PeerfectCV
Founder | Product & Execution
A peer-driven resume feedback platform built to deliver fast, structured, and actionable feedback for jobseekers.
Context
End-to-end product build: from user research and problem definition to deployment and early retention.
The Problem
Jobseekers spend days refining resumes, but the feedback loop is broken:
- Feedback often takes days or never arrives.
- Responses are generic and lack actionable depth.
- Power dynamics make asking for follow-ups awkward.
- Existing "expert" services are expensive and unscalable.
Designed and shipped an MVP in under 6 weeks.
-
Email-based authentication & verification system -
Structured submission flow for high-quality inputs -
Automated reciprocal peer-matching engine
Product Approach
Feedback comes from relevant peers, not anonymous reviewers.
Structure matters more than volume.
Speed and reciprocity drive engagement and retention.
Metrics & Outcomes
50+ Jobseekers
Onboarded during closed beta.
88% Completion
High onboarding completion rate.
Quality +40%
Iterative UX increased actionable feedback scores.
Problem Context
The gap in the market
The gap was not lack of effort from users, but the lack of a structured, reciprocal system.
PeerfectCV is designed around a simple insight: most jobseekers need timely, contextual feedback, not generic advice. During research, I observed that while communities like Reddit or LinkedIn exist, they fail to provide consistent quality or guaranteed responses.
Core Principles
Product Architecture
Trust & Verification
- Implemented email-based authentication to prevent spam.
- Lightweight verification to ensure user quality without friction.
Structured Input
- Built a guided submission flow to standardize requests.
- Forced constraints on feedback length to ensure digestibility.
Reciprocity Engine
- Automated workflows using lightweight backend tools.
- Matching logic prioritizes active reviewers to incentivize participation.
Key Decisions
Tech stack decisions were driven by speed, reliability, and ease of iteration.
Constraint over Features
Intentionally scoped to solve one problem well rather than expanding into adjacent features like interview prep.
Reciprocity over Payment
Lowered the barrier to entry by using "give to get" mechanics instead of a paywall.
Structure over Freedom
Open text boxes led to vague feedback. Guided forms improved actionable scores by 40%.
Speed over Scalability
Used lightweight no-code/low-code backend tools to ship the MVP in 6 weeks.
Product Demo
This project was about shipping a simple system that actually gets used.
Clear constraints drove better participation than feature depth.