Proof of Usefulness Report

GEMESYS

Analysis completed on 2/3/2026

+105
Proof of Usefulness Score
Gaining Momentum

GEMESYS is a high-potential deep-tech startup developing memristor-based analog AI chips, validated by a recent €8.6M ($9M+) Pre-Seed funding round (Nov 2024) from top-tier investors like Sony and Amadeus Capital. While the technology promises a paradigm shift in energy efficiency (20,000x), the project is currently in the R&D/pilot stage with no mass-market availability, which limits its current real-world utility score. The submission form itself was of very poor quality, containing demonstrable inaccuracies (claiming 'most people' have used it and understating the team size), which significantly impacted the Response Quality metric. However, the external verification of the company's funding and technology justifies a score reflecting 'Promising' status despite the lack of current user scale.

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+22.5
Audience Reach Impact+8.0
Technical Innovation+22.5
Evidence Of Traction+27.0
Market Timing Relevance+11.4
Functional Completeness+0.25
Subtotal+91.65
Usefulness Multiplierx1.15
Final Score+105

Project Details

Description
Current hardware for artificial intelligence is inefficient. For today's supercomputer center, it takes a long time, huge datasets and the energy of a whole power plant to train a high-end AI model, resulting in high costs and unsustainability. The problem is rooted in the fundamental architecture of today’s digital hardware itself, since it has nothing in common with the way the human brain works. GEMESYS Technologies offers an analog chip design based on the same information-processing mechanisms as the human brain. This enables AI hardware vendors to distribute a novel chip, that trains neural networks 20,000 times more energy-efficient than current technology. It not only significantly reduces the cost, time and amount of data required to train a neural network, but also increases overall quality as well as performance. Its small size and high energy efficiency allows it to be embedded in nearly every device, enabling decentralized on the edge training, data processing and decision making.

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