What if every AI decision left a cryptographic receipt you could verify on demand? Lagrange is building that exact ledger for model outputs: DeepProve, its zkML engine, produces succinct, verifiable proofs that an AI inference or pipeline actually executed as claimed. That’s auditability baked into AI, not retrofitted after the fact,.

This changes the calculus for using AI in regulated or high-stakes domains. Instead of taking a model’s output on faith, apps can publish a tiny proof on-chain showing the input, model fingerprint, and deterministic result — useful for finance, oracles, compliance, and any place where accountability matters. Lagrange’s roadmap and partner integrations (including recent enterprise ties) show a push from research demos to production-grade, low-latency proving.

The economics and engineering are nontrivial: proving at scale must balance cost, latency, and prover decentralization. Lagrange’s recent funding and infra bets aim to expand operator capacity and integrate with cloud/AI stacks so verifiable AI can actually meet real-world SLAs. If they succeed, we won’t just trust AI more — we’ll be able to prove we did.

Risks remain: prover centralization, integration complexity with huge model weights, and the perennial tension between cryptographic guarantees and practical throughput. Still, adding receipts to model outputs is one of the few technical paths that makes AI auditable at scale — and Lagrange is building the toolbox for it.

@Lagrange Official #lagrange $LA