{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_documind","slug":"documind","name":"Documind","type":"product","url":"https://www.documind.chat","page_url":"https://unfragile.ai/documind","categories":["documentation"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_documind__cap_0","uri":"capability://search.retrieval.cross.document.semantic.search.and.question.answering","name":"cross-document semantic search and question answering","description":"Enables users to pose natural language questions across multiple uploaded documents simultaneously, using vector embeddings and semantic similarity matching to retrieve relevant passages and synthesize answers. The system likely indexes document chunks into a vector database (e.g., Pinecone, Weaviate, or proprietary) and routes queries through an LLM with retrieved context to generate coherent cross-document responses without requiring manual document switching or keyword-based search.","intents":["I need to find all mentions of a specific topic across 10+ research papers without reading each one","I want to ask a question and get an answer that synthesizes information from multiple contracts or legal documents","I need to compare how different documents address the same subject matter"],"best_for":["researchers analyzing literature across multiple papers","legal teams reviewing contract portfolios","content teams synthesizing insights from competitive analysis documents"],"limitations":["Semantic search quality degrades with highly specialized jargon or domain-specific terminology not well-represented in training data","No explicit support for temporal reasoning — cannot reliably answer 'what changed between document versions'","Context window limits mean very long documents may be chunked, potentially losing cross-section relationships"],"requires":["Documents in supported formats (PDF, DOCX, TXT, likely others)","Active internet connection for cloud-based embedding and LLM inference","User account with sufficient storage quota for document uploads"],"input_types":["text","PDF","DOCX","natural language queries"],"output_types":["text","structured summaries","citation references to source documents"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_documind__cap_1","uri":"capability://text.generation.language.intelligent.multi.document.summarization.with.configurable.abstraction.levels","name":"intelligent multi-document summarization with configurable abstraction levels","description":"Generates summaries of single or multiple documents at varying levels of abstraction (e.g., executive summary, detailed outline, key points) using extractive and abstractive summarization techniques. The system likely uses prompt engineering or fine-tuned models to control summary length and focus, potentially with document-specific metadata (title, author, date) to contextualize summaries and avoid hallucination of non-existent details.","intents":["I need a one-page executive summary of a 50-page report for stakeholder review","I want to quickly understand the key findings across 5 research papers without reading them fully","I need to extract action items and deadlines from meeting notes or project documents"],"best_for":["busy executives and managers reviewing large document volumes","researchers conducting literature reviews","content teams creating briefing documents"],"limitations":["Abstractive summarization may introduce subtle inaccuracies or omit nuanced caveats from original documents","No explicit support for domain-specific summarization (e.g., legal summaries with liability emphasis vs. technical summaries with implementation focus)","Summary quality depends heavily on document structure — poorly formatted or scanned documents may produce incoherent summaries"],"requires":["Documents uploaded to Documind platform","Sufficient LLM API quota for summarization requests","Optional: user preferences for summary length and style"],"input_types":["text","PDF","DOCX","multiple documents"],"output_types":["text","structured summaries with sections","bullet-point lists"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_documind__cap_2","uri":"capability://automation.workflow.real.time.collaborative.document.annotation.and.markup","name":"real-time collaborative document annotation and markup","description":"Enables multiple users to simultaneously view, annotate, highlight, and comment on documents with live synchronization of changes across all connected clients. The system likely uses operational transformation (OT) or conflict-free replicated data types (CRDTs) to merge concurrent edits, with a WebSocket-based backend to broadcast annotation changes in real-time without requiring manual refresh or version control.","intents":["I need my team to review and annotate a contract simultaneously without creating conflicting versions","I want to highlight key passages and see my colleague's comments appear instantly as they annotate","I need to track who made which annotations and when for audit purposes"],"best_for":["distributed teams collaborating on document review","legal and compliance teams managing contract workflows","editorial teams reviewing content drafts"],"limitations":["Real-time sync may introduce latency on high-latency networks or with very large documents (1000+ pages)","No explicit version history or branching — concurrent edits are merged but not preserved as separate versions","Annotation data is likely stored in Documind's cloud, not exportable to standard formats like DOCX with tracked changes"],"requires":["Active Documind account for all collaborators","Stable internet connection for WebSocket communication","Document uploaded to Documind platform","Appropriate sharing permissions configured"],"input_types":["text","PDF","DOCX","user annotations and comments"],"output_types":["annotated documents","comment threads","audit logs with timestamps and user attribution"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_documind__cap_3","uri":"capability://data.processing.analysis.ai.powered.document.organization.and.tagging","name":"ai-powered document organization and tagging","description":"Automatically categorizes and tags uploaded documents using NLP-based document classification, extracting metadata like document type (contract, report, research paper), topic, date, and key entities. The system likely uses pre-trained classifiers or zero-shot classification models to assign tags without manual labeling, with optional user feedback loops to refine classifications over time.","intents":["I want to automatically organize hundreds of documents into folders by type and topic without manual sorting","I need to extract metadata like dates, parties, and document types from a large document collection for indexing","I want to find all documents related to a specific project or client automatically"],"best_for":["teams managing large unstructured document repositories","researchers organizing literature collections","compliance teams categorizing regulatory documents"],"limitations":["Classification accuracy depends on document clarity and structure — poorly formatted or handwritten documents may be misclassified","No support for custom classification schemas — users are limited to pre-defined categories","Entity extraction may miss domain-specific entities (e.g., proprietary product names, internal acronyms)"],"requires":["Documents uploaded to Documind","Sufficient processing quota for classification","Optional: user feedback to improve classification accuracy"],"input_types":["text","PDF","DOCX","multiple documents"],"output_types":["document tags","metadata (type, topic, date, entities)","organized document collections"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_documind__cap_4","uri":"capability://text.generation.language.document.aware.conversational.chat.with.context.retention","name":"document-aware conversational chat with context retention","description":"Provides a chat interface where users can have multi-turn conversations about uploaded documents, with the LLM maintaining context across turns and referencing specific document sections. The system likely implements a sliding context window that includes recent conversation history plus relevant document chunks retrieved via semantic search, enabling coherent follow-up questions without re-uploading context.","intents":["I want to ask follow-up questions about a document without re-stating the context each time","I need to have a back-and-forth conversation to clarify ambiguous sections of a contract or report","I want to explore different interpretations of a document through dialogue"],"best_for":["researchers exploring document content interactively","legal professionals clarifying contract language","analysts investigating data or findings in reports"],"limitations":["Context window limits mean very long conversations may lose earlier context or document references","No explicit memory across sessions — starting a new chat loses conversation history","LLM may hallucinate details or misinterpret ambiguous document passages in follow-up turns"],"requires":["Document uploaded to Documind","Active chat session with document context loaded","Sufficient LLM API quota for multi-turn conversations"],"input_types":["natural language queries","follow-up questions","clarification requests"],"output_types":["text responses","document citations","structured answers"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_documind__cap_5","uri":"capability://automation.workflow.batch.document.processing.and.export","name":"batch document processing and export","description":"Supports bulk operations on multiple documents simultaneously, such as batch summarization, tagging, or export to standard formats. The system likely queues batch jobs asynchronously and notifies users upon completion, with options to export results in formats like CSV, JSON, or DOCX for downstream processing or integration with other tools.","intents":["I need to summarize 100 documents at once and export the summaries as a CSV for analysis","I want to extract metadata from all documents in a folder and import it into my database","I need to generate a report that combines summaries and tags from multiple documents"],"best_for":["teams processing large document volumes","researchers conducting batch analysis","data teams integrating document insights into pipelines"],"limitations":["Batch processing may have rate limits or queue delays during peak usage","Export formats may not preserve all document formatting or annotations","No support for custom processing logic — limited to pre-defined batch operations"],"requires":["Multiple documents uploaded to Documind","Sufficient API quota for batch operations","Optional: export destination or integration endpoint"],"input_types":["multiple documents","batch operation parameters"],"output_types":["CSV","JSON","DOCX","structured data exports"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_documind__cap_6","uri":"capability://data.processing.analysis.document.comparison.and.diff.analysis","name":"document comparison and diff analysis","description":"Identifies and highlights differences between two or more document versions, showing added, removed, and modified text with side-by-side or unified diff views. The system likely uses sequence alignment algorithms (e.g., Myers' diff algorithm or similar) to compute minimal diffs and present changes in a human-readable format, with optional support for semantic comparison (e.g., detecting paraphrased sections).","intents":["I need to see what changed between two versions of a contract to identify new obligations or removed clauses","I want to compare how different authors addressed the same topic in research papers","I need to track edits across multiple document revisions for audit purposes"],"best_for":["legal teams reviewing contract revisions","compliance teams tracking regulatory document changes","researchers comparing document versions"],"limitations":["Diff algorithms may struggle with heavily reformatted documents or significant structural changes","Semantic comparison (paraphrase detection) is not explicitly mentioned and may not be supported","No support for three-way merges or conflict resolution for concurrent edits"],"requires":["Two or more document versions uploaded to Documind","Documents in comparable formats (e.g., both DOCX or both PDF)"],"input_types":["text","PDF","DOCX","multiple document versions"],"output_types":["diff visualization","side-by-side comparison","change summary"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_documind__cap_7","uri":"capability://data.processing.analysis.document.to.structured.data.extraction","name":"document-to-structured-data extraction","description":"Extracts structured information from unstructured documents (e.g., extracting contract terms, invoice line items, or research metadata) and outputs as JSON, CSV, or database-ready formats. The system likely uses prompt engineering with few-shot examples or fine-tuned extraction models to identify and parse key fields, with optional validation against user-defined schemas.","intents":["I need to extract all contract terms (parties, dates, payment terms) from 50 contracts and load them into a database","I want to pull invoice data (vendor, amount, date) from PDFs and import into my accounting system","I need to extract research metadata (authors, methodology, findings) from papers for a literature database"],"best_for":["finance teams processing invoices and contracts","legal teams extracting contract metadata","researchers building structured datasets from documents"],"limitations":["Extraction accuracy depends on document consistency and clarity — highly variable formats may produce inconsistent results","No support for custom extraction schemas — limited to pre-defined document types","Hallucination risk: LLM may invent missing fields rather than marking them as null"],"requires":["Documents in supported formats (PDF, DOCX, TXT)","Clear document structure or examples for few-shot learning","Optional: schema definition for validation"],"input_types":["text","PDF","DOCX","unstructured documents"],"output_types":["JSON","CSV","structured records","database-ready formats"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_documind__cap_8","uri":"capability://safety.moderation.document.access.control.and.permission.management","name":"document access control and permission management","description":"Manages granular access permissions for documents, allowing users to share documents with specific team members or groups with role-based access levels (e.g., viewer, commenter, editor). The system likely stores permissions in a database and enforces them at the API level, with audit logging to track who accessed or modified documents.","intents":["I need to share a sensitive contract with my legal team but prevent them from editing it","I want to give a client read-only access to a project document without exposing other projects","I need to track who accessed a confidential document and when for compliance purposes"],"best_for":["teams handling sensitive or confidential documents","organizations with compliance or audit requirements","distributed teams with varying access needs"],"limitations":["No explicit support for row-level or field-level access control — permissions are document-level only","No support for time-limited access or expiring share links","Audit logs may not be exportable or queryable for compliance reporting"],"requires":["Documind account for all users requiring access","Document uploaded to Documind","Appropriate admin permissions to manage sharing"],"input_types":["user identities","permission levels","sharing requests"],"output_types":["access control lists","audit logs","permission confirmations"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_documind__cap_9","uri":"capability://search.retrieval.document.search.with.natural.language.and.filters","name":"document search with natural language and filters","description":"Provides a search interface combining natural language semantic search with optional metadata filters (e.g., date range, document type, author). The system likely uses vector embeddings for semantic matching and applies filter predicates to narrow results, with ranking by relevance and recency. Results include snippets showing matching context.","intents":["I want to find all documents mentioning 'data privacy' across my entire document library","I need to search for contracts from Q3 2023 that mention 'liability' in a specific section","I want to find documents similar to a reference document without knowing exact keywords"],"best_for":["teams with large document libraries","researchers searching literature collections","compliance teams finding relevant regulatory documents"],"limitations":["Semantic search may return false positives for polysemous terms or domain-specific jargon","Filter options are likely pre-defined — no support for custom metadata filters","Search results may not be sortable by custom criteria (e.g., document importance or relevance score)"],"requires":["Documents indexed in Documind (automatic upon upload)","Active search session","Optional: filter parameters"],"input_types":["natural language queries","filter criteria","reference documents"],"output_types":["ranked search results","snippets with context","document metadata"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Documents in supported formats (PDF, DOCX, TXT, likely others)","Active internet connection for cloud-based embedding and LLM inference","User account with sufficient storage quota for document uploads","Documents uploaded to Documind platform","Sufficient LLM API quota for summarization requests","Optional: user preferences for summary length and style","Active Documind account for all collaborators","Stable internet connection for WebSocket communication","Document uploaded to Documind platform","Appropriate sharing permissions configured"],"failure_modes":["Semantic search quality degrades with highly specialized jargon or domain-specific terminology not well-represented in training data","No explicit support for temporal reasoning — cannot reliably answer 'what changed between document versions'","Context window limits mean very long documents may be chunked, potentially losing cross-section relationships","Abstractive summarization may introduce subtle inaccuracies or omit nuanced caveats from original documents","No explicit support for domain-specific summarization (e.g., legal summaries with liability emphasis vs. technical summaries with implementation focus)","Summary quality depends heavily on document structure — poorly formatted or scanned documents may produce incoherent summaries","Real-time sync may introduce latency on high-latency networks or with very large documents (1000+ pages)","No explicit version history or branching — concurrent edits are merged but not preserved as separate versions","Annotation data is likely stored in Documind's cloud, not exportable to standard formats like DOCX with tracked changes","Classification accuracy depends on document clarity and structure — poorly formatted or handwritten documents may be misclassified","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"ecosystem":0.2,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.283Z","last_scraped_at":"2026-04-05T13:23:42.552Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=documind","compare_url":"https://unfragile.ai/compare?artifact=documind"}},"signature":"PkPCcLpnMa1TN9qvdhiWBxNvOagnwn7Ii6TUE9Cf+H29YUW9mMkCOCVSdUdH32aND/wLDybjOn/zPvE9wqXrCQ==","signedAt":"2026-06-21T00:12:51.219Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/documind","artifact":"https://unfragile.ai/documind","verify":"https://unfragile.ai/api/v1/verify?slug=documind","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}