Explore a shared document library with source-linked answers

What is Ai-Powered Document Analysis Platform?

Petal is a document analysis platform for working with a collection of material you already trust. It organizes documents in a shared library and adds AI questions, summaries, and drafting support around them. This is useful when information is distributed across technical papers or internal files and the team needs a way to investigate a question without losing track of which document supports the answer.

Organize the material before asking across it

Petal describes a cloud document library with metadata extraction and duplicate-file handling. Users can highlight passages, leave comments, share documents, and ask questions grounded in the collection. Its Notebook supports writing tasks, while a multi-document table provides a way to compare material using natural-language criteria. These features connect individual reading with collaborative review of a larger set of sources.

Make comparisons explicit

For a technical review, define the question and choose documents that actually address it. Use the same comparison fields across the collection, such as method, assumptions, or reported limitations. Open the cited passages when an answer appears decisive, and record disagreements rather than smoothing them into a single conclusion. Shared annotations are most useful when colleagues can see both the interpretation and the evidence behind it.

Features to explore

• Document questions and answers
• Central document library
• Annotations and comments
• Multi-document comparisons
• Notebook drafting

Who is Ai-Powered Document Analysis Platform for?

Research groups and teams maintaining a collection of technical or internal documents. It is useful when answers need to remain traceable and colleagues want to compare evidence while sharing the same underlying source library.

Before you get started

Verify source passages and document versions before relying on an answer. Review library access and sharing links, particularly for confidential material, and avoid assuming a generated comparison includes every relevant qualification.