The project corresponds to Equilar, a verified market leader in executive compensation data (est. 2000, 200-500 employees). However, the submission quality is critically low, containing factually incorrect claims ('audience reach: everyone', 'most people have used my product') and suspicious metadata (submitter name 'LunarHeart'). The score reflects the high intrinsic value and traction of the actual company, significantly penalized by the vague and inaccurate submission details.
Ready to Compete for $150k+ in Prizes?
Move this data into a HackerNoon blog draft to become eligible for your share of $150k+ in cash and software prizes
Score Breakdown
Project Details
Algorithm Insights
Recommendations to Increase Usefulness Score
Document User Growth
Provide specific metrics on user acquisition and retention rates
Showcase Revenue Model
Detail sustainable monetization strategy and current revenue streams
Expand Evidence Base
Include testimonials, case studies, and third-party validation
Technical Roadmap
Share development milestones and feature completion timeline