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OpenGradient Use Cases ​

OpenGradient unlocks a world of new possibilities for trustless applications, allowing developers to build AI agents, smarter applications, and optimized features that improve user experience. With the Python SDK and x402 LLM inference, developers can leverage verified, decentralized AI inference across a wide range of use cases.

We published an extensive blogpost on exciting verticals of applied AI in Web3 that the team is focusing on. For the latest updates on OpenGradient's R&D efforts, we recommend giving the blog section on our website a comprehensive read.

Web & Enterprise Applications ​

Build applications that leverage OpenGradient's verified inference infrastructure. LLM inference uses TEE (Trusted Execution Environment) verification, providing cryptographic guarantees and hardware attestation:

Enterprise Applications ​

  • Decentralized and Privacy-Preserving LLMs: Run LLM inference with full verification and transparency, ensuring model integrity without exposing proprietary data
  • Secure Deployment of Proprietary AI Models: Deploy proprietary models with cryptographic guarantees of execution integrity
  • AI-Based Fraud Detection for Fintech Applications: Use verified ML models for fraud detection with full auditability and transparency
  • Secure Compute for Healthcare AI: Healthcare applications that require verified, tamper-proof AI inference for critical decision-making with privacy protection and code verification through TEE attestation
  • Transparent and Decentralized AI Content Moderation: Content moderation systems with verifiable AI decisions and transparent model execution, providing cryptographic proof of moderation decisions

Mission-Critical Applications ​

  • Smart Contract Analysis: Analyze smart contracts with verified prompts for audit compliance, ensuring cryptographic proof of what prompts were sent to the LLM
  • Resolution and Decision Verification: Verify that resolutions or decisions used the correct prompt with accurate data inputs, ensuring fair and transparent outcomes with verifiable proof of the logic used
  • Financial Services AI: Build financial applications that require verifiable LLM inference with full audit trails for regulatory compliance
  • Regulatory Compliance Tools: Create AI systems that provide cryptographic proof of LLM interactions for audit and compliance requirements

Privacy-Sensitive Applications ​

  • Privacy-Preserving Chat Applications: Build chat applications that process sensitive information with TEE verification, ensuring data remains private and code execution is audited
  • Secure Customer Support: Deploy customer support systems that handle personal data with hardware-level attestation and confidential compute
  • Enterprise AI with Audit Trails: Build enterprise AI applications that require verifiable, tamper-proof LLM inference with full transparency

AI Agents ​

  • Autonomous AI Agents: LLM-backed agents that make decisions and interact with applications, with cryptographic proof of which prompts were used to take specific actions, enabling transparent and auditable agent behavior
  • Blockchain-Orchestrated AI Agents: Agents that execute complex workflows with full verification and provable actions

Data Processing ​

  • Decentralized AI Decision-Making Processes: Critical decision systems that require verifiable, tamper-proof AI inference with full audit trails
  • Real-Time Model Inference: Applications that need fast, verified inference with competitive pricing and full integrity guarantees
  • AI Decision Documentation: Create systems that document AI decisions with verifiable prompts and responses for accountability
  • Verified LLM Services: Build LLM-as-a-Service offerings with payment-gated access and TEE verification for trustless AI inference

MemSync Use Cases ​

Build AI applications with persistent memory and long-term context management using MemSync, OpenGradient's long-term memory layer for AI. MemSync is powered entirely by OpenGradient's verifiable LLM inference, using TEE-verified execution for memory extraction, classification, and user profile generation:

Personalized AI Applications ​

  • Personalized Chatbots: Build chatbots that remember user preferences, past conversations, and context across sessions, providing truly personalized interactions
  • AI Assistants with Memory: Create virtual assistants that learn about users over time, remembering their preferences, interests, and important information
  • Context-Aware Applications: Develop applications that adapt based on user history, preferences, and stored memories for enhanced user experiences

Memory-Enhanced LLM Apps ​

  • Long-Term Context Management: Build LLM applications that maintain context beyond token limits by leveraging semantic search across stored memories
  • Multi-Session AI Applications: Create AI applications that maintain continuity across multiple sessions, remembering user interactions and preferences
  • Intelligent Memory Organization: Deploy applications that automatically extract, classify, and organize memories (semantic vs episodic) for optimal context retrieval

Enterprise & Consumer Applications ​

  • Customer Relationship Management (CRM) AI: Build CRM systems that remember customer interactions, preferences, and history across all touchpoints
  • Personalized Recommendation Systems: Create recommendation engines that learn from user behavior and preferences stored in long-term memory
  • Educational AI Tutors: Develop tutoring systems that remember student progress, learning patterns, and areas of difficulty for personalized instruction
  • Healthcare AI with Patient Memory: Build healthcare applications that maintain patient context and history across multiple interactions

Key Capabilities ​

All use cases benefit from OpenGradient's core capabilities:

Available Now ​

  • Verifiable LLM Inference: LLM execution through x402 with TEE verification provides hardware attestation, privacy protection, and cryptographic proof of prompts for mission-critical applications
  • Multi-Chain Payment Settlement: Pay for inference on OpenGradient or Base with flexible payment options
  • Provable Prompt Usage: Cryptographic proof of which prompts were used for any inference, enabling transparent verification of agent actions
  • Long-Term Memory: MemSync provides automatic memory extraction, semantic search, and user profile generation for building personalized AI applications with persistent context - all powered by OpenGradient's verifiable LLM inference
  • Model Hub: Decentralized model storage on Walrus for permissionless model hosting and distribution

Coming Soon (Alpha Testnet) ​

On-chain ML inference capabilities are under development:

  • Atomic Execution: Model inference executes atomically within transactions, ensuring state consistency
  • ZKML & TEE Verification: ML execution with multiple verification methods (ZKML, TEE, Vanilla), with proofs natively validated by the network
  • Model Scheduling: Automate periodic model execution with the Model Scheduler
  • Price Feeds: Access real-time and historical price data through integrated oracles for time-series models
  • Data Preprocessing: Built-in data preprocessing capabilities for preparing model inputs
  • Composability: Chain multiple models together for complex workflows