Resume analyze and improve
Upload your resume and a job description. Get a match score and ATS-focused rewrites.
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Get StartedSimple workflow
Upload your resume and a job description. Get a match score and ATS-focused rewrites.
Open resume analyzerGenerate STAR answers, interview questions, and record yourself to refine delivery.
Interview prepNetworking drafts, cold outreach, and cover letters tailored to each role.
Networking & outreachUpload your resume and a job description. Get a 0-100 match score across impact verbs, keywords, readability, and ATS compatibility.
Input: resume (PDF/DOCX) + job description. Output: 0–100 match score across Impact Verbs, Keyword Match, Readability, ATS Friendly.
Process: NLP tokenization + semantic similarity. Maps JD requirements → resume keywords. Color-coded: green = found, orange = partial/synonym, red = missing.
Process: detects weak patterns (“Responsible for…”) → rewrites using action verb + metric + result formula. Example: “Managed projects” → “Led team of 8, delivered 3 projects ahead of schedule, cut costs 15%.”
Checks: file format (.pdf), standard headings, no tables/columns, no images/icons, keyword density. Five pass/fail results. 90% of large companies auto-filter before human review.
Task: Defines your responsibility in each scenario. Clear, concise, recruiter-ready framing.
Action: Breaks down your approach step-by-step. Highlights leadership, initiative, and problem-solving skills.
Result: Quantified impact with metrics, percentages, and dollar amounts. The part interviewers remember.
AI-generated cold emails, LinkedIn DMs, coffee chat questions, and follow-ups personalized to each contact.
Cold email. Input: recipient role + company + your background. Output: personalized subject line, hook, and CTA.
LinkedIn DM. Input: profile data. Output: message referencing their posts, shared interests, or mutual connections.
Coffee chat. Input: person's background + target role. Output: role-specific questions that show research.
Thank-you. Input: conversation notes. Output: follow-up referencing specific details from the meeting.
Upload your resume and job description. AI drafts a custom cover letter matching your experience to the role's requirements.
Input: resume (PDF/DOCX) + job description. Parser extracts entities from both, maps experience → role requirements.
Process: keyword extraction from JD → matched to resume entities → generates natural language draft with role-specific phrasing.
Review: matched phrases highlighted. Every sentence maps to a JD requirement. Inline editing before export.
Output: ATS-friendly PDF. Auto-named [Role]_Cover_Letter.pdf. Formatted, proofread, ready to attach.
Duolingo-style learning paths with 1,200+ interactive lessons, progress tracking, and daily streaks to keep you accountable.
Learning Paths: structured career tracks for Interview Mastery, IB Technical, and Coffee Chat. Each path has progress rings so you always know where you stand.
Interactive Lessons: multiple-choice quiz steps with instant feedback. Work through 12-step modules that build on each concept progressively.
Daily Streaks: keep your momentum with a streak counter, weekly progress calendar, and personal best tracking. Consistency beats cramming.
AI scans your bullets for weak verbs, missing numbers, and keyword gaps. Get instant rewrites using the action-metric-result formula.
Verb analysis: detects weak verbs (“Helped,” “Responsible for”) → suggests strong replacements (“Led,” “Delivered,” “Shipped”).
Quantification: flags vague bullets → adds $, %, timeframe. Example: “Improved sales” → “Grew revenue 34% in 6 months.”
Keyword scan: JD → resume token comparison. Shows per-keyword match strength: green (exact), orange (partial), red (missing).
ATS checklist: format validation: single-column, no tables, standard fonts, .pdf. 75% of resumes fail ATS on formatting alone.