Apodex

What is Apodex

Apodex is an AI research tool designed to provide verified, citation-backed answers to complex questions. It positions itself as a "self-evolving heavy-duty solver" that performs deep research to find answers that may not already exist, checking every step to ensure trustworthiness. The platform emphasizes step-level reasoning traces and citations in every report, distinguishing it from simple chat-based AI assistants.

How to use Apodex

To use Apodex, you can pose a question in the provided text area on the main page. You can also upload files (images, PDFs, documents, spreadsheets, presentations) to support your inquiry. After submitting a question, Apodex reasons through it step by step, verifying each conclusion before moving to the next, and produces a verified brief. Users can sign in to save their inquiries and continue them later. The interface also offers curated example questions across various domains, which can be selected to see how the tool works.

Features of Apodex

• Step-level reasoning trace: Apodex shows the step-by-step reasoning process it follows to arrive at an answer.
• Citations in every report: Each report includes citations to sources, ensuring the information is verifiable.
• Branch off any report: Users can branch off from any report to explore related questions or deeper aspects.
• Full-text search across threads: The platform allows searching across all saved inquiries and threads.
• File upload support: Users can upload images, PDFs, documents, spreadsheets, and presentations to provide context.
• Voice input: A microphone button is available for voice input.
• Curated example questions: The homepage displays five curated questions in areas like macroeconomics, medicine, ML research, and AI policy.
• API Platform: Apodex offers an API platform for developers.

Use Cases of Apodex

Apodex can be used for a variety of complex research tasks, including:

• Macroeconomics: Analyzing economic indicators and predicting central bank actions (e.g., "Will the Fed cut rates before Q3 2026?")
• Medicine: Evaluating clinical evidence on drug effects and combinations (e.g., "What's the current evidence on GLP-1's long-term cardiovascular effects?")
• ML Research: Comparing technical approaches (e.g., "Transformer vs. SSM: what does the evidence actually show?")
• AI Policy: Tracking regulatory changes (e.g., "Key EU AI liability regulatory shifts in the past 12 months")