> For the complete documentation index, see [llms.txt](https://docs.fairmath.xyz/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.fairmath.xyz/overview/intro.md).

# Intro

Privacy engine for AI and Blockchain

<figure><img src="https://90165124-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FmHu1qRdgJeHeQup4pe3R%2Fuploads%2FEthaqobPGS2lhQ506FoS%2FCover%201500_500-4.jpg?alt=media&amp;token=80f04385-8677-41a1-8e42-cb8cf5105a0f" alt=""><figcaption></figcaption></figure>

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**Fair Math** is building **cryptographic infrastructure** for **privacy‑preserving AI** and **on‑chain finance**.

#### Core products on the Fair Math stack:

* [**FHE Computer** ](/fhe-computer/overview.md)— a decentralized computing platform designed to perform operations directly on encrypted data using **Fully Homomorphic Encryption (FHE)**. It serves as the foundation for running privacy‑preserving applications and scaling secure computation in both Web2 and Web3 contexts.
* [**Fair Math AI**](https://fairmath.xyz/ai) — privacy‑native rails for running AI models and agents directly on encrypted data, built on a **FHE** optimized for scalable AI workloads.
* [**Fair Math Payments** ](https://app.gitbook.com/o/n6hALlBc1Rcd3fH2JvqV/s/mHu1qRdgJeHeQup4pe3R/~/changes/41/fair-math-payments)— is a **modular privacy framework** designed to integrate into existing on‑chain financial systems, enabling selective encryption of transactions and balances without requiring changes to the underlying infrastructure.<br>

We are committed to an **open‑source and community‑driven approach**, fostering collaboration between researchers and developers.