Empowering SMEs with AI-driven automation for scalable, intelligent business operations
xWRK specializes in deploying AI-driven agent systems that automate repetitive tasks, optimize workflows, and enhance decision-making for enterprises. Our solutions are designed to integrate seamlessly with existing IT infrastructure, providing a scalable framework for businesses of all sizes.
We focus on delivering tailored AI solutions to small and medium enterprises (SMEs) in the UK, helping them leverage cutting-edge technology without compromising on scalability or cost-effectiveness. Our approach combines advanced machine learning models with enterprise-grade automation to drive operational excellence.
Ryan Loughty, a former researcher at the University of Toronto (2015–2018), pioneered work in deep learning and neural network scaling theory. His research on optimizing AI agent swarms for complex systems inspired the founding of xWRK in 2022. This foundation enables us to deliver scalable, intelligent automation solutions that address real-world business challenges.
Started PhD research at the University of Toronto, focusing on neural network scalability and distributed learning.
Published key papers on optimizing AI agent swarms for parallel processing and real-time decision-making.
Transitioned from academia to industry, founding xWRK to apply his research in practical enterprise solutions.
We design AI systems that can scale across multiple enterprises, using distributed learning models and adaptive algorithms. This ensures our solutions grow with your business needs.
Our solutions are built with enterprise-grade security, compliance, and integration capabilities. We work closely with clients to ensure AI tools align with their specific industry requirements.
xWRK is committed to helping enterprises harness the power of AI to drive innovation, reduce costs, and improve efficiency. By combining academic rigor with practical application, we deliver solutions that transform the way businesses operate in a digital-first world.