> ## Documentation Index
> Fetch the complete documentation index at: https://docs.apus.network/llms.txt
> Use this file to discover all available pages before exploring further.

# Welcome to APUS Network

> Deterministic, confidential, and verifiable — with on-chain proofs for every inference.

## Overview

### **Why APUS?**

APUS Network provides **trustworthy, on-chain verifiable AI inference** by combining\
**deterministic execution**, **confidential GPU compute**, and **cryptographic proofs**.\
Every inference request generates **auditable on-chain evidence**, ensuring that the model, inputs, and outputs can be independently verified — without trusting any centralized provider.

### **What APUS Enables**

* **Deterministic Execution**\
  Same input, same output—across runs and hardware. Reproducible by design (B3+ levels).

* **Confidential GPU Inference**\
  Remote attestation proves your inference ran inside a GPU TEE, binding code, model hash, and nonce.

* **On-Chain Verifiability**\
  Every inference emits **cryptographic proof headers** and **Arweave-stored audit logs**, allowing anyone to inspect or verify the result independently.

* **Decentralized & Trustless**\
  APUS uses AO + Arweave as the foundation for trust-minimized compute, enabling censorship-resistant and tamper-proof AI applications.

### **The APUS Value Proposition**

APUS is the first OpenAI-compatible inference network that delivers deterministic outputs, confidential execution, and verifiable proofs for every API request:

* A deterministic output
* A confidential execution environment
* A permanent, verifiable on-chain proof

This makes APUS the ideal foundation for **regulated AI**, **enterprise compliance**, **autonomous agents**, and **dApps requiring trustless AI logic**.

***

## Quick links

<a href="overview/vision" target="_self">
  What we build
</a>