See exactly what you get back
Every AI Mock Interview ends in a scored report like this one — not a vague "good job." Here's a real scorecard from a Software Engineer session.
This is an illustrative sample report, not a real candidate's data — it shows the exact format and depth of feedback your own AI Mock Interview produces.
Session Report
Software Engineer — Technical & Behavioral Round
5 questions answered · 14 min session · Interviewer: AI Assistant "Alex"
graphic_eqCommunication Analysis
142 wpm
Speaking pace ✅ in range
Ideal: 130–160 wpm
3
Filler words
"basically", "kind of"
78
Avg. words / answer
check_circleWhat landed
- checkNamed a specific project, stack, and a quantified outcome instead of speaking in generalities.
- checkUsed role-specific keywords the question was listening for ("architecture", "trade-off", "impact").
- checkAnswers stayed under 90 seconds — tight and easy for an interviewer to follow.
lightbulbWhere to sharpen
- arrow_forwardA few answers leaned on "we" — naming your own specific contribution reads stronger to an interviewer.
- arrow_forwardThe system-design answer skipped trade-offs — naming the alternative you didn't pick shows deeper judgment.
- arrow_forwardA couple of filler words ("basically", "kind of") slightly softened otherwise confident answers.
Sample question & feedback
Alex asks:
"Walk me through a project you're proud of and the tech stack you used."
Candidate answered:
"I led the migration of our checkout service from a monolith to a Node.js microservice, cutting p95 latency by 40% and reducing on-call incidents by half over the next quarter..."
boltInstant feedback
Strong — specific tech, a named metric, and a clear before/after. Say "I" once more; right now "our checkout service" slightly blurs your individual role in the migration.
This is one of 5 questions in the session — every answer gets its own transcript, score breakdown, and feedback like this.
More role-specific sample reports (Data Analyst, Product Manager, and more) are coming soon.
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