Case study
ApplyLogic
An AI job search that rates every job against your resume, reviews that resume the way an applicant tracking system would, and helps you apply.
- Type
- Web app
- Our role
- Product, design, engineering, and operations
- Live
- applylogic.ai ↗
The problem
Job boards show you everything, and that is the problem. Job seekers scroll past hundreds of listings with no idea which ones they can win. Most applications are then filtered by an applicant tracking system before a person reads them. Nobody tells you why you were passed over.
We wanted a job search that answers one question for every listing: how well does my actual resume fit this job, and what would make it fit better?
What we built
ApplyLogic takes a resume once and turns it into a search profile. Every job then gets a fit rating with the reasons behind it.
PDF, DOC, DOCX, Markdown, TXT, or a LinkedIn export. Scanned and image-only PDFs work too.
AI pulls target titles, seniority, and skills from the resume. The user can edit every detail.
Great, Good, Fair, or Poor for every job, with the matched skills and the signals behind the call.
An ATS-style review, a tailored resume in one click, and a cover letter checked for generic writing.
Technical highlights
Job data pipeline
Listings pulled continuously from ten hiring platforms, then normalized into consistent salary, location, and skill data.Six-dimension ATS scoring
Hard skills, title relevance, experience, education, soft skills, and formatting, weighted into a 0 to 100 score.Grounded AI rewrites
Rewrites only use what the resume already says. When something is missing, the app asks instead of inventing it.Works with your AI assistant
An MCP server lets Claude, ChatGPT, or any MCP client search and track jobs through an OAuth connection the user controls.What it shows about how we work
- Explain the output. Every AI result in ApplyLogic comes with its reasons, so users can trust it and push back on it.
- Keep AI honest. A second pass checks cover letters and rejects generic writing before the user sees a draft.
- Meet users where they are. The same features work in the web app and inside the AI assistant the user already has.
Want the product story? Read the launch post.