Giza Protocol is a developer platform for creating and deploying verifiable machine learning applications on blockchain networks — using zero-knowledge proofs (ZKPs) to make ML model inferences trustless and on-chain-composable. In traditional AI integration with smart contracts, the contract must trust a centralized oracle or API that ran the model — you can’t verify the computation. Giza enables smart contracts to verify that a specific ML model (with a specific set of weights) produced a specific output, without re-running the full inference on-chain. This enables ZK-ML: machine learning inference that is cryptographically verifiable, creating a new primitive for trustless AI-powered DeFi, autonomous agents, and risk models.
How It Works
| Step | Process |
|---|---|
| 1. Model export | Developer trains an ML model (neural network, classifier, etc.) and exports it in ONNX format |
| 2. Giza compilation | Giza compiles the model into an arithmetic circuit — equivalent representation in ZK-provable format |
| 3. Prover generation | A Cairo-based ZK prover is generated that can prove model execution |
| 4. On-chain deployment | The verifier contract is deployed on Starknet/Ethereum — accepts a ZK proof as input |
| 5. Inference + proof | At runtime, the model runs off-chain, generates an output and a ZK proof; proof is submitted on-chain |
| 6. Smart contract verification | The verifier checks the proof; the smart contract trustlessly consumes the verified model output |
Key Features
| Feature | Details |
|---|---|
| ZK-ML | Zero-knowledge proofs applied to machine learning verification — trustless AI inference for smart contracts |
| Giza Agents | Autonomous AI agents with on-chain verifiable decision-making — agents can prove their reasoning |
| ONNX support | Standard ML model format support — deploy models trained in PyTorch, TensorFlow, or scikit-learn |
| Starknet-native | Built on Cairo (Starknet’s ZK-native language) — leverages Starknet’s STARK infrastructure |
| DeFi integration | Risk models, pricing algorithms, or liquidation triggers that can be ZK-verified by protocols |
Use Cases
- Verifiable DeFi risk models: A lending protocol uses an ML credit risk model — borrowers can verify the model was actually applied to their application, not an arbitrary decision
- Autonomous ZK agents: AI agents whose decision-making process can be cryptographically proved — enabling trustless agent governance participation
- On-chain ML pricing: ML-based pricing models for derivatives or structured products that can be verified without trusting a centralized black box
- Auditable trading algorithms: On-chain proof that a trade was executed by a specific algorithm, not manual manipulation
History
- 2022: Giza founded; team begins research into ONNX-to-Cairo model compilation
- 2023: Giza Transpiler launches — converts ONNX models to Cairo; initial ZK-ML proofs demonstrated
- 2024 (Q1): Giza Agents platform launches — autonomous AI agent framework with ZK-verifiable action proofs
- 2024 (Q2–Q4): AI agent meta accelerates adoption interest; Giza’s verifiable AI angle differentiates in crowded agent market
- 2025: Production deployments of ZK-ML in DeFi protocols; broader ecosystem development
Common Misconceptions
“Giza runs ML models on-chain.”
Running full ML inference on-chain (inside a smart contract) is computationally prohibitive. Giza runs inference off-chain and generates a ZK proof of correct execution — the smart contract only verifies the proof (a fast, cheap operation), not the full model.
“Giza Protocol has a public token.”
As of early 2025, Giza Protocol does not have a publicly tradeable native token — it is an infrastructure platform operating pre-token, with revenue from developer services.
Criticisms
- Proof generation cost: Generating ZK proofs for ML inference is computationally expensive — currently adding significant latency and cost vs. simple oracle calls. For many DeFi use cases, the trustlessness benefit doesn’t justify the overhead
- Model complexity limits: Current ZK-ML systems handle relatively small models efficiently — large neural networks (GPT-scale) are not feasible to prove with current ZK infrastructure
- Adoption bootstrapping: For ZK-ML to matter for DeFi, protocols must choose to use it over simpler oracle solutions — adoption requires significant developer education and infrastructure standardization
- Early-stage infrastructure: The ZK-ML space is still in early R&D — Giza is one of several teams (Modulus Labs, EZKL) racing toward production-grade solutions, and the winning approach is not yet clear
Social Media Sentiment
Giza Protocol is respected in the technical ZK and AI intersectionist communities as a genuinely innovative project solving a real problem. Lower public visibility than AI agent personality projects — it’s infrastructure-layer work that doesn’t translate into easy marketing narratives. Seen positively as “serious builders” in the ZK-ML space.
Last updated: 2026-04
Related Terms
Sources
- “Giza: On-Chain Verifiable ML with ZK Proofs” — Giza Protocol Documentation (2023). Technical overview of Giza’s ONNX-to-Cairo compilation pipeline and ZK-ML proof architecture.
- “ZK-ML: Zero-Knowledge Machine Learning Survey” — UC Berkeley / Independent Research (2024). Academic survey of the ZK-ML field — covering proof systems used, supported model architectures, and computational overhead.
- “Giza Agents: Autonomous AI with Verifiable Actions” — Giza Blog (2024). Announcement and technical description of Giza’s autonomous agent platform with ZK-verifiable action proofs.
- “Cairo: ZK-Native Smart Contract Language” — StarkWare / Starknet Documentation (2023). Technical overview of Cairo — the ZK-native language used by Starknet and the foundation for Giza’s proof generation.
- “The Verifiable AI Primitive: What Smart Contracts Need from Machine Learning” — Paradigm Research (2024). Analysis of how ZK-ML enables new DeFi primitives — risk pricing, credit scoring, and autonomous agent decision verification.