Lepton AI
Build and deploy high-performance AI applications at scale with zero infrastructure management.

OpenLedger is a decentralized data network purpose-built for AI, operating at the intersection of blockchain and machine learning. By 2026, it has positioned itself as the 'Data Layer' for the AI economy, solving the critical scarcity of high-quality, verifiable training data. The technical architecture leverages a sovereign Layer 1/Layer 2 environment (often EVM-compatible) to facilitate transparent data contribution, validation, and curation. Unlike centralized data silos, OpenLedger uses a Proof-of-Contribution consensus mechanism where data providers are rewarded in native tokens for supplying high-fidelity datasets. The platform features integrated Zero-Knowledge (ZK) proofs to ensure data privacy and authenticity, allowing developers to fine-tune LLMs on permissioned data without exposing the raw underlying assets. Its 2026 market position is defined by its ability to provide 'verticalized' data—highly specific industry datasets for healthcare, legal, and engineering—that are otherwise inaccessible to general-purpose web crawlers. The ecosystem supports a decentralized workforce of data labelers and validators, ensuring that the data entering the AI pipeline is cleaned, structured, and ethically sourced, directly addressing the 'garbage in, garbage out' problem in modern foundation models.
Uses Zero-Knowledge proofs to verify that a specific dataset was used in training without revealing the data contents.
Build and deploy high-performance AI applications at scale with zero infrastructure management.
The search foundation for multimodal AI and RAG applications.
Accelerating the journey from frontier AI research to hardware-optimized production scale.
The Enterprise-Grade RAG Pipeline for Seamless Unstructured Data Synchronization.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Distributes the indexing of embeddings across the network to prevent single-point-of-failure in RAG systems.
A proprietary consensus mechanism that evaluates data quality and novelty before issuing rewards.
Smart contract-based voting for the community to determine which datasets should be prioritized for the next epoch.
On-chain workers that automatically clean and reformat raw data into AI-ready JSON/Parquet formats.
Enables training algorithms to run on data locally at the source node, returning only weight updates.
Ability to settle data transactions and rewards across Ethereum, Solana, and Cosmos.
Access to high-quality, verified medical records is difficult due to privacy and siloed databases.
Registry Updated:2/7/2026
Contributors are paid in tokens upon every successful training epoch.
Lack of real-time, cross-company logistics data for predictive modeling.
Centralized labeling services are expensive and often lack diverse perspectives.