Steav offers a pre‑training platform that embeds attestation capabilities into AI models, allowing organizations to verify the trustworthiness of model outputs. The service launches its first round in Q3 2026, providing a verifiable trust layer for developers and enterprises that need auditable AI performance and compliance.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations deploying AI models often lack mechanisms to verify that model outputs are trustworthy, auditable, and compliant with regulatory standards, leading to risk of undetected errors or bias.
Solution
Steav provides a pre‑training platform that incorporates attestation capabilities directly into AI models. By embedding a verifiable trust layer during the pre‑training phase, the platform enables developers and enterprises to generate cryptographically signed evidence of model performance and compliance for each inference. This attestation can be audited by downstream systems or regulators, ensuring that model behavior meets defined trust criteria. The service is scheduled to launch its first round in Q3 2026, offering an out‑of‑the‑box solution for building auditable AI pipelines without extensive custom tooling.
Target Audience
Primary customers are AI development teams and enterprise organizations that require provable model reliability and regulatory compliance for their deployed AI systems.
Features
- Pre‑training workflow that injects attestation modules into model architectures
- Cryptographic signing of inference results to create tamper‑evident proof of output integrity
- Built‑in compliance checks that generate audit logs aligned with common AI governance frameworks
- API for developers to query and verify attestation data programmatically
- Compatibility with major machine‑learning frameworks and cloud training environments
- Scalable cloud infrastructure to support enterprise‑level model training volumes