AI-powered document analysis platform that allows users to chat with their documents and knowledge bases to get fully sourced, reliable answers.
Petal is a freemium document analysis platform that lets users chat with their documents and knowledge bases to get fully sourced, citation-backed answers, with pricing starting at free for light use and paid plans from $19.99/month for the Pro tier. Used by thousands of researchers and professionals across academia, legal, and corporate sectors, Petal focuses on multi-document querying — allowing users to upload entire collections of PDFs and ask natural language questions that are answered by synthesizing information across all uploaded sources.
Unlike single-document chat tools such as ChatPDF, Petal is built around the concept of knowledge bases: organized collections of related documents that can be queried simultaneously. Every AI-generated answer includes inline citations linking directly to specific pages and passages in the original documents, enabling one-click verification of any claim. This provenance-first approach makes Petal particularly well-suited for research workflows where traceability and accuracy are non-negotiable.
The platform supports over 20 languages for document processing and querying, and includes built-in tools for PDF reading, highlighting, and annotation. Automatic metadata extraction populates titles, authors, dates, and other properties from uploaded files without manual data entry, while file deduplication keeps document libraries clean. A browser extension enables rapid capture of documents from the web directly into knowledge bases.
Petal's free tier allows up to 5 documents and 50 AI queries per month, which is useful for evaluating the platform but insufficient for sustained research. The Pro plan at $19.99/month supports up to 500 documents per knowledge base, unlimited queries, and team collaboration for up to 10 members. Enterprise plans offer custom pricing with unlimited documents, team members, SSO, and dedicated support. According to the platform, the average Pro user uploads approximately 120 documents and runs over 300 queries per month, reflecting active adoption among power users. The platform processes documents using OCR-based parsing, enabling support for scanned PDFs alongside native digital documents, and handles complex formatting including tables, figures, and mathematical notation in scientific papers.
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Petal's AI is trained exclusively on the user's uploaded documents rather than general web data, producing answers grounded in trusted sources. Every response includes inline citations linking to exact pages and paragraphs, enabling one-click verification. This provenance-first approach ensures that users can trace every claim back to its origin, which is critical for academic, legal, and compliance-driven workflows where unsourced assertions are unacceptable.
Users can query across entire knowledge bases containing hundreds of documents simultaneously, rather than being limited to single-file interactions. The platform synthesizes information from multiple sources into cohesive answers, identifying connections and contradictions across papers. This capability is particularly valuable for literature reviews, due diligence processes, and competitive analysis where insights emerge from patterns across many documents.
The cloud-based document drive automatically extracts metadata from uploaded files, populating titles, authors, dates, and other properties without manual entry. Built-in file deduplication prevents redundant uploads, while dedicated technical and scientific document support handles complex formatting including tables, figures, and mathematical notation. This automation significantly reduces the overhead of building and maintaining large research libraries.
Teams can highlight key passages, leave comments, and share annotated documents via generated shareable links — eliminating email-based document exchange. The AI can identify key points and explain complex ideas within the annotation workflow. Pro plans support teams of up to 10 members with shared knowledge bases, while Enterprise plans offer unlimited seats and role-based access controls for larger organizations.
A browser extension allows users to capture and import documents directly from the web into their Petal knowledge bases. Combined with automatic metadata population, this streamlines the research ingestion pipeline so users can go from discovering a paper to querying it within seconds, removing friction from the document collection phase of any research project.
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$19.99/month
Custom pricing
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In early 2026, Petal has continued to refine its AI query engine with improved accuracy for cross-document synthesis and faster document processing times. The platform has expanded its language support and improved OCR handling for complex scientific documents with tables and equations. Team collaboration features have also seen updates, with enhanced annotation sharing and improved knowledge base management tools.
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