Unali provides a research-grade AI assistant that utilizes a proprietary dataset derived from 200 million scientific papers to deliver tailored wellness information. The platform addresses the challenge of inadequate health outcomes by offering evidence-based answers to user inquiries, enhancing decision-making in wellness management.
Funding
Funding not disclosed
Founders
Product
Problem
Many individuals experience inadequate health outcomes due to the limitations of conventional medicine, including ineffectiveness, high costs, and inaccessibility. This has led to increased interest in complementary and alternative medicine (CAM), but healthcare providers often lack familiarity with the related scientific research, potential interactions, and side effects.
Solution
Unali provides a research-grade AI assistant that delivers evidence-based wellness knowledge by leveraging a proprietary dataset of 200 million scientific papers. The platform uses standardized measures of scientific quality derived from FDA guidelines to evaluate research and provide accurate answers to user inquiries. By combining scientific rigor with user preferences and specific offerings, Unali personalizes the question-answering experience. The AI assistant mitigates shortcomings through strict safeguards and a transparent architecture, allowing users to view original sources and contribute to overall accuracy.
Target Audience
Unali is designed for health and wellness professionals seeking a user-friendly search engine to monitor behaviors and collect feedback, as well as businesses looking to embed research-grade wellness knowledge into their platforms.
Features
- AI assistant powered by 200 million research papers curated for scientific rigor
- Applies standardized measures of scientific quality derived from FDA guidelines
- Fine-tunes AI models on specific tasks and constantly iterates to improve accuracy
- Rates scientific research and conversations to provide transparent, evidence-based answers
- Allows users to view original sources and contribute to the overall accuracy of the platform
- Employs a hybrid search approach, combining semantic information with keyword filtering
- Utilizes Chain of Thought Prompting to implement study design evaluation