Business Models and Financial Projection:
Last updated
Last updated
Xelora operates on a multifaceted business model designed to generate revenue while delivering value across various components of the platform. Below are the key revenue streams:
Decentralized Dataset Pool: (Start from XXX USDT equal of AXB/ month) Users pay a monthly subscription fee to access the decentralized dataset pool, which includes rare, high-quality datasets for AI training.
Comprehensive Platform usage: (199 USDT equal of AXB/month) A subscription-based model provides users with full access to Xelora's end-to-end AI development platform, covering data curation, model building, fine-tuning, deployment, and monitoring.
ML Model Marketplace: (10% on seller per transaction) AI builders can buy, sell, and license machine learning models on the marketplace, with a transaction fee applied to each completed sale.
P2P Consensus-Driven Crowdsourcing for AI: (3% for product owners and 3% for contributors) Crowdsourcing tasks such as labeling and validation are powered by a peer-to-peer system. Xelora takes a percentage of the project budgets allocated for these tasks as a fee for managing the platform and ensuring quality assurance.
Xelora offers a launchpad for AI projects, enabling startups to raise funding directly from the community. Revenue is generated through a fixed fee plus a percentage of the funds raised during token sales on the platform.
Users who wish to use the auto-provisioning feature to optimize compute resource allocation can stake tokens to access this service.
Advertising on Marketplaces: Xelora will introduce advertising opportunities for businesses and individuals to promote their models, datasets, or services on the platform’s marketplace. Fee structures for advertising are to be determined
By combining subscription-based models, transaction fees, and innovative features such as token staking and crowdfunding, Xelora's business model is designed to ensure sustainable growth while fostering a collaborative AI development ecosystem.