliable at math. So I let it judge the language and moved every number into plain JavaScript. That one split is why the score can be trusted and why the same resume always gets the same result.
Less time on each application.
More of it that actually lands.
The goal was never to automate the job search. It was to shift where the human effort goes: away from research and formatting, toward judgment and editing. Outreach OS handles what a machine can do well. The applicant handles what only they can.
The right question isn't "how do I apply to more jobs." It's "how do I make each application worth sending."
This is v1.
The product vision is bigger.
Outreach OS started as a personal tool. The problems it solves belong to every job seeker trying to stand out in a market that rewards specificity. The roadmap treats this as v1 of a product, not a finished project.
Six steps. One run.
Ready to interview.
The full flow, screen by screen. Scroll through, or click any step to jump to it. Click the screen to expand.
The hard part wasn't the AI.
It was knowing what to leave to code.
Outreach OS is proof that the hard part of an AI product isn't the model. It's knowing what to leave to code, and shipping it.