> For the complete documentation index, see [llms.txt](https://docs.meetnuri.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.meetnuri.com/technology/ai-and-web3.md).

# AI & Web3

**AI & Intelligence Layer**

At the heart of Nuri lies its AI coaching engine powered by **OpenAI** and **Whisper**:

* **OpenAI’s GPT-based NLP** enables empathetic, context-aware conversations that evolve with user behavior. Unlike static chatbots, it delivers actionable insights that consider workout fatigue, diet consistency, or mood variability.
* AI generates **multi-domain recommendations** - such as suggesting hydration after intense cardio or recommending zinc-rich meals post-fasting.
* **Whisper**, OpenAI’s speech-to-text engine, provides **voice-enabled input**, enhancing accessibility for users with physical disabilities or those who prefer hands-free logging during workouts or cooking.

This AI layer transforms Nuri into a **virtual wellness coach** that personalizes every interaction, increasing emotional connection and stickiness.

**Blockchain & Web3 Integration**

Nuri integrates Web3 infrastructure to **tokenize wellness behavior** securely and scalably:

* **BNB Smart Chain (BSC)** powers low-cost transactions (<$0.01 avg), enabling micro-rewards without friction - a crucial design feature for habit-based incentives.
* **WalletConnect** auto-generates non-custodial wallets, allowing users to own their health data and $NURI earnings without handling private keys directly.
* On-chain governance, streak-based bonus rewards, and premium unlocks incentivize sustained engagement. According to DappRadar studies, gamified token ecosystems can increase user retention by up to **40%**.

Blockchain adds transparency, security, and a sense of ownership - turning daily effort into verifiable progress.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.meetnuri.com/technology/ai-and-web3.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.