Vrda is a blockchain-based real estate investment platform designed to lower entry barriers by enabling fractional property ownership through NFTs on the Ethereum network. Owais and his team worked on structuring a solution to address long-standing challenges in traditional real estate, including high capital requirements, illiquidity, slow ownership transfers, and limited transparency. Market analysis highlighted growing interest in tokenized assets and decentralized finance, revealing the need for a secure, borderless platform where investors could access real estate opportunities using cryptocurrency while maintaining clear and verifiable ownership records. The platform tokenizes real-world properties into ERC721A NFTs, allowing users to purchase fractional shares using ETH or USDT, with smart contracts automating ownership, claims, and governance. Owais and his team supported the integration of an admin dashboard for managing property listings and availability, alongside APIs connecting property NFTs to VR and metaverse environments, and the introduction of the DaMeta1 ERC20 token for claimable rewards. This approach improved accessibility, increased transaction efficiency, enhanced transparency through on-chain records, and positioned Vrda as a forward-looking real estate investment platform bridging physical assets with digital and immersive experiences.
Next Gen Snipping – AI-Powered Automated Crypto Trading
This AI-powered crypto trading bot was built to automate memecoin spot trading on centralized exchanges such as MEXC, leveraging the Hummingbot framework for strategy execution and market connectivity. Owais and his team contributed to designing a modular platform that addressed common challenges in automated trading, including rigid strategy management, lack of real-time visibility, and security risks around exchange API keys. Market analysis highlighted growing demand for always-on trading systems capable of handling high volatility while remaining configurable, observable, and extensible for future AI and DeFi use cases. The solution introduced secure authentication with 2FA, encrypted API key handling, and flexible strategy management through validated YAML files with hot-reload support. Owais and his team helped implement real-time monitoring dashboards displaying PnL, volume, and bot health, alongside live operational logs for rapid debugging and transparency. Each trading bot runs in an isolated Docker container, ensuring scalability and fault isolation, while AI-ready JSON signal injection laid the groundwork for adaptive trading logic. The result was a production-ready MVP that enabled safe, real-time strategy control and positioned the platform for Phase 2 expansion into AI training, futures trading, and decentralized exchange integrations.