Deep Research for Obsidian integrates with OpenAI to power an intelligent research assistant inside Obsidian. It can query academic databases and the web, ingest PDFs and reference metadata, produce concise summaries, highlight missing links in your knowledge graph, and propose an exploration path to further your study. All outputs are stored as markdown notes with citations, allowing seamless integration with your existing note-taking workflow.

The Science App allows users to analyze any claim with both supporting and opposing evidence derived from peer-reviewed scientific research. By using AI to search scientific papers, it links users directly to the sources, providing a balanced analysis of evidence strength and scientific consensus. The platform is designed to aid researchers in streamlining their literature review process while also offering the general public access to evidence-based information in an approachable format.

Deep Research Agent leverages OpenAI's GPT models to perform advanced document retrieval and analysis. Users configure data sources (e.g., PubMed, arXiv), define queries, and receive digestible summaries highlighting methods, results, and key arguments. It supports multi-document comparison, citation extraction, and interactive Q&A sessions. Modular architecture allows integration of custom connectors, NLP pipelines, and export formats like markdown or JSON. With built-in scheduling, it can periodically update literature reviews, detect new research trends, and generate reports. Ideal for research teams, academics, and industry analysts seeking to reduce manual reading time and improve insight discovery in vast scientific corpora.