Empowering AI with Structured Decentralized Data
Sentinent enriches every file with machine-readable context using Model Context Protocol (MCP), enabling intelligent search, retrieval, and use by AI agents and LLMs.
With zero-knowledge encryption, your data stays secure, private, and fully under your control across the network.
Ideal for long-term and low-cost data retention, Sentinent supports cold storage, semantic backup, and archives that are AI ready.
By staking tokens you help maintain data integrity and network decentralization while actively contributing to the Sentinent ecosystem.
Why Sentinent Stands Apart
Sentinent
- ✔️Semantically enriched and AI-readable
- ✔️Decentralized peer-to-peer infrastructure
- ✔️Zero-knowledge encryption by default
- ✔️Context-aware search for agents and LLMs
- ✔️Cold storage optimized for AI workloads
- ✔️Wallet-based access, no accounts needed
- ✔️Staking mechanism for network participation
Others
- ❌Flat files without semantic context
- ❌Centralized, server-based infrastructure
- ⬛Account-based login and identity systems
- ⬛No user rewards or participation incentives
- ⬛Basic file links, limited privacy control
- ❌Not optimized for AI agents or workflows
- ❌Third-party control over data access
Structured Storage for the Intelligent Web
Contextual Upload Service:
Upload your files with metadata enriched by the Model Context Protocol. Sentinent makes data machine-readable from the start, structured for agents and intelligent retrieval.
ZK-Preserved Storage:
Store sensitive or personal data with privacy by default. Every file is encrypted using zero-knowledge proofs, giving users complete control over access and visibility.
Semantic Retrieval Layer:
Access data based on context, not filenames. Sentinent enables intelligent systems and LLMs to fetch what they need through meaning-aware search and structured filtering.
Structured Storage for AI-Driven Workflows
AI Agent Memory
Sentinent provides AI agents with access to structured, machine-readable data enriched through semantic context. This enables faster decision-making and more accurate autonomous behavior.
Personal Knowledge Vaults
Users can create encrypted data environments for personal, professional, or research use. All files remain private, fully owned, and accessible only by the data owner.
Cold Storage for AI Workloads
Sentinent supports long-term, low-cost storage for semantically enriched datasets. It is ideal for backups, offline training archives, and historical model inputs.
Model Training and Retrieval
Developers can use Sentinent as a structured data backend for RAG and LLM pipelines. Query-ready inputs improve performance, reduce noise, and align outputs with real context.
