In a world where artificial intelligence is advancing faster than our ability to fully understand or control it, one company is tackling a deceptively simple but critical question: How can we trust AI?
Enter @Lagrange Official a pioneering team at the intersection of cryptography and machine learning, with a bold mission — to make every AI decision provable.
Their flagship product, DeepProve, is already being recognized as the fastest system for zero-knowledge machine learning (zkML). In practical terms, it allows developers, regulators, and users to mathematically verify what an AI model did, without revealing sensitive data like the input, the output, or the model itself.
This isn’t just a privacy breakthrough. It’s about accountability, safety, and transparency in AI — and it’s coming at a time when the world desperately needs it.
A New Layer of Trust for AI
Imagine asking an AI system to analyze your medical records and give a diagnosis. Now imagine receiving a confident answer — but with no way to tell if the model was certified, whether your private data was misused, or even if the result was computed honestly.
That’s the status quo.
With DeepProve, Lagrange flips the script. You still get the diagnosis, but this time, it comes with a cryptographic proof that says, “This result was generated by this specific model, on your exact input, with no funny business.” No need to trust the developer, the cloud provider, or even the model itself. You can just verify it.
“We want to make cryptographic proofs a default for AI,” says Lagrange co-founder Ismael Hishon-Rezaizadeh. “It should be crazy not to verify what an AI is doing — especially when lives, rights, and massive financial systems are at stake.”
Under the Hood: What DeepProve Actually Does
At its core, DeepProve is a developer tool that wraps AI models in zero-knowledge proofs. You don’t need to understand cryptography to use it — just export your trained model (usually in ONNX format), run a one-time setup, and then let DeepProve handle the rest.
Every time the model is run, DeepProve creates a succinct proof — think of it like a cryptographic receipt — showing that the inference (the prediction or output) was computed honestly. Crucially, it doesn’t expose the input data or the model’s inner workings.
The secret sauce lies in two mathematical tricks:
Sum-check protocols, which efficiently verify big linear algebra operations like matrix multiplications — the backbone of every neural network.
Lookup arguments, which allow DeepProve to validate non-linear functions like ReLU or softmax without recomputing them from scratch.
These techniques make DeepProve astonishingly fast. In benchmark tests, it was up to 1000× faster than other zkML systems when generating proofs, and over 600× faster in verification time.
In other words, this isn’t a research prototype. It’s production-ready infrastructure for building verifiable AI at scale.
Built for Real Use Cases, Not Just Theory
What does verifiable AI actually look like in the wild? Lagrange has already shown some powerful use cases:
In healthcare, a hospital can prove that an AI diagnosis came from a certified model, without revealing the patient’s medical history.
In finance, a DeFi protocol can prove its trading bot wasn’t front-running users, or that it made a fair decision — without disclosing its algorithm.
In government and defense, autonomous agents (like drones or surveillance tools) can be audited post-facto, proving their decisions were algorithmically valid — and aligned with policy.
One particularly fun demo was Turing Roulette, an interactive online game where hundreds of thousands of players tried to guess whether they were chatting with a human or AI. Every inference — over 3.7 million in total — was proven in real time using DeepProve.
If you can prove AI inferences live in a game played by half a million people, you can probably prove them in a regulated financial system or hospital, too.
Why This Matters Now
The stakes are rising. AI is no longer confined to toy problems or lab experiments. It's writing legal documents, generating code, making hiring recommendations, analyzing x-rays, and even flying drones.
At the same time, AI is becoming increasingly opaque. Deep neural networks are so complex that even their creators can’t always explain how they work — let alone prove they’re doing the right thing.
And trust? That’s wearing thin. Headlines about biased algorithms, deepfakes, and unauthorized data usage are eroding public confidence.
This is where zkML comes in — and why Lagrange is getting so much attention.
Backed by Giants, Built by Visionaries
In 2024, @Lagrange Official raised over $17 million in seed funding, led by Founders Fund (Peter Thiel’s firm), with participation from 1kx, Maven 11, Archetype, Fenbushi, and other crypto-native VCs.
They’ve also joined NVIDIA’s Inception program — a big nod from the AI hardware titan — and are working closely with Intel on hardware acceleration for proof generation. On the blockchain side, Lagrange is integrated with major protocols like EigenLayer, zkSync, Base, Mantle, and others.
Lagrange’s network of “provers” — distributed nodes that generate zk proofs — runs on EigenLayer, a restaking platform built on Ethereum. Provers are rewarded for honest work and penalized if they mess up. It’s a scalable, incentive-aligned infrastructure that makes global AI verification possible.
How Lagrange Compares to Others in the Field
They’re not alone in this space — but they are different.
Aleo is building privacy tools for on-chain applications, including ML. But it’s more like a general-purpose ZK blockchain than a specialized AI verifier.
RISC Zero offers a zkVM that can verify general Rust programs, including AI — but it’s slower, since it isn’t tailored to ML.
Modulus Labs is working on AI verifiability too, but mostly for DeFi bots and smart contracts.
zkSync, while best known for Ethereum scaling, is also exploring zkML — and is already tapping into Lagrange’s prover network.
Lagrange stands out by laser-focusing on fast, scalable AI proof generation, built with real-world developers and applications in mind.
The Future: Verifiable Everything
Looking ahead, Lagrange has an ambitious roadmap:
Support for larger models, including transformers and LLMs like GPT and Claude.
Proofs of training, so developers can verify how a model was trained — not just how it was used.
Fairness and bias audits, done cryptographically.
Hardware acceleration, through partnerships with Intel and NVIDIA.
Cloud integrations, so any AI-as-a-service provider can offer verifiable outputs with minimal overhead.
The end goal? A world where every AI system, everywhere, runs with built-in accountability. Like HTTPS for websites, Lagrange wants zkML to become a universal standard for AI trust.
Final Thoughts
What Lagrange is building with DeepProve isn’t just a faster ZK engine. It’s an entirely new trust architecture for the AI age.
In an era of black-box algorithms and rising AI anxiety, they offer something rare and invaluable: proof.
Not marketing claims. Not brand trust. Not “take our word for it.”
Mathematical, auditable, unforgeable proof.
And that may just be what it takes to align AI with the future we actually want.