Proof of Usefulness Report

Kwant.ai

Analysis completed on 1/24/2026

+628
Proof of Usefulness Score
Category Standard

Kwant.ai is a well-established Construction Tech company (est. ~2014) with significant verified revenue (~$4.7M) and enterprise traction (customers like Walbridge, Paric). The solution uses proprietary IoT sensors and AI to solve critical safety and productivity problems, offering high real-world utility. However, the project submission itself was of poor quality, containing demonstrable falsehoods (claiming 'most people' have used a niche B2B product) and lazy inputs ('everyone'). While the business fundamentals warrant a high score (scaling above the calibration baseline), the final Proof of Usefulness score is heavily penalized by the lack of credible evidence provided in the specific submission fields.

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Score Breakdown

Real World Utility+270
Audience Reach Impact+70
Technical Innovation+135
Evidence Of Traction+112.5
Market Timing Relevance+90
Functional Completeness+5
Subtotal+682.5
Usefulness Multiplierx0.92
Final Score+628

Project Details

Project URL
kwant.ai
Description
Kwant.ai is the first company to use proprietary low powered sensors network to automate construction site data collection to improve safety and productivity. Using artificial intelligence, Kwant .ai provides actionable analytics like schedule and cost risk, early warning signals to optimize workforce and predict and prevent safety incidents. Kwant collects real-time location, time, identification and activity data of craft-workers and assets using minimal infrastructure not possible before and visualizes in 3D heatmap Their platform empowers owners, real estate developers, builders and insurance companies measure and mitigates risks \n\nTheir customers already include some of the largest builders and is deployed in multiple projects where they are enabling decision makers to take proactive actions using predictive analytics and workers to adopt safety culture. It has already validated saving by increasing productivity by 11% and decreasing safety incidents by 80%

Algorithm Insights

Market Position
Strong market validation with clear user adoption patterns
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