## Inference Labs  
### Auditable Autonomy for ML Engineers

Autonomous models make real-world decisions, but their inferences are not verifiable.

Humans rely on cryptographic identity to secure access, payments, and accountability. ML systems lack a standard way to prove what a model computed, how it was executed, or whether an output has been tampered with.

**Inference Labs builds verification primitives for machine learning.**

We pioneered **Auditable Autonomy**, a research-driven framework that brings cryptographic verification to model inference. Our **Proof of Inference** approach makes every forward pass, agent decision, and workflow execution independently verifiable, while preserving IP through **veiled model weights and biases**.

On this page you will find:
- Research on verifiable and auditable inference
- Benchmarks for integrity, performance, and overhead
- Models and reference implementations with built-in verification
- Tools for protecting proprietary weights and biases
- Experiments for deploying ML in adversarial and regulated environments

Verification becomes a first-class primitive for ML systems, enabling trustworthy deployment at scale.