# DexLord AI

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ALERT LOGIC EXPLANATION

DexLord AI leverages a custom-trained CHATGPT module intricately tailored to our database. It simplifies the interpretation of our metrics, warnings, risks, and beneficial metrics, making them more accessible and user-friendly for individuals of all proficiency levels

These metrics include VMR, LORDSCORE, volume, market caps, holder metrics, supply distribution, and even deployer balances. We aim to enhance the accessibility and usability of our platform for all users



**LIVE DATA & SOCIAL METRICS**

DexLord AI is also being integrated with various unique features to provide real-time online data, including social media sentiment analysis. This includes gathering information from Twitter posts, metrics, hashtags, bios, websites, copy, narratives, and identifying key figures discussing the project. Our aim is to automate the process of researching and identifying promising projects, saving users time and providing a fast, efficient way to discover new opportunities<br>

{% hint style="success" %}
DexLord AI is an ongoing project, continuously evolving to meet the needs of our users. Our roadmap includes the integration of various APIs and datasets to enhance its capabilities over time. However, our primary focus remains on optimizing its ability to interpret alerts effectively, ensuring that users receive the most relevant and actionable information.
{% endhint %}

We're focusing entirely on enhancing the Dexlord experience, and we're still brainstorming a bunch of ideas to make our Dapp and alerts even better. There's a lot of potential for what we can add and improve, and we're excited about all these possibilities.<br>

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# Agent Instructions: 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:

```
GET https://dexlord.gitbook.io/docs/dexlord-ai.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
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.