Collaboration
PROTEX is an independent research, teaching, and applied AI programme focused on knowledge architecture, AI evaluation, and human-AI decision systems.
PROTEX works at the intersection of research, education, and practical AI system design. The programme explores how structured knowledge, uncertainty, retrieval, governance, and human judgement shape the behaviour of AI systems.
Collaboration may involve research papers, teaching activities, guest lectures, postgraduate modules, workshops, AI evaluation projects, benchmark design, knowledge architecture, governance research, case studies, and applied AI systems.
The aim is not only to study AI systems, but also to demonstrate, teach, and evaluate how they operate when they are grounded in real knowledge environments.
PROTEX welcomes thoughtful collaboration with universities, researchers, training providers, organisations, practitioners, and domain specialists interested in trustworthy AI systems, structured knowledge, education, evaluation, and responsible human-AI decision support.
Collaboration may develop across three main areas.
Research Collaboration
Joint work on AI evaluation, knowledge architecture, Human-AI Collaboration, governance, corpus design, retrieval systems, and decision-support environments.
- Research papers and publications
- Benchmark and evaluation studies
- Case studies and methodological frameworks
- PhD-related research discussions
Education & Teaching
Lectures, workshops, postgraduate teaching, and practical demonstrations using PROTEX as a research and teaching environment for AI systems.
- Guest lectures and seminars
- Postgraduate programme modules
- AI evaluation workshops
- Live demonstrations of AI prototypes
Applied AI Projects
Practical collaboration around enterprise AI, knowledge management, AI governance, retrieval quality, Copilot environments, and knowledge-based assistants.
- AI reliability assessments
- Knowledge architecture audits
- RAG and Copilot knowledge environments
- Decision-support and onboarding systems
PROTEX can be used as a teaching demonstrator for modern AI systems.
PROTEX includes a working AI prototype that can be demonstrated through a chat-based interface. This makes it possible to show how AI systems retrieve, organise, analyse, and interpret structured knowledge in real time.
Teaching activities may focus on knowledge architecture, AI evaluation, Human-AI Collaboration, RAG systems, AI governance, uncertainty handling, decision-support environments, and the limits of automated reasoning.
The system is especially useful for showing that AI evaluation is not only a question of whether an answer is correct. It also involves evidence grounding, source traceability, uncertainty, completeness, response stability, and the distinction between fact, analysis, and interpretation.
Collaboration can support research papers, benchmarks, and AI evaluation projects.
PROTEX provides a methodological environment for studying how AI systems behave when operating on structured knowledge repositories. The programme supports work on benchmark design, retrieval stability, reliability assessment, AI assurance, and evaluation methodologies.
This includes research into factual grounding, false-premise handling, uncertainty preservation, semantic contamination, evidence adherence, omission risk, and the role of knowledge architecture in trustworthy AI performance.
Open to universities, training providers, organisations, and independent contributors.
Collaboration is not limited to one professional background. Relevant contributions may come from academic research, teaching, organisational practice, policy work, technical development, domain expertise, or independent inquiry.
- Universities and postgraduate programme teams
- Training providers developing AI-related courses
- Researchers working on AI, governance, evaluation, or knowledge systems
- Organisations implementing AI-supported decision processes
- Companies using Microsoft Copilot, RAG systems, or internal AI assistants
- Policy, governance, compliance, and risk professionals
- Students interested in AI evaluation and knowledge architecture
- Domain specialists with relevant subject-matter expertise
Collaboration is shaped by the question, not by a fixed format.
Some collaborations are narrow and project-specific. Others develop through repeated discussions, shared teaching interests, research questions, or applied work across several areas.
The common thread is interest in how knowledge, uncertainty, evidence, governance, and human judgement interact inside AI-supported systems.
Explore a potential collaboration.
If you are working on related problems, developing AI education, exploring AI evaluation, or implementing knowledge-based AI systems, feel free to get in touch.
Suitable for research conversations, teaching proposals, postgraduate modules, workshops, benchmark projects, evaluation work, governance studies, corpus design, case studies, and applied AI collaboration.
Collaboration Enquiries
For research, education, workshops, AI evaluation, project proposals, and related enquiries: