Real World Use Cases

On-chain analytics: Querying historical data

One of the most impactful applications of ZK coprocessors is in the domain of on-chain data analytics. Blockchains contain vast amounts of historical data, such as user balances, contract states, and event logs. Accessing and analyzing this data in real time, however, can be expensive or infeasible directly on-chain. ZK coprocessors provide a solution by enabling developers to query historical blockchain states off-chain and return proofs that the computation was executed correctly.

Axiom is one of the earliest platforms to productize this concept. It allows smart contracts to query historical Ethereum data, such as whether a wallet held a minimum balance at a past block, without manually parsing storage or running a full archive node. The request is sent to Axiom’s coprocessor, which retrieves the data from a verified off-chain source, runs the computation inside a zkVM, and generates a proof. This proof is then submitted to Ethereum, where it is verified by a contract. The contract can then act on the result as if it were computed on-chain, with full trust.

By enabling contracts to access verified historical context, ZK coprocessors like Axiom open the door to smarter DeFi protocols, conditional governance, and time-based rewards, all while keeping the blockchain lightweight.

Cross-chain state queries and rollup bridges

Another important use case for ZK coprocessors and proof networks is enabling secure cross-chain communication. Traditionally, bridging data or assets between chains involves trusting intermediaries or using optimistic assumptions with time delays. Zero-knowledge proofs offer a trustless alternative. They allow one chain to verify a proof that a specific state or transaction occurred on another chain, without running a full node of the source chain.

Lagrange Network enables developers to perform these kinds of cross-chain queries in a verifiable way. A smart contract on Ethereum, for example, can request proof of token ownership or voting participation on a rollup like Fraxtal. Lagrange’s coprocessor fetches and processes the required state, generates a proof, and relays it through its proof network to the target chain. The receiving contract validates the proof and uses the information immediately, with no need for finality delays or trusted bridges.

Similarly, zkLink is developing infrastructure that connects liquidity and logic across multiple chains. It allows dApps to aggregate state from various networks using ZK proofs and synchronize updates without giving up security. These systems improve interoperability while maintaining strong cryptographic guarantees, making them well-suited for cross-chain lending, trading, and governance.

AI x ZK: Verifying ML outputs without revealing inputs

Zero-knowledge coprocessors are also being explored in the context of artificial intelligence. Machine learning models are increasingly used in decentralized applications, but verifying their outputs presents a challenge. If a user submits an ML result — such as a score, prediction, or classification — how can the application know that it was computed correctly and not manipulated?

ZK machine learning, or ZKML, addresses this by enabling a user to run an ML model off-chain and generate a zero-knowledge proof of its output. The proof certifies that a particular input was processed by a specific model and produced a valid result, without revealing the input itself or the internal weights of the model. This protects both user privacy and model integrity.

Mina Protocol has been a leading contributor in this space, developing zkML tools that compile neural networks into circuits compatible with ZK proof systems. Developers can run inference off-chain and post a proof on-chain, allowing smart contracts to act on verified outputs from machine learning models.

This approach enables privacy-preserving identity checks, risk assessments, and content filtering in a decentralized context. As ML models become more capable, the ability to validate their behavior trustlessly will be increasingly important.

Private gaming, Sybil-proof identity, and more

The modular nature of ZK coprocessors makes them applicable across a range of emerging use cases. In gaming, for instance, players may want to prove achievements, scores, or inventory status without revealing all game data. ZK coprocessors allow players to generate proofs of their in-game actions, which can be used for rewards, leaderboard placement, or gated content access, all while keeping sensitive data private.

In identity systems, ZK proofs can demonstrate that a user meets certain criteria — such as uniqueness, age range, or ownership history — without exposing personal information. This is critical for decentralized social platforms and DAOs that require Sybil-resistance or role-based access without relying on centralized identity providers.

Projects like Worldcoin are exploring ways to combine biometric data with zero-knowledge proofs to confirm unique humanity while preserving user anonymity. While controversial in design, the underlying proof architecture is being refined and tested through public chains like World Chain. Proof networks in these systems serve as scalable coordinators for global identity attestations.

What’s live now, what’s coming soon

Many of the use cases described above are already live or in active development. Axiom has integrated with leading DeFi protocols to support on-chain analytics with verified historical data. Lagrange’s cross-chain query infrastructure is being tested on rollups, enabling smart contracts to access data across networks. zkML tools from Mina, Risc Zero, and Modulus are being refined to support efficient neural network inference in zero-knowledge.

Proof networks such as Succinct and ZeroGravity are deploying testnets that allow developers to submit arbitrary computation requests and receive verified results via smart contract callbacks. These networks are abstracting the complexity of proof generation and delivery, making zero-knowledge infrastructure usable by developers who are not cryptography experts.

At the same time, limitations remain. Latency in proof generation, high costs for large models, and limited developer tooling are all challenges that still need to be addressed. However, the foundational building blocks — efficient zkVMs, scalable proof networks, and modular verifier contracts — are now in place.

As these systems mature, ZK coprocessors and proof networks are expected to power a new generation of applications that are trustless, private, and interoperable by default.