Yes. ReN supports students in producing evidence-based, well-researched academic work, including case studies, presentations, research projects and business analysis. By providing access to millions of data points from global company filings and disclosures, ReN helps students analyse real-world organisations, develop critical thinking and build stronger, evidence-led arguments.

ReN’s AI-powered analysis accelerates research, brings together relevant insights from large volumes of company data and helps students work with up-to-date information. Data points are linked to their sources, enabling students to verify findings, reference supporting evidence and demonstrate how they reached their conclusions.

For faculty and supervisors, this source-level traceability supports the review of research methods, verification of evidence and greater transparency throughout the assessment process.

ReN acccelerates research time and gives more time to thinking critically about what it means. By bringing together extensive global company data and using AI to analyse filings, disclosures and financial performance, ReN helps students uncover insights, investigate business challenges and identify evidence to support their arguments. Every finding is linked to its source, allowing students to verify information, explore the underlying evidence and draw their own conclusions.

Rather than replacing independent research, ReN provides the tools for students to ask better questions, compare perspectives, test assumptions and develop their analytical skills. Students remain responsible for interpreting findings, constructing arguments and producing their own assessed work in accordance with their institution's academic integrity and AI-use policies.

AI-powered analysis helps users interrogate large volumes of information quickly, identify patterns and compare companies or sectors across markets without manually processing every document. Every ReN insight is linked to its underlying source, allowing users to trace findings back to the original evidence, validate AI-generated insights and build a transparent audit trail for academic research, case studies and presentations.

Built from the latest primary disclosures through ReN’s data sources, users can investigate business performance and emerging developments using current information, while retaining the ability to verify the evidence and draw their own conclusions.

ReN is designed to make AI-powered research transparent, traceable and evidence-led. Its analysis is grounded in verified corporate data, with insights linked to their underlying sources to help users validate findings. This approach significantly reduces the risk of unsupported AI-generated claims. Where ReN cannot establish a sufficiently reliable answer, it can decline to respond or provide a low-confidence score, helping users understand when further investigation is needed.

ReN does not use user data to train its AI models and is protected by SOC 2 security controls, helping universities safeguard student and institutional data in line with their data privacy, security and governance requirements.

ReN is ready to use, enabling students and faculty to get started immediately without complex technical integration or specialist training. Universities can introduce ReN to a single class, module or research team to explore its value in practice, with applications spanning undergraduate and postgraduate study.

Yes. Every data point in ReN is audit ready, with automated traceability back to the original data source and even page and paragraph. This transparency enables students, lecturers and researchers to validate information, review supporting evidence and demonstrate the basis for their conclusions. It also supports academic assessment, published research, research review and institutional oversight, while encouraging independent thinking and evidence-based analysis.