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Proof of Usefulness Report

ViridisChem Inc.

Analysis completed on 3/18/2026

+234
Proof of Usefulness Score
Gaining Momentum

ViridisChem presents a robust technical solution for chemical toxicity evaluation with significant real-world B2B utility. However, the submission is hindered by vague, exaggerated claims regarding audience ('everyone') and traction ('most people have used my product'). Due to the lack of verifiable user metrics, concrete revenue data, and poor response quality, the score reflects a project with strong technical foundations but unproven market penetration, placing it in the small but promising tier.

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Score Breakdown

Real World Utility+150.0
Audience Reach Impact+5.0
Technical Innovation+75.0
Evidence Of Traction+2.5
Market Timing Relevance+50.0
Functional Completeness+2.5
Subtotal+285
Usefulness Multiplierx0.82
Final Score+234

Project Details

Project URL
Description
ViridisChem offers a powerful AI driven self-learning cloud software platform that provides REAL-TIME toxicity evaluation of every chemical and mixture covering even new drug-targets and proprietary chemicals. Supported by in-house toxicity database with 90 million chemicals and 2.5 billion properties, comprehensive experimental data repository, global regulatory database covering over 135 US and international regulatory lists, and over 50 prediction models providing information on 60 different endpoints, its product Chemical Analyzer visually shows chemical’s toxicity implications (environmental, health and safety) to help critical R&D decision-making. By providing over 50 chemical and toxicological properties, global regulatory concerns and full GHS classification, it is a great tool to build/validate SDS, select less toxic raw material, and to explore least toxic drug targets analogs. Some of the most unique capabilities we offer are: - Single platform with toxicity information available from most reliable sources, - Real-time execution of most industry-recognized toxicity prediction models offering over 80% accuracy, and - Large repository of experimental data allowing us to use deep-machine learning to predict new health-related tox-endpoints

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