How does AI evaluate my resume fit?

Before you rewrite a single bullet, you need to know **what the pipeline is actually scoring**. Modern fit tools do not just count keywords—they compare meaning between your experience and the job description.

Step 1 — Decode the job description

The AI extracts must-have requirements, seniority signals, and domain vocabulary from the posting. Critical requirements weigh more than nice-to-haves. If the role tests system design but your resume leads with project management, the fit score reflects that mismatch early.

Step 2 — Map evidence to requirements

Each requirement is matched against your resume bullets. Strong fit means a defensible bullet proves the skill—not a keyword dropped into a generic line. The scorer flags **gaps** (requirement with no evidence), **weak matches** (adjacent but thin), and **strong matches** (clear proof).

Step 3 — Score semantic alignment

Applicant tracking systems increasingly use embedding-based similarity: they compare how closely your language matches the recruiter's rubric. Two resumes with the same keywords can score differently if one uses the posting's phrasing and quantified outcomes.

Step 4 — Surface strategic interventions

A strategic resume review does not auto-rewrite everything. It ranks interventions by impact: missing must-haves first, then reframes for domain language, then formatting fixes that affect parsing. You approve or skip each change—nothing ships without your sign-off.

Step 5 — Re-score after curation

After approved edits, the fit score updates so you can see movement before export. Pair the curated resume with a study pack and voice mock for the same role so prep stays aligned with what you actually claim.

ResumeInterview runs this pipeline end-to-end: fit check → approved interventions → ATS export → interview prep on the same thread.