# Technical Implementation

<figure><img src="/files/Km16SRKjlYTrUwkViJkt" alt=""><figcaption></figcaption></figure>

**Luntra's MEV Radar** revolutionizes blockchain protection through intelligent, real-time defense:

#### &#x20;AI-Powered Mempool Analysis

* Using graph neural networks, find coordinated MEV assaults by examining transaction relationship patterns.&#x20;
* Time-Series Models: Monitor pending transactions for temporal trends to forecast sandwich attempts&#x20;
* PyTorch Integration: 94% accurate bot detection using custom-trained models on historical MEV data&#x20;
* Continuous mempool monitoring with sub-second threat assessment is known as real-time scanning.

#### Dynamic Protection Mechanisms

* AI uses probability scoring to give each cluster of pending transactions a threat rating between 0% and 100%.&#x20;
* Automatic Rerouting: Luntra's private relayer immediately reroutes high-risk transactions.&#x20;
* Smart Reordering: In block construction, detrimental MEV attempts are deprioritized, which benefits users.&#x20;
* Gas Refund System: Automatic gas compensation for impacted users is triggered by detected MEV incidents.

#### Integrated Chain-Level Defense

* Integrated Security: No need for manual opt-ins or external RPC services&#x20;
* ChainSage Wallet Integration: Integrated security for all wallet transactions&#x20;
* On-Chain MEV Detection: Prior to execution, smart contracts can query MEV risk scores.&#x20;
* Private Relayer Network: Specific infrastructure for submitting transactions in a secure manner

***

### Success Scenario Example

Let's see how Alex's trading experience transforms with Luntra MEV Radar:

Alex initiates the same $10,000 USDC to ETH swap, but now on Luntra:

**Pre-Transaction Analysis (0.1 seconds)**:&#x20;

* MEV Radar looks for odd grouping around ETH/USDC pairs in the mempool.&#x20;
* Based on comparable historical trends, AI models determine that there is an 87% chance of a sandwich attack.&#x20;
* Coordinated bot behavior targeting Alex-sized trades is exposed by graph neural networks.

**Automatic Protection Activation (0.2 seconds)**:&#x20;

* Rerouting through Luntra's private relayer is instantaneous when the threat score is high.&#x20;
* By avoiding the public mempool, the transaction avoids bot detection.&#x20;
* The best course of action is pre-validated by the smart contract.

**Secure Execution (3 seconds)**:&#x20;

* With no MEV intervention, the trade is executed at the fair market price.
* &#x20;Alex gets all of the anticipated ETH tokens.&#x20;
* A successful MEV prevention event is recorded by the system.

**Result**: Alex earns an extra 0.08 ETH, pays regular gas fees, and saves $350 that would have been forfeited to MEV. There was no need for configuration because the entire protection process was autonomous and invisible!


---

# 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://luntra.gitbook.io/luntra-infrastructure/luntra-mev-radar/technical-implementation.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.
