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Responsible for managing team projects and meeting deadlines
Led cross-functional team of 8, delivering 3 projects 2 weeks ahead of schedule
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.
S: Situation. Company + problem + metric. Two sentences max. Example: "At Stripe, onboarding had a 40% drop-off rate."
T: Task. Your responsibility + measurable target + timeframe. Example: "Cut drop-off 50% in one quarter, no added headcount."
A: Action. Verb-driven sequence: researched → designed → built → tested. Show ownership and decision-making at each step.
R: Result. Quantified outcomes: revenue +32%, drop-off −62%, adopted company-wide. Always close with numbers.
Input: webcam + mic capture via browser. Select from real interview questions. No login, no install.
Process: speech-to-text → NLP on transcript. Measures tone, pace (WPM), volume, filler word count.
Output: four scores — Confidence, Structure, Filler Words, Eye Contact. Percentage breakdown per metric.
Coaching: pattern-matched feedback. Flags weak signals → generates specific fixes. Example: "replace um with a 1-second pause."
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.
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.
Database: 500+ questions. Categories: IB Technical, Behavioral, Accounting & Valuation, M&A / LBO. Sourced from real bank interviews.
Difficulty tags: Easy → Medium → Hard. Progressive difficulty. Filter by category + level.
Flashcards: question → think → reveal model answer. Active recall method for faster retention of technical concepts.
Progress tracking: completion rings per category. Shows % prepared and which topics still need review.
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.