Humata is an AI document question-and-answer tool that allows users to upload PDF files β research papers, legal documents, reports, textbooks β and ask natural language questions about the content. The AI reads and understands the documents and answers questions with citations pointing to the specific sections that contain the relevant information.
The tool is used across research, legal, finance, and education for extracting specific information from long documents without reading them in full. Users can compare multiple documents, generate summaries, extract key terms, and get answers to highly specific questions that would require extensive manual search in traditional document review. The vendor states its free tier is backed by OpenAI's model.
Key capabilities include PDF upload and natural language Q&A, citation-backed answers linking to source sections, multi-document comparison, AI summarization, key term extraction, and document search.
Pricing: the Free plan, at $0 per month for one user, includes up to 60 pages of document processing per month. Expert, at $9.99 per month for up to 3 users, includes 500 pages per month with a $0.02 per-page charge for additional pages. Team, at $49 per user per month for up to 10 users, includes 5,000 pages per month with a $0.01 per-page charge for additional pages. Enterprise offers custom per-user pricing with unlimited users and a custom page allowance.
Limitations: Humata is specialized for document Q&A β it is not a general research tool and doesn't search external databases. Quality of answers depends on the quality and structure of the uploaded PDF. Complex tables, charts, and equations in PDFs may not be extracted accurately. The free tier's 60-page limit is quickly exhausted with even a single research paper upload, and higher tiers rely on page-based overage charges rather than flat unlimited use.
Best suited for law students, researchers, and professionals who regularly work with long PDFs and want to quickly find specific information without manual reading β particularly for due diligence, literature review, and document-heavy workflows.
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