Fatty
Ment Tech Labs partnered with the Fatty team to build a multi-chain trading and yield optimization protocol that bridges DeFi intelligence with user automation. The platform integrates AI-assisted trading strategies, automatic compounding, and real-time portfolio tracking, empowering both retail and institutional investors to earn smarter.
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Fatty was conceived as a next-generation DeFi ecosystem that merges automated trading intelligence with transparent revenue sharing. The founders identified a major gap in the crypto market where most traders lacked access to reliable automation tools and real-time yield strategies that could adapt to volatile market conditions.
The vision behind Fatty was to create an accessible, intelligent protocol that allows anyone to earn from algorithmic trading performance. Through its product FatBot, users gain access to a trading bot powered by machine learning models that execute optimized sniping and compounding strategies across multiple networks.
Fatty’s revenue-sharing model was a standout feature, designed to give token holders a consistent 50 percent share of platform-generated fees. The project aimed to redefine passive income in DeFi by combining transparency, automation, and interoperability, all under one ecosystem.
Ment Tech Labs helps projects like Fatty turn complex trading logic into real-time, revenue-generating ecosystems powered by machine learning and multi-chain infrastructure.
Before partnering with Ment Tech Labs, SocialSwap had a strong vision i.e. to combine trading with social interaction. But executing that vision within a decentralized framework presented unique challenges that required both blockchain expertise and product clarity.
Inefficient Multi-Chain Execution Layer
The trading engine struggled to synchronize order routing and yield strategies across Ethereum, BSC, and Arbitrum, causing delays in transaction execution.
Inflexible Trading Algorithms
FatBot’s early AI models operated on static datasets and lacked real-time adaptability, resulting in missed opportunities during high-volatility events.
Bottlenecks in Smart Contract Throughput
High-frequency trading activity triggered gas spikes and execution lag within the on-chain automation layer, directly impacting user profitability.
Complex Revenue Tracking Mechanism
The 50 percent revenue share model required an auditable and automated accounting system that could distribute ETH or SOL rewards accurately to thousands of wallets.
Scalability Constraints During Market Peaks
Concurrent trading sessions generated overload on backend compute clusters, limiting uptime and performance consistency during volume surges.
Limited Data Intelligence for Strategy Optimization
The early version lacked predictive analytics and real-time feedback loops that could help the AI models improve trading precision over time.
Ment Tech Labs’s Approach to Engineering Fatty’s AI-Driven DeFi Ecosystem
To transform Aurix Technologies’ vision into a fully functional, user-focused exchange ecosystem, Ment Tech Labs followed a structured and agile approach. The goal was to create a secure, high-performance, and reward-driven platform that could serve both professional traders and everyday users.
Discovery and Research Phase
Our team conducted a full technical audit of Fatty’s trading engine, token architecture, and AI model dependencies. Using live test data from DeFi protocols across Ethereum, Arbitrum, and BSC, we mapped the latency bottlenecks and liquidity routing gaps that hindered real-time execution. We also benchmarked similar automation systems to design a framework capable of maintaining sub-second performance under market stress.
Architecture and System Design Phase
Ment Tech Labs built a modular architecture that separated the trading logic, reward engine, and multi-chain connectors into independent layers. This enabled cross-network yield aggregation and minimized gas congestion. An optimized node communication protocol was designed to handle simultaneous trades across networks without compromising synchronization.
AI Model Integration Phase
We integrated adaptive machine learning models trained on live order book data and volatility indicators. The models continuously updated trading parameters, such as position size, entry points, and stop-loss thresholds, based on dynamic market sentiment. A retraining pipeline was implemented using Python and TensorFlow to ensure the models improved after each cycle.
Smart Contract Development Phase
Custom Solidity contracts were built to automate compounding, liquidity injection, and revenue sharing. Each contract was optimized for gas efficiency and designed to handle high-frequency execution safely. The revenue engine used an on-chain accounting protocol to automatically split and distribute 50 percent of trading profits to $FATTY token holders.
Testing and Optimization Phase
Extensive simulations were performed on testnets using parallel trading loads to validate performance at peak volume. The AI agents were stress-tested with real market data across volatile trading pairs to ensure execution accuracy. Our DevOps team applied runtime analytics to monitor gas usage, model accuracy, and latency under different liquidity conditions.
Deployment and Monitoring Phase
The upgraded FatBot system was deployed on BSC and Arbitrum with multi-node redundancy and continuous monitoring. A real-time analytics dashboard was launched to track trade performance, model predictions, and validator node status. Continuous updates ensured that new chain integrations could be added seamlessly without disrupting live trading sessions.
Cross-Chain Trading Architecture
Engineered a unified DeFi infrastructure that connects Ethereum, Arbitrum, BSC, and Base networks. The system manages concurrent order execution with synchronized liquidity routing and automated chain failover for uninterrupted operation.
Intelligent Sniping 2.0 Framework
Developed an AI-driven execution engine capable of real-time sniping based on millisecond-level market movements. The system identifies token launches, detects pre-liquidity windows, and executes trades through automated smart routing.
Adaptive Yield Aggregation Layer
Created an algorithmic layer that aggregates rewards from multiple farming pools and redistributes them through auto-compounding strategies, boosting APY performance by up to 38 percent.
Predictive Market Sentiment Engine
Integrated natural language processing models trained on live market feeds and social sentiment data to predict short-term volatility trends and guide FatBot’s trading logic.
On-Chain Profit Distribution Protocol
Deployed an automated profit-sharing module that routes 50 percent of platform revenue to $FATTY token holders in real time, using an immutable accounting contract for full transparency.
Smart Contract Cluster for Performance and Security
Implemented modular smart contracts for liquidity injection, order validation, and vault management. Each contract was optimized for gas reduction and penetration-tested for attack resilience.
AI Training and Feedback Loop Infrastructure
Built an ML feedback pipeline that retrains the AI models based on post-trade analytics, order slippage data, and user performance patterns to enhance execution efficiency.
Advanced User Analytics Dashboard
Delivered a full-featured dashboard integrated with TradingView APIs, offering users access to portfolio metrics, AI model accuracy scores, and historical trade performance insights.
High-Availability Cloud Deployment Environment
Configured a scalable backend infrastructure using Kubernetes and AWS load balancers to maintain uptime above 99.97 percent during peak trading activity and token sale surges.
Our clients
The Aurix Exchange platform is a robust digital asset ecosystem that seamlessly integrates trading, payments, and
rewards. Each component is crafted for optimal reliability, scalability, and real-time efficiency.
Real-Time Sniping Intelligence
An AI-assisted trading core that scans token launches and liquidity events in milliseconds, executing trades at the optimal price entry before public exposure.
Multi-Chain Trading Engine
Supports simultaneous execution across Ethereum, BSC, Arbitrum, and Solana with unified wallet management and synchronized order settlement.
AI Agent-Driven Automation
Utilizes autonomous trading agents that learn from user behavior and market data to refine strategy selection and optimize position timing.
Revenue Share Smart Contract
Automated protocol that distributes 50 percent of platform revenue directly to $FATTY holders in real time using trustless on-chain accounting.
Gas-Efficient Execution Layer
Built with micro-optimized Solidity contracts that reduce gas expenditure by up to 27 percent while maintaining execution reliability under high-frequency trading loads.
Modular Strategy Vaults
Enables users to choose between aggressive, balanced, or conservative yield strategies with automated rebalancing and compounding logic.
Driving Performance and Efficiency Across DeFi Markets
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