As artificial intelligence reshapes how knowledge is created and shared, the most impactful innovations are emerging where technology meets human insight. Fatai Alimi is part of this wave. A software engineer, AI researcher and co-founder of Grelixir Ltd, a fast-growing technology company building intelligent software tools for modern businesses, Alimi is focused on translating advanced AI research into practical, real-world systems.
With a Master’s degree in Software Engineering, completed with distinction in 2024 at Staffordshire University, United Kingdom, Fatai represents a new generation of engineers exploring how artificial intelligence can enhance, rather than replace, human expertise. His work centres on the integration of large language models into systems designed to support researchers, professionals and organisations in making better, faster decisions.
One of his most notable projects is an intelligent software platform that applies AI to systematic literature reviews, a process long recognised as one of academia’s most demanding tasks. Faced with an ever-growing volume of research publications, scholars often spend months reviewing and synthesising hundreds of papers. Fatai’s platform is designed to ease that burden by enabling users to define review criteria while the AI retrieves, filters, categorises and summarises relevant studies. Currently under review for academic publication, the project aims to make high-quality research synthesis more efficient and accessible.
Beyond this work, Fatai is also contributing to research at the intersection of artificial intelligence, energy and human wellbeing. He collaborates with other researchers on studies involving physical activity classification, exploring how machine learning models can interpret sensor data to better understand behavioural and energy-related patterns. The work reflects his broader interest in applying AI to problems with tangible societal impact.
Outside of research and product development, Fatai is deeply committed to mentorship and service. He serves as an Expert Coding Facilitator at GT Scholar, where he teaches young people core programming skills, and volunteers as a software engineer with organisations including Free UK Genealogy and WYKEI. For him, technical excellence and community contribution are closely linked.
In this conversation, Fatai reflects on his journey into technology, his approach to building human-centred AI systems, and his vision for integrating large language models into tools that empower people and organisations.
Tell us about your background and how you found your way into technology.
I have always been curious about how systems work, from simple mechanical processes to complex digital platforms. That curiosity gradually drew me into software engineering, where I became interested not just in writing code, but in designing systems that scale, adapt and learn.
During my Master’s degree, I developed a deeper interest in artificial intelligence, particularly large language models and their potential to make knowledge more accessible. That interest now shapes my work across both research and industry, including my role at Grelixir Ltd, where we focus on building intelligent tools that help organisations operate more efficiently.
What inspired your work on automating systematic literature reviews using AI?
The inspiration came from a shared challenge in academic research. Systematic literature reviews are essential, but they require an enormous amount of time and mental energy. During my postgraduate studies, I experienced this first-hand and began exploring whether AI could help reduce that workload.
The platform I developed functions as an intelligent research assistant. It can retrieve and analyse academic papers, extract key insights and align them with user-defined criteria. By handling the most time-consuming elements of the process, it allows researchers to focus more on interpretation, originality and critical analysis.
You’re also working at the intersection of AI and energy. Can you tell us more about that?
I’m involved in collaborative research that explores how artificial intelligence can be applied to energy-related challenges, particularly through physical activity classification. This involves training machine learning models to interpret sensor data in ways that improve our understanding of human behaviour and energy usage patterns.
What excites me about this work is its practical relevance. It demonstrates how AI can contribute to outcomes related to efficiency, sustainability and wellbeing, rather than remaining purely theoretical.
Why do you invest time in teaching and volunteering?
I strongly believe that knowledge should be shared. Mentors played a key role in my early development, and mentoring others is a way of continuing that cycle.
Through teaching and volunteering, I’m constantly reminded that technology is ultimately about people. Whether you’re writing software for research or teaching someone their first programming concept, the goal should always be meaningful impact.
What is your long-term vision for AI and large language models?
My focus is on building human-centred AI systems. While research is advancing rapidly, there is often a gap between innovation and practical usability. My goal is to help bridge that gap by creating systems that people can trust and use effectively.
I see large language models evolving into collaborative tools that enhance human creativity, reasoning and decision-making, while remaining ethical, transparent and context-aware.
What advice would you give to aspiring engineers or researchers?
Start small, but start early. Curiosity, consistency and a willingness to experiment matter more than getting everything perfect from the beginning.
It’s also important to learn how to communicate clearly. Clear thinking leads to better systems, better collaboration and better outcomes.
Any final thoughts?
I am currently refining my research software while continuing to strengthen connections between academic research and industry applications. There is significant potential in building tools that move seamlessly from theory to practice.
Ultimately, my aim is to help researchers, engineers and organisations see AI not as an abstract concept, but as a practical partner in creating smarter, more effective solutions.
