Petal

Petal is a freemium AI document analysis and research workspace that combines document chat, citation tools, reference management, cloud storage, and source-grounded answers, with a Free plan at $0/month and paid individual tiers that publicly list monthly prices, storage, AI-credit limits, seats, guests, and feature differences.

According to Petal's pricing page, the individual plan lineup includes Free at $0/month, Plus from $2.55/month with annual and education discounts, Advanced from $8.49/month with annual and education discounts, and Premium at $25.49/month. The Free plan includes 1 GB cloud storage, 1 seat, 3 guests, 400 non-replenishing AI credits, 2 collections, 3 annotations per document, single-doc chat, unlimited citation lists, and 7-day trials for multi-doc chat, AI Create, and AI Table. Plus increases storage to 2 GB, adds monthly 400 AI credits, enables export, removes collection and annotation limits, and includes AI Table, but does not include multi-doc chat or AI Create. Advanced raises storage to 10 GB and 1,200 monthly credits, adds priority support, multi-doc chat, and AI Create. Premium raises storage to 25 GB and 2,000 monthly credits, with priority support and the full listed feature set.

According to the provided website content, Petal lets users link the AI to their own knowledge bases so it can produce fully sourced and reliable answers. The product is positioned for people and organizations that rely heavily on documents, including academia, corporate R&D teams, and industry experts. Its core promise is to reduce the time spent searching, scanning, and manually extracting information from documents by allowing users to ask questions directly against their own uploaded or connected sources.

The platform emphasizes source-grounded answers rather than general-purpose chatbot responses. Petal says users can train AI on their own documents to support their work, and it highlights that answers are generated from sources the user trusts. This makes the tool especially relevant for research workflows where citations, traceability, and document context matter. The website describes Petal AI as a context-aware generative AI system that can provide accurate and trustworthy answers instantly by using the user’s own sources. For users who need to move through dense academic papers, technical documents, research reports, or internal knowledge collections, this source-aware design is the central value proposition.

Petal also functions as a centralized home for digital documents. The website describes a cloud drive that provides a single location for knowledge and helps ensure documents are synchronized and secure. That makes Petal more than a one-off PDF chat tool: it is presented as a broader document workspace where users can maintain a single source of truth, organize their materials, and query them through AI. This can be useful for researchers, faculty members, corporate research teams, and domain experts who need to preserve institutional or project knowledge across many documents.

The company highlights credibility through adoption signals, stating that Petal is trusted by more than 20,000 researchers, faculty, and industry experts. The visible content also identifies academia, corporate R&D, and industry experts as target audiences. Based on the provided content, the clearest verifiable facts are that Petal supports document-based AI chat, uses user-owned knowledge bases, emphasizes fully sourced answers, offers a cloud drive for centralized document storage, presents synchronization and security as document-management benefits, and includes a Get Started Free call to action.