Rivo
The Data Asset Exchange for AI
Enabling compliant circulation and value settlement across the AI lifecycle. With every training, inference, and agent action, data generates traceable revenue —transforming from a static collection into a priced, rewarded asset.
Why Rivo Exists
Rivo exists to bring AI data into a tradable, measurable, and fully traceable economic system.
Data has value, but cannot enter the AI world
High-quality data remains dormant across individuals, enterprises, and institutions.
Unclear ownership, high compliance costs, and missing trust mechanisms prevent it from being safely used in model training and inference.
[ Data exists, but is not yet AI-consumable. ]
Data lacks standards, AI cannot scale its use
Datasets are poorly defined, hard to compare in quality, and unclear in usage scope.
AI companies cannot procure and reuse trusted data with the same scale and confidence as compute.
[ Data has not yet become AI infrastructure. ]
After data is used, value and accountability disappear
Once data is invoked, its origin, permissions, and responsibility chain become opaque.
Compliance risk falls on users, while contributors receive no ongoing value.
[ Data is used, but leaves no footprint. ]
Core Capabilities of Rivo
Data as Assets
Turn raw data into clearly defined, tradable data assets.
Standardized Datasets
Package datasets for training, alignment, and evaluation at scale.
Trusted Data Exchange
Enable neutral, transparent data transactions under clear rules.
Controlled Usage & Licensing
Define explicit usage scope and permissions for every transaction.
End-to-End Traceability
Track data usage with auditable, accountable lifecycle records.
AI-Native Transactions
Designed for model training and agent-based execution at scale.
Rivo Transaction Structure
Rivo is the data exchange backbone of Mobiusi.
Built on compliance and trust, it bridges data providers and AI consumers,powering va lue flow throughout the AI-native economy.
RIVO
ASSET REGISTRY
TRADING LAYER
DELIVERY GATEWAY
Data Asset Types Supported by Rivo
Multimodal Training Datasets
Text, image, video, audio, and sensor datadesigned for LLMs pre-training and fine-tuning.
Task-Level & Behavior Datasets
Datasets designed around specific tasks, behaviors,decision chains, and agent workflows.
Industry-Specific Data Assets
Domain-focused datasets for healthcare, industrial systems, finance, agriculture, and other vertical scenarios.
Graph & Structured Data Assets
Structured, relational, and graph-based data directly compatible with the Mozi Graph System.
Annotated & Alignment Data Assets
High-quality labeled datasets for SFT, RLHF, preference alignment, evaluation, and benchmarking.
Derived, Synthetic & Temporal Data Assets
Augmented, synthesized, and time-series datasets derived from original assets under controlled rules, including logs, events, and temporal signals.