OpenIO — Discover, build, and deploy private computation logics.
Project Brief
We’re solving one of the biggest blockers in privacy technology today:
privacy primitives as ZK, FHE, and iO are powerful but completely fragmented, overly complex, and nearly impossible for most developers to use.
Right now:
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Each technology lives in its own ecosystem
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Tooling is incompatible and inconsistent
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Developers need deep cryptographic expertise
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There’s no standard library, no shared registry, and no way to compose these technologies together
This makes it extremely difficult to build private AI, sealed logic, encrypted computation, or any privacy-first application.
We’re fixing this.
Our solution and key features
We’re building OpenIO, the first unified platform that lets developers discover, compose, and deploy ZK, FHE, and iO logic without touching cryptography.
Key Features
1. Privacy Model Hub
A global registry where developers can browse, fork, remix, and deploy:
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ZK circuits
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FHE operators & encrypted models
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iO-sealed logic modules
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Hybrid pipelines
Think of it as Hugging Face for privacy computation, backed by decentralized storage.
2. Full-Stack Builder (Visual + Code)
Flow Mode
A drag-and-drop workflow builder that compiles directly into sealed/encrypted logic.
Code Mode
A JS/TS/Python/Rust development environment that outputs the same IR.
Both modes stay in sync and allow developers to build hybrid ZK → FHE → iO pipelines effortlessly.
3. Unified Runtime & Deployment Engine
We are building a shared execution layer that orchestrates private computation across:
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Cloud
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Local
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Eventually on-chain
The runtime handles:
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ZK proof generation & verification
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FHE encrypted compute
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iO sealed logic execution
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Encrypted data flows
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Pipeline orchestration
All logic stays sealed. All data stays encrypted. No math required.
Target users and market
We’re targeting developers and teams who need privacy-preserving compute but don’t have cryptography expertise:
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AI teams running inference on encrypted data
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Web3 teams building sealed smart contracts
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MPC / ZK application builders
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Enterprises with sensitive datasets
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Companies protecting proprietary algorithms
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Privacy-first startups
The market spans privacy-preserving AI, enterprise privacy, on-chain privacy, healthcare/finance, and secure multi-party systems.
What makes us unique
We’re the only platform that:
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Unifies ZK + FHE + iO in a single workflow
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Offers both a visual builder and a full-code environment
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Compiles everything into a consistent IR and shared runtime
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Provides a Hugging Face–like hub for privacy models
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Packs logic into sealed, encrypted, verifiable artifacts
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Supports hybrid deployments across cloud, edge, local, and enclave
We make privacy computation feel like modern software development, not cryptographic research.
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How we will integrate 0G**
We’re integrating 0G in three progressive phases: Storage first, AI second, Compute third.
1. Storage Integration (Now)
Right now, our primary focus is using 0G Storage as the decentralized backbone for the OpenIO Privacy Model Hub.
We will store:
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ZK circuit bytecode
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FHE operators and encrypted model assets
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iO-sealed logic modules
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Pipeline IR graphs
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Model manifests, metadata, and versions
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Sealed/encrypted workflow artifacts
This makes 0G the source of truth for all reusable privacy components across the platform.
Why now?
This is the most immediate need: large cryptographic artifacts, multi-version models, and global distribution demand high-throughput, reliable decentralized storage — something 0G is greatly optimized for.
2. AI Integration (Next)
As soon as storage is fully integrated, we will extend OpenIO’s pipeline to use 0G for AI workloads, especially around encrypted or privacy-preserving models.
This includes:
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Storing model weights packaged for sealed inference
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Serving encrypted datasets for private training or evaluation
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Versioning model families used in private AI pipelines
Why next?
AI is a supportive use case for OpenIO. 0G enables that scalable distribution without sacrificing privacy or performance.
3. Compute Integration (Later)
In the future, we plan to integrate 0G’s compute layer as it matures, enabling end-to-end decentralized privacy computation.
Potential Phase-3 integrations include:
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Distributed ZK proof generation
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Encrypted FHE compute tasks powered by 0G workers
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iO-sealed logic execution anchored to decentralized compute
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Storing/verifying computation logs and encrypted states
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Using 0G for large-scale hybrid private pipelines
Why later?
The moment 0G Compute is production-ready, it becomes a natural extension of OpenIO’s runtime — allowing fully decentralized private computation instead of relying solely on cloud/edge/local execution.
High-Level Timeline
| Phase | Integration Focus | Timeline |
|---|---|---|
| Phase 1 | Storage | Now → Q1/Q2 (already integrating) |
| Phase 2 | AI / zkML | Q2 → Q3 (as features stabilize) |
| Phase 3 | Compute | Q4 onward (progressive rollout) |
Overall Benefits for 0G
By integrating 0G across storage, AI, and compute, we strengthen the ecosystem in multiple ways:
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High-volume real-world usage of 0G infrastructure from a consumer-facing product
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Visibility and credibility for 0G as a practical, deployable tech stack.
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Stress-tested storage, inference, and compute layers through continuous production traffic
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User onboarding into the 0G ecosystem, many of whom are non-crypto-native
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New templates and reference architectures for other builders adopting decentralized AI, Storage or Compute.