Research & learning assistant for instant document Q&A and summaries
Humata is an AI document assistant that lets users ask questions and get cited answers from uploaded files; it's best for researchers, students, and knowledge workers who need fast extractive Q&A across PDFs and docs, and its pricing is accessible with a limited free tier and paid plans starting at an affordable monthly rate (approx. $12/month).
Humata is an AI-powered research & learning assistant that answers questions, summarizes, and extracts insights directly from your documents. It primarily provides document Q&A and multi-file summarization with source citations, enabling fast comprehension of PDFs, DOCX, and web URLs. Humata's key differentiator is its document-aware chat that references page numbers and highlights source text, aimed at students, researchers, legal assistants, and analysts. The platform offers a usable free tier with limited uploads and paid plans that unlock higher quotas and model options, making it accessible for individual learners and small teams.
Humata launched as a document-centric AI assistant positioned for research & learning workflows, offering on-top conversational access to uploaded files. The product's core value proposition is reducing time-to-insight: instead of manually skimming long PDFs or combining search results, users ask plain-language questions and receive concise answers with inline citations back to the original document. Humata was developed to sit between single-file Q&A tools and full knowledge-base platforms, emphasizing easy document ingestion and immediate, source-backed responses for knowledge work.
At the feature level Humata supports multi-format ingestion (PDF, DOCX, TXT, and URLs) and maintains per-file context so you can ask cross-document questions. The chat interface returns extractive answers with page references and quoted snippets when available, and it can generate structured summaries and bullet-point notes from one or many documents. Humata also offers document search and highlights so you can jump to the original passage, and it lets users upload multiple files into a single workspace for combined Q&A. For advanced users, Humata supports connecting your OpenAI API key to run queries on OpenAI models (including GPT-4 where allowed), and it retains chat history for ongoing research sessions.
Pricing is a mix of free and paid tiers. The free tier allows limited uploads and a restricted number of queries per month (approximate limits: small number of documents and daily question cap). The Pro plan (approximately $12/month billed monthly) increases upload quotas, gives priority processing, and unlocks OpenAI-key integration and higher response limits. A Team or Business tier (approx. $25–$30/user/month) adds shared workspaces, admin controls, and bulk onboarding features suitable for small teams; enterprise/custom pricing is available for larger deployments with SSO and contractual support. Note: these price points and exact quotas are approximate and can change; always check Humata.ai for current billing details.
Humata is used by a range of people who need rapid document comprehension: a PhD student using it to cut reading time and extract citations from 50+ journal PDFs, and a legal assistant using it to summarize clauses and locate precedent passages across case files. Market analysts use Humata to synthesize earnings reports, while customer success teams use it to scan product manuals and support docs for quick answers. Compared with ChatPDF-style single-document tools, Humata leans toward multi-file workspace workflows and OpenAI-key integration, making it a closer competitor to multi-document research assistants than to single-file parsers.
Three capabilities that set Humata apart from its nearest competitors.
Current tiers and what you get at each price point. Verified against the vendor's pricing page.
| Plan | Price | What you get | Best for |
|---|---|---|---|
| Free | Free | Limited uploads, restricted monthly query quota, basic features only | Casual users and students testing document Q&A |
| Pro | $12/month (approx) | Higher upload quota, priority processing, OpenAI key support | Individual researchers needing regular document analysis |
| Team | $30/user/month (approx) | Shared workspaces, admin controls, larger monthly quotas | Small teams needing collaborative research workflows |
| Enterprise | Custom | SSO, contractual SLAs, large-document quotas, custom integrations | Organizations requiring security and scale |
Copy these into Humata as-is. Each targets a different high-value workflow.
Role: You are an expert research assistant. Task: Read the uploaded document and produce a concise executive summary. Constraints: 1) Use only information present in the document; do not hallucinate. 2) Provide exactly five bullets: one-sentence high-level takeaway, two bullets with top two findings (one sentence each, include page numbers), one bullet with the primary limitation (one sentence, page), and one bullet with the one-sentence recommended action. Output format: numbered list of five bullets, each ending with a parenthetical page citation like (p.12). Example: 1) Main takeaway — The study shows X (p.3).
Role: You are a study-oriented content extractor. Task: Convert the uploaded lecture PDF into 20 Anki-style Q/A flashcards. Constraints: 1) Use only document content. 2) Each flashcard must be one question (concise) and one answer (1–2 sentences) with a parenthetical source like (p.5). 3) Avoid trivial factuals (dates unless central). Output format: JSON array of objects [{"q":"...","a":"...","source":"p.X"}]. Examples: {"q":"What is the definition of X?","a":"X is defined as...","source":"p.4"}.
Role: You are a legal assistant summarizer. Task: Parse the uploaded contract(s) and extract clauses matching these types: "Termination", "Indemnity", "IP/Ownership", "Confidentiality", "Liability". Constraints: 1) For each clause found, include type, exact quoted clause (<= 300 chars), starting page, clause number or header, a short 10-word risk assessment (Low/Medium/High), and a 15-word recommended next step. 2) Use only document text; add page citations. Output format: CSV with columns: ClauseType, Quote, StartPage, ClauseHeader, Risk, Recommendation. Example row: "Termination","The agreement may be terminated...","p.42","12. Termination","High","Negotiate cap on termination fees."
Role: You are a market analyst summarizer. Task: Read the uploaded earnings reports and produce a comparative table of key metrics. Constraints: 1) Extract for each company: Revenue, Net Income, EPS, Operating Margin, Cash Flow, and YoY change where available; include the page number where each metric is found. 2) Present numerical values standardized to the same currency and units; flag any conversions performed. Output format: CSV with columns: Company, Metric, Value, Unit, YoY%, PageCitation, Note. Example row: "Acme Co", "Revenue", "$4,200,000", "USD, thousands", "+6%", "p.8", "Converted from millions."
Role: You are a PhD research assistant with domain expertise. Task: Read the supplied set of papers and produce (A) a literature matrix and (B) a research-gap and next-experiments section. Constraints: 1) Literature matrix must include: Paper ID, Full citation, Research question, Methods, Sample size, Key findings (one sentence with page citation), Limitations (one sentence with page), and Relevance score 1–5. 2) Then list top 3 research gaps synthesizing across papers (2–3 sentences each) and for each propose one follow-up experiment: hypothesis, brief method (2–3 sentences), and expected outcome. Output format: JSON with keys "matrix" (array) and "gaps" (array). Example matrix item: {"id":"P1","citation":"...","question":"...","methods":"RCT","n":"120","findings":"... (p.5)","limitations":"... (p.12)","score":4}.
Role: You are a compliance officer and evidence mapper. Task: Using the uploaded regulations and internal policy documents, create a risk register that maps each regulatory requirement to evidence in the documents. Constraints: 1) For each regulation clause, include: RegID, Short description, Verbatim evidence quote (<=200 chars) from internal docs, SourceDoc and Page, ComplianceStatus (Compliant/Partial/Non-compliant), RiskRating (Low/Medium/High), Recommended remediation (one sentence), and Suggested owner and due-date (YYYY-MM-DD). 2) Use only provided documents; do not infer compliance beyond the text. Output format: JSON array of objects. Example: {"RegID":"GDPR-5","desc":"Data retention limit","quote":"We retain records for 7 years...","source":"EmployeePolicy.pdf p.14","status":"Partial","risk":"Medium","remediation":"Implement 90-day deletion policy","owner":"DPO","due":"2026-09-30"}.
Choose Humata over ChatPDF if you need multi-file workspaces and OpenAI-key model control for collaborative research.
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