Methodology
How the recruiter check works
Applye reads a job the way a screener does, not the way you hope it reads. The check is built in layers: cheap deterministic code first, then rubric-guided AI judgement only where it earns its tokens. This is the career-ops methodology, rebuilt for a desktop GUI.
The score
Every scored job gets a single fit reading on a 0 to 100 scale, plus a one-line verdict you can act on: pursue it, hold and improve the application, or skip it. The number is not the point; the point is a clear, blunt signal and the reasons behind it. The line between "pursue" and "hold" is defined by the scoring rubric and shown per job, not a fixed universal cutoff.
The tone is deliberately direct: how an HR screener actually reads, not encouragement.
Layer 1: deterministic, 0 tokens
Before any model runs, plain code does the obvious work for free. It parses the posting, hard filters on location, salary, contract type, and visa, and assigns a legitimacy tier. Obvious mismatches and likely-fake postings stop here, so you never spend tokens scoring a job that was never real.
Green
Looks legitimate. Clear company, comp, and posting signals. Proceeds to scoring.
Yellow
Flagged. Missing salary, thin detail, or odd patterns. Scored, but with a caution.
Red
Ghost-job patterns or a suspicious domain. Surfaced as a warning, not quietly scored.
Scan summary, for a batch: "2 passed, 1 rejected (location), 1 flagged yellow (no salary)."
AI scoring layers
Only jobs that pass the code filter reach the model, and it is asked to act as a blunt recruiter, not a cheerleader. Each result is structured and cached against a hash of the job text, so re-opening it costs nothing.
- Recruiter rubric score with point-by-point deductions, not a vibe.
- Top missing keywords the posting expects and your profile lacks.
- Hiring-manager red flags a screener would raise.
- ATS filter pass on the formatting that silently breaks parsers (the real hyperref lesson, encoded as a check).
Before you submit
The check closes with practical notes, so you fix the application before it goes out:
- Salary missing prompts a comp-research step.
- Portfolio required is called out.
- Deadlines are surfaced.
Honest about judgement
This is rubric-guided LLM judgement, not a deterministic formula. Two runs can phrase things differently, and the model is an excellent assistant but a poor judge of someone's life. So the score is a sharp opinion you can argue with, never a verdict. The deductions are shown, you can expand the full breakdown on demand, and you always override it. We do not publish fixed rubric weights as if they were physical constants; the rubric lives in auditable skill files that ship with the app and open to the public with the source.
Token economy
The whole design follows one rule: do not call AI where code suffices. Parsing, filtering, legitimacy tiers, the first-pass ATS check, follow-up badges, and analytics are all plain code at 0 tokens. The model is reserved for genuine judgement (scoring, tailoring), runs on a cheap tier for routine work, and every answer is cached. A real search costs cents, not a subscription.
See the full pipeline in the docs
The augmentation boundary
The check scores and the app drafts, but it stops there. It never decides for you and never submits for you. A recruiter is a person, and the relationship is yours, not a bot's. Drafting is automated. Submitting is not.