Proof of Usefulness Report

Proximilar LLC

Analysis completed on 3/19/2026

+81.2

Proof of Usefulness Score

You're In Business

The project proposes a useful application of machine learning for corporate EPS prediction, but fails to provide realistic or verifiable traction data. Claims such as 'most people have used my product' for a niche B2B financial tool indicate gross exaggeration and a misunderstanding of the target audience. The submission quality is vague, resulting in a low score reflective of minimal verifiable impact despite potential theoretical utility.

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

View All Reports

Score Breakdown

Real World Utility+37.50

Audience Reach Impact+1.00

Technical Innovation+22.50

Evidence Of Traction+1.25

Market Timing Relevance+10.00

Functional Completeness+0.25

Subtotal+72.5

Usefulness Multiplierx1.12

Final Score+81

Project Details

Project URL

proximilar.com

Description

Our state-of-the-art algorithms predict corporate EPS and sales more accurately than the Wall St analyst consensus. They consistently beat the competition in every sector, time period and size category. The machine learning technology we have built has broad applications in corporate finance and beyond: risk management, trading, M&A, options valuation, etc.

Algorithm Insights

Market Position

Growing utility with room for optimization

User Engagement

Documented reach suggests active user community

Technical Stack

Modern tech stack aligned with sponsor technologies

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