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In a world where artificial intelligence is increasingly making high-stakes decisions — in healthcare, finance, governance, and even warfare — we face a pressing question: can we really trust AI? Not just its intentions, but its outputs? Can we be sure an algorithm’s recommendation was actually computed as claimed, without bias, tampering, or shortcuts? @Lagrange Official

Enter Lagrange, a rising startup quietly building the infrastructure for verifiable AI. Their pitch is simple, even radical: every AI output should come with a cryptographic proof. Think of it like a digital receipt that says, “Yes, this AI ran the right model, on the right input, and gave you this output — and here’s the math to prove it.”

Their flagship tool, DeepProve, is the fastest system yet for turning machine learning predictions into airtight zero-knowledge proofs.$LA And while that sounds abstract, the implications are huge: imagine hospitals validating diagnoses without exposing private scans, or regulators confirming a bank’s risk model without peeking into customer data.

This isn’t just research. Lagrange is building real tools for developers and is already working with major players in crypto, AI, and hardware. And with backing from Peter Thiel’s Founders Fund, NVIDIA, Intel, and Ethereum’s EigenLayer, they might just be onto something big.

Let’s break down what they’re doing — and why it could become one of the most important trust layers in the AI era.

A New Trust Layer for AI

Lagrange’s origin story is rooted in a core frustration: modern AI is powerful, but opaque. You rarely know how a result was computed, and you often have to take the model’s creator on faith. For founder and CEO Ismael Hishon-Rezaizadeh, that wasn’t good enough. In 2023, he co-founded Lagrange to fix it — using one of the most advanced tools from modern cryptography: zero-knowledge proofs (ZKPs).

ZKPs are kind of magical. They let someone prove that a computation was done correctly — without revealing any details about what was computed. In the case of AI, it means proving that a neural network processed a certain input and produced a certain output, without revealing the input, the output, or the model itself.

That’s the core idea behind zkML, or zero-knowledge machine learning — and it’s where Lagrange is leading the pack.

DeepProve: Turning AI Predictions into Cryptographic Guarantees

Launched in early 2025, DeepProve is Lagrange’s zkML engine. Think of it as a wrapper for AI models: it runs the model like usual, but also spits out a cryptographic proof — a tiny, tamper-proof package that confirms the model ran as expected.

This isn’t just academic. Developers can integrate DeepProve into real-world applications right now. Feed it a model (say, a fraud detector or medical image classifier), run an inference, and it will generate a succinct proof that everything was done correctly — even if the model is massive or the data is sensitive.

Better yet, that proof can be verified on-chain, off-chain, or anywhere you need. No need to trust the model provider or audit every line of code. Just verify the proof.

What Makes DeepProve Different?

Lagrange’s secret sauce is in speed and scale. Zero-knowledge proofs are powerful, but traditionally slow. Proving a simple AI model could take hours. That’s not viable for real-world apps.

DeepProve changes that. According to the company, it’s over 100x faster than previous zkML systems, and in some cases up to 1000x faster. It uses advanced cryptographic techniques (like sum-checks and lookup tables) combined with a decentralized prover network to massively parallelize the work.

Here’s how it works in plain terms:

You give DeepProve an AI model and input.

It sends the heavy computation to a network of specialized provers (kind of like a GPU-powered cloud).

These provers crunch the math, generate a proof, and return it — all in seconds or less.

You (or your user, or your smart contract) verify the result instantly.

It’s like AWS for trust — except decentralized, verifiable, and privacy-preserving.

Real-World Applications: Why This Matters

DeepProve isn’t just for crypto nerds or AI researchers. It unlocks real, practical use cases that were previously impossible or too risky:

Healthcare: AI diagnoses can now come with proof — without exposing your scan, your name, or the model’s proprietary logic.

Finance: Lenders can prove their credit scoring model was run correctly, without showing their algorithm or your income.

Web3 & DeFi: DAOs and dApps can verify off-chain AI decisions (like governance or trading bots) without bringing sensitive logic on-chain.

Cross-chain apps: DeepProve works across chains, helping protocols aggregate data or verify conditions in a trustless way.

The common theme: verifiability without exposure. In a world awash in fake news, deepfakes, black-box AI, and malicious bots, that’s a compelling promise.

A Growing Web of Partners and Backers

Lagrange isn’t building this alone. In fact, it’s already plugged into some of the biggest ecosystems in tech and crypto:

NVIDIA brought Lagrange into its Inception program — a major nod to its AI relevance.

Intel is collaborating with Lagrange on hardware acceleration for ZK proofs.

It’s a core EigenLayer AVS — meaning its prover network is secured by Ethereum’s massive validator set.

It integrates with chains like zkSync, Polygon, Base (Coinbase), Mantle, and LayerZero.

It’s also backed by Binance Labs, 1kx, Maven 11, and Archetype, among others.

Their seed round, led by Founders Fund, raised $13.2 million in mid-2024 — and total funding now sits close to $18 million. Lagrange’s tech is also being tested by major crypto infrastructure providers like Coinbase Cloud, Nethermind, and Kraken’s staking arm.

In short: they’re not just building cool crypto math in a vacuum. They’re integrating it directly into how the next wave of Web3 and AI applications will work.

What’s Next: From AI Receipts to AI Transparency

Lagrange has big plans. Their zkML tooling is just the start. Next up:

Supporting larger and more complex models (including transformers and LLMs).

Enabling proofs of training, not just inference.

Expanding their decentralized prover network to support more apps and chains.

Rolling out “Euclid,” a ZK-powered coprocessor for querying big data across chains.

Long-term, they want cryptographic verification to become as standard for AI as HTTPS is for websites. Every output, every model, every decision — provable, private, and audit-ready.

As Hishon-Rezaizadeh put it, “Every transformative technology needs its trust layer. For AI, it’s cryptographic verification.”

Final Thoughts: Why Lagrange Matters

In 2025, AI is no longer a curiosity. It’s writing code, recommending drugs, detecting fraud, and steering companies. But too often, we don’t know what it’s doing — or why. Lagrange offers a new path forward: AI that proves itself.

They’re not trying to make models smarter. They’re making them honest. In a time when trust is scarce, that might be the most important upgrade we can give artificial intelligence.

$LA

#lagrange