The Objective
Our platform facilitates a seamless onboarding experience for metaverse participants via a permissionless metaverse protocol that unlocks accessibility and interoperability in metaverse assets. The reitio ecosystem will provide unique ways that users can leverage to create, assemble, fractionalise, monetise and share 3D models.
We will empower creators to create their own intuitive and powerful virtual assets via drag and drop facilities. You can quickly make your own virtual assets (e.g., wearables and collectables), animate them, and monetise them in metaverse marketplaces. Below are the primary objectives of reitio:
3.1 Virtual Assets Deployable Across Metaverse
Most 3D world assets creators, avatars, or environments are siloed and unable to collaborate across virtual worlds. For instance, the virtual item which you own in Sandbox could not be deployed and used in Decentraland. Similarly, you cannot open a game that was built in Unity with an Unreal Engine application.
reitio wants to unlock the metaverse’s potential by allowing users to create and monetise their creations in multiple environments. We believe that enabling users to deploy their creations across different metaverse environments unlocks many prospects in the hitherto untapped sector.
3.2 Data Analytics Unlocks Valuable Insights
Through Curated Datasets Data analytics through curated datasets is the key for users in acquiring valuable insights to the metaverse commerce economy. Unlike other platforms, reitio’s platform is data-driven where any user can utilize data analytics to streamline research and content assessment, understand consumer preferences, and forecasting of market trends for investment opportunities.
For example, advertisers can leverage on consumer behavioural data to optimize their marketing efforts while retail investors are able to utilize the platform’s analytical capabilities to make better investment decisions on virtual assets. Similarly, content creators and virtual influencers can have the edge with aggregated datasets to estimate their popularity and growth.
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