Attestable
Attestable builds zero-knowledge proof technology that lets organizations cryptographically verify what an AI system actually computed — which model, which weights, which inputs, and which policy were used to produce a given output — without exposing the model itself or the underlying data. The pitc
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Attestable builds zero-knowledge proof technology that lets organizations cryptographically verify what an AI system actually computed — which model, which weights, which inputs, and which policy were used to produce a given output — without exposing the model itself or the underlying data. The pitch is that as AI systems are given more autonomy and access to sensitive workflows, trust can no longer rest on a vendor’s word, a hardware enclave, or an operator’s attestation; it has to rest on a mathematical proof that can’t be faked, cloned, or extracted.
Founded in 2025 by three mathematicians — CEO Yogev Bar-On, CTO Shahar Papini, and VP of R&D Shahar Samocha, with backgrounds spanning RAND, Unit 8200, StarkWare, and the Sui blockchain — the Israeli company came out of stealth in August 2026 with a $20 million Seed round led by TLV Partners and Altimeter Capital, alongside Netz Capital, Cerca Partners, and individual investors including Wiz co-founder Assaf Rappaport and Armis co-founder Yevgeny Dibrov.
Innovation Matrix Assessment
Came out of stealth in 2026 with a novel zero-knowledge proof approach to AI verification, a genuinely new technical angle on AI trust rather than an incremental feature.
Deploying zero-knowledge cryptographic verification into live enterprise AI pipelines is nontrivial and unproven at this stage; the company has not yet demonstrated broad operational deployment.
A $20M Seed round led by TLV Partners and Altimeter Capital, with participation from Assaf Rappaport and Yevgeny Dibrov, is a credible signal for a pre-launch startup, independently reported in August 2026.
Proposes a genuinely different trust model for AI systems — cryptographic proof instead of vendor attestation — addressing a real and growing concern as AI agents gain more autonomy, though it remains conceptually early.
As a pre-launch, seed-stage company with no disclosed enterprise deployments yet, there is no independent evidence of real-world efficacy beyond the founders' and investors' public statements.
AI system trust and verifiability is a rapidly rising enterprise and regulatory concern as autonomous AI agents take on more consequential tasks, keeping this category highly relevant.
Why CISOs Should Care
Offers a way to cryptographically verify AI system behavior — proving a specific, approved model and policy produced a given output — rather than relying on vendor claims or trust in the AI provider's infrastructure.
What Makes It Different
Uses zero-knowledge proof cryptography rather than hardware attestation (secure enclaves) or policy-based monitoring, aiming to make AI integrity mathematically provable instead of merely asserted.
The Matrix Verdict
53/100 — INCREMENTAL INNOVATOR
A very early-stage, technically ambitious bet on zero-knowledge proofs for AI verification, backed by a credible seed syndicate and a founding team with strong cryptography pedigree; real-world efficacy at enterprise scale is unproven this early.
Editorial Note: Claims vs. Verified Findings
Funding amount, investors, and founder backgrounds are independently reported (Calcalist, Fortune-adjacent outlets, Altimeter and TLV Partners' own statements); specific performance or overhead claims for the ZK-proof system are company-sourced and not independently benchmarked here.
Sources
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