RealEye is a powerful online research tool designed for conducting webcam eye-tracking studies. It enables users to track visual attention and analyze data in real-time via their web browser. With user-friendly features, RealEye is perfect for a variety of applications, including UX testing, advertisement analysis, and market research. This solution eliminates the need for specialized hardware, making eye-tracking studies more accessible and cost-effective.

ResearchGOAT is a generative AI-powered qualitative research platform that automates research processes, including real-time, live qualitative interviews in any language. By leveraging cutting-edge AI, ResearchGOAT captures the richness and nuance akin to human moderation. This platform is ideal for research professionals looking to simplify and enhance their qualitative research, making it easier, faster, and more affordable while maintaining high-quality insights.

Virtual Scientists leverages GPT-based language models to create specialized AI Agents that replicate expert scientists across various fields. Each virtual researcher is configured with tailored prompt engineering to provide accurate, context-aware answers, propose experimental protocols, interpret scientific data, and generate insights. Users select a scientific persona, input their questions or project details, and receive detailed, discipline-specific guidance supported by references and reasoning for educational or research purposes. The platform is hosted on GitHub Pages and is fully open-source. The codebase supports easy customization and extension of new scientific personas by modifying JSON configuration files. Ideal for collaborative research, teaching demonstrations, or personal study, Virtual Scientists bridges the gap between AI language models and practical scientific problem-solving by offering a dynamic, interactive environment for exploring complex topics with expert-like guidance.

TxAgent is a web-based AI-driven platform designed to accelerate translational research and clinical decision support by analyzing multi-omics and clinical datasets. Users upload patient gene expression or mutation profiles, configure disease context and drug libraries, and run an automated pipeline that integrates network biology, machine learning models, and drug-target interaction databases. The agent returns ranked lists of single and combination therapies, toxicity-adjusted scores, pathway enrichment analyses, and interactive network visualizations to guide experimental design and therapeutic hypotheses.