CafeCloud — AI-Assisted Gaming PC Rentals
Our primary users are occasional gamers with low-spec laptops or PCs and reliable internet connections who want to play demanding games without purchasing expensive hardware. They need an affordable remote PC that supports their game and remains available for their entire session. Hardware specifications alone do not establish connection quality, session availability, or total cost. Using a shared remote PC also raises concerns about account credentials, personal files, and data left behind after a session. Our supply partners are internet cafés with spare gaming PCs during specific hours. They need additional revenue while retaining control over availability and protecting their machines from unauthorized access or harmful software. CafeCloud aims to connect these users through a PC-rental marketplace. AI interprets gamers’ preferences and ranks eligible PCs, while software checks compatibility, budget, availability, and host security requirements before reservation. Planned safeguards include approved hosts, encrypted connections, restricted user permissions, and session cleanup to protect both renters and PC owners.
Preliminary score
- Value for the user19.0 / 25
A specific problem and a clear outcome from solving it.
- Prototype and use of AI17.0 / 30
A working core scenario, and what AI actually contributes to the result.
- Quality testing16.0 / 20
Tests, examples of failures, and comparison with the current approach.
- Feasibility8.0 / 15
Data requirements, running costs and a clear next step.
- Originality6.0 / 10
A meaningful difference from common solutions.
What the judges wrote
- GPT-6 AstraPreliminary66/100
- Strengths
- Your focus on occasional gamers and cafés with spare capacity gives the marketplace a concrete two-sided use case. Your architecture sensibly separates AI preference ranking from deterministic compatibility, budget and reservation checks. Your testing report provides specific failures and regression checks, and clearly distinguishes successful fallback behavior from successful model calls.
- To improve
- Your next demonstration should complete a reservation-to-play session on real hardware, including input control, disconnect recovery and session cleanup, with measured latency and FPS. Your AI evaluation needs repeated live runs and human-labelled preference cases against the cheapest-eligible baseline to establish ranking value and reliability. Your café pilot should document how host availability and performance data stay accurate, estimate per-session operating costs, and measure booking success or time-to-play against current practice.
The team
Məhəmmədəli Babayev
data
Aqil HəsənzadəLead
business
Əli Məmmədli
developer