{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"v7","slug":"v7","name":"V7","type":"dataset","url":"https://www.v7labs.com","page_url":"https://unfragile.ai/v7","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"v7__cap_0","uri":"capability://planning.reasoning.domain.specialized.ai.agent.orchestration.for.private.markets.workflows","name":"domain-specialized ai agent orchestration for private markets workflows","description":"V7 Go deploys pre-built, domain-specific AI agents (Financial Agent, Legal Agent, Insurance Agent) that execute end-to-end workflows by chaining multiple LLM calls, document extraction, and analysis steps. Agents are instantiated within V7's infrastructure with configurable triggers (event-based activation via Zapier, API calls, or scheduled execution) and output routing to CRM systems, OneDrive, or data rooms. The platform abstracts multi-step reasoning chains behind a workflow UI rather than exposing raw API endpoints, enabling non-technical users to execute complex document analysis pipelines without prompt engineering.","intents":["I need to automate due diligence document review across multiple deal sources without building custom extraction logic","I want to trigger document analysis workflows when new files arrive in a data room or CRM","I need consistent financial/legal document parsing across my organization without training custom models"],"best_for":["private equity and venture capital teams automating deal sourcing and due diligence","legal departments processing contracts and regulatory documents at scale","insurance underwriting teams analyzing risk documents"],"limitations":["Agents are pre-built and domain-specific — no custom agent creation or fine-tuning documented; users cannot modify agent reasoning logic","Workflow definitions are stored in V7 platform with unknown export format — high vendor lock-in if migration is needed","No real-time API inference mode — workflows are asynchronous, not suitable for sub-second latency requirements","Agent behavior and model versions are opaque — no control over which LLM backbone or version is used","No multi-tenancy documentation — unclear if agents can be isolated per customer or shared across organizations"],"requires":["V7 Go platform account with appropriate license tier","Integration credentials for data sources (PitchBook, Dealroom, OneDrive, data room APIs)","Zapier account or direct API access for trigger configuration (if using event-based activation)","Destination system credentials (CRM, OneDrive, data room) for output routing"],"input_types":["documents (PDF, Word, Excel)","structured data (from PitchBook, Dealroom APIs)","unstructured text (contract excerpts, deal memos)","event triggers (file uploads, API calls, scheduled execution)"],"output_types":["structured extraction (JSON with parsed financial metrics, legal clauses, risk flags)","summary reports (markdown or formatted text)","CRM records (deal updates, contact enrichment)","file outputs (OneDrive, data room storage)"],"categories":["planning-reasoning","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"v7__cap_1","uri":"capability://tool.use.integration.multi.source.document.ingestion.with.trigger.based.activation","name":"multi-source document ingestion with trigger-based activation","description":"V7 Go integrates with external data sources (PitchBook, Dealroom, data rooms, OneDrive) and event systems (Zapier) to automatically detect new documents and trigger agent workflows. Documents are ingested via API connectors or file upload, with metadata extraction (source, timestamp, document type) used to route to appropriate agents. Trigger logic supports event-based (file arrival), scheduled (daily/weekly), and manual (user-initiated) activation modes, enabling hands-off automation of document processing pipelines.","intents":["I want documents from PitchBook and Dealroom to automatically flow into my due diligence workflow without manual uploads","I need to trigger analysis when new files arrive in a data room or OneDrive folder","I want to schedule daily batch processing of deal documents from multiple sources"],"best_for":["deal teams managing documents across multiple platforms (PitchBook, Dealroom, data rooms)","organizations with high document volume requiring automated ingestion","teams using Zapier for workflow automation and seeking document processing integration"],"limitations":["Supported data sources are limited to documented integrations (PitchBook, Dealroom, OneDrive, data rooms) — custom source connectors not documented","Trigger latency not specified — no SLA for time between document arrival and agent activation","No bulk historical ingestion documented — appears to support forward-looking triggers only, not backfill of existing document libraries","Metadata extraction is automatic but customization options unknown — cannot specify custom extraction rules per source"],"requires":["V7 Go account with data source integrations enabled","API credentials or OAuth tokens for each data source (PitchBook, Dealroom, OneDrive, etc.)","Zapier account if using event-based triggers from third-party apps","Appropriate file permissions in source systems (read access to documents)"],"input_types":["documents (PDF, Word, Excel, PowerPoint)","structured metadata (from PitchBook/Dealroom APIs)","event payloads (Zapier webhooks, file upload notifications)"],"output_types":["document queue (staged for agent processing)","metadata records (source, timestamp, document type)","trigger logs (activation history, timing)"],"categories":["tool-use-integration","automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"v7__cap_10","uri":"capability://automation.workflow.workflow.execution.monitoring.and.error.handling.with.status.tracking","name":"workflow execution monitoring and error handling with status tracking","description":"V7 Go provides real-time monitoring of workflow executions with status tracking (pending, running, completed, failed), execution duration metrics, and error logging. Failed executions are logged with error details and can be retried manually or automatically. Status updates are pushed to users via email notifications or webhook callbacks. Execution history is retained for audit purposes and performance analysis.","intents":["I need to monitor the status of my workflows in real-time and get notified when they complete or fail","I want to understand why a workflow failed and retry it without re-triggering from the source","I need to track execution duration and identify slow workflows for optimization"],"best_for":["teams running mission-critical workflows and needing visibility into execution status","organizations requiring audit trails and execution history for compliance","teams optimizing workflow performance and needing execution metrics"],"limitations":["Error handling and retry logic not documented — unclear if failed executions are automatically retried or require manual intervention","Notification options are limited to email and webhooks — no mention of Slack, Teams, or other messaging integrations","Execution history retention period not specified — unclear how long execution logs are retained","Performance metrics are limited to execution duration — no mention of token consumption per execution or cost tracking","No mention of execution debugging tools — unclear if users can inspect intermediate results or step-by-step execution logs"],"requires":["V7 Go account with workflow monitoring enabled","Email address or webhook URL for notifications","Access to execution history dashboard"],"input_types":["workflow executions (automatically tracked)","notification preferences (email, webhook)"],"output_types":["execution status (pending, running, completed, failed)","execution duration (total time, per-step timing)","error logs (error message, stack trace, failed step)","status notifications (email, webhook)","execution history (list of past executions with status and duration)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"v7__cap_11","uri":"capability://automation.workflow.usage.limit.enforcement.and.token.quota.management","name":"usage limit enforcement and token quota management","description":"Enforces per-account token usage limits and quota management to prevent unexpected cost overruns. The platform tracks token consumption in real-time, alerts users when approaching limits, and stops processing when limits are exceeded. Administrators can set usage limits per account, team, or project; limits are enforced at the agent execution level. The system provides usage dashboards and reports showing token consumption by agent, document type, and time period.","intents":["prevent unexpected cost overruns from runaway document processing workflows","allocate token budgets to teams or projects for cost control","monitor token consumption in real-time and adjust processing accordingly","generate usage reports for cost allocation and billing"],"best_for":["organizations with fixed budgets for document processing","finance teams needing cost control and allocation","teams with variable processing volume seeking predictable costs"],"limitations":["usage limits are enforced at account level; no granular limits per agent, team, or project","no warning system or graceful degradation; processing stops immediately when limit is reached without partial results","limit enforcement is hard stop; no option to exceed limits with approval or additional charges","usage reports are limited to token consumption; no cost breakdown by LLM provider or operation type","no cost optimization recommendations or suggestions for reducing token consumption"],"requires":["V7 Go account with quota management enabled","configured usage limits per account"],"input_types":["usage limit configuration (tokens per period)","agent execution requests"],"output_types":["usage reports (tokens consumed, remaining quota)","limit enforcement logs","cost allocation reports"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"v7__cap_12","uri":"capability://code.generation.editing.python.code.execution.within.agent.workflows","name":"python code execution within agent workflows","description":"Enables agents to execute Python code snippets for custom data transformations, calculations, or logic within extraction and processing workflows. Code execution is sandboxed and scoped; users can define Python functions that operate on extracted data and return results. The system manages code execution, error handling, and timeout enforcement. Available libraries are limited to a curated set (NumPy, Pandas, etc.); external API calls and file system access are restricted.","intents":["perform custom calculations or data transformations on extracted data","implement business logic that cannot be expressed through property types or skills","validate or clean extracted data using Python code","integrate custom Python libraries into agent workflows"],"best_for":["technical users needing custom logic in extraction workflows","teams with complex calculations or transformations","organizations with existing Python code to integrate"],"limitations":["code execution is sandboxed; no access to arbitrary libraries or external APIs","available libraries are limited to curated set (NumPy, Pandas, etc.); no pip install or custom dependencies","no file system access; code cannot read or write files outside sandbox","execution timeout is enforced; long-running code may be terminated","error handling is limited; code errors may cause agent execution to fail without graceful fallback","no code versioning or testing framework; code changes apply immediately to running agents"],"requires":["V7 Go account with Python code execution enabled","Python programming knowledge","familiarity with available libraries (NumPy, Pandas, etc.)"],"input_types":["Python code snippets","extracted data from agents","function parameters and inputs"],"output_types":["transformed data from Python functions","calculation results","validation or cleaning outputs","execution logs and error traces"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"v7__cap_13","uri":"capability://data.processing.analysis.document.quality.assessment.and.processing.readiness","name":"document quality assessment and processing readiness","description":"Automatically assesses document quality and processing readiness before extraction, identifying issues like poor image quality, missing pages, or unsupported formats that may impact extraction accuracy. The system provides quality scores and recommendations for document preprocessing (rotation, enhancement, OCR). Quality assessment is performed before agent execution, enabling users to filter or preprocess documents before processing.","intents":["identify documents that may fail extraction due to quality issues","assess OCR readiness for scanned documents","recommend preprocessing steps to improve extraction accuracy","filter low-quality documents from batch processing workflows"],"best_for":["teams processing large batches of documents with variable quality","organizations with scanned or legacy documents requiring OCR","users wanting to optimize extraction accuracy by filtering low-quality documents"],"limitations":["quality assessment criteria are not documented; unclear what factors determine quality scores","preprocessing recommendations are limited to rotation and enhancement; no automatic preprocessing","quality scores are not standardized; unclear what thresholds indicate acceptable quality","no integration with external document preprocessing services; users must preprocess documents externally","quality assessment adds latency to document processing workflow"],"requires":["V7 Go account with quality assessment enabled","documents in supported formats"],"input_types":["documents (PDF, images, spreadsheets)"],"output_types":["quality scores and assessment results","preprocessing recommendations","document filtering and readiness reports"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"v7__cap_2","uri":"capability://tool.use.integration.multi.destination.workflow.output.routing.with.format.transformation","name":"multi-destination workflow output routing with format transformation","description":"V7 Go routes agent analysis results to multiple destination systems (CRM, OneDrive, data rooms) with automatic format transformation. Extracted data is mapped to CRM fields (deal records, contact enrichment), documents are stored in OneDrive with metadata tags, and summaries are pushed to data rooms for stakeholder review. Routing rules are configured per workflow, enabling a single agent execution to populate multiple downstream systems without manual export/import steps.","intents":["I want agent analysis results automatically written to my CRM as deal records and contact enrichment","I need extracted documents and summaries stored in OneDrive with proper folder structure and metadata","I want to push analysis summaries to data rooms for investor/stakeholder review without manual copying"],"best_for":["deal teams using CRM systems (Salesforce, HubSpot, Pipedrive) for pipeline management","organizations with OneDrive/SharePoint as document repository","teams sharing analysis results with external stakeholders via data rooms"],"limitations":["Supported destination systems are limited to documented integrations (CRM, OneDrive, data rooms) — custom destination connectors not documented","CRM field mapping is pre-configured per agent type — custom field mapping rules not documented as user-configurable","No conditional routing documented — cannot route different analysis results to different destinations based on content (e.g., high-risk flags to compliance team)","Output format transformation is automatic but customization options unknown — cannot specify custom JSON/CSV schemas for CRM exports","No retry logic or error handling documented — unclear behavior if destination system is unavailable"],"requires":["V7 Go account with destination integrations enabled","API credentials or OAuth tokens for each destination (CRM, OneDrive, data room)","Appropriate write permissions in destination systems","Pre-configured field mappings between agent output schema and destination system schema"],"input_types":["agent analysis results (structured JSON with extracted fields, summaries, risk flags)","document artifacts (PDFs, processed files)","metadata (source, timestamp, confidence scores)"],"output_types":["CRM records (deal updates, contact enrichment, activity logs)","OneDrive files (documents, summaries with metadata tags)","data room uploads (analysis reports, supporting documents)","formatted exports (JSON, CSV for downstream systems)"],"categories":["tool-use-integration","automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"v7__cap_3","uri":"capability://data.processing.analysis.volume.based.usage.tracking.and.cost.calculation.with.token.reporting","name":"volume-based usage tracking and cost calculation with token reporting","description":"V7 Go provides token-level usage reporting and cost calculation, tracking LLM tokens consumed per workflow execution, document processed, and agent invocation. Token Reports dashboard displays usage trends, per-user consumption, and cost breakdowns. Pricing is volume-based (pay-per-document or pay-per-token processed) with custom pricing tiers per customer. Usage limits can be configured per user or organization to enforce cost controls and prevent runaway spending.","intents":["I need to track how many tokens my team is consuming across all workflows to forecast costs","I want to set usage limits per user or department to control spending","I need to understand which workflows or agents are most expensive to optimize ROI"],"best_for":["finance teams managing AI platform costs and budgets","organizations with multiple teams/departments sharing V7 Go and needing cost allocation","enterprises negotiating custom pricing and requiring detailed usage metrics"],"limitations":["Pricing model is custom and non-transparent — no public per-token or per-document rates available; requires custom quote","Token counting methodology is undocumented — unclear if tokens are counted at input, output, or both stages, and whether system prompts are included","Usage limits are enforced at organization/user level but granular workflow-level limits not documented","No cost optimization recommendations documented — reports show usage but not actionable insights for reducing spend","Historical usage data retention period not specified — unclear how long reports are available for audit purposes"],"requires":["V7 Go account with Token Reports feature enabled","Admin access to view organization-wide usage and cost data","Custom pricing agreement with V7 specifying per-token or per-document rates"],"input_types":["workflow executions (tracked automatically)","document processing events (tracked automatically)","agent invocations (tracked automatically)"],"output_types":["token usage reports (CSV, dashboard visualization)","cost breakdowns (per-workflow, per-user, per-agent)","usage trend charts (daily/weekly/monthly)","usage limit alerts (when approaching configured thresholds)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"v7__cap_4","uri":"capability://safety.moderation.role.based.access.control.with.per.user.license.management","name":"role-based access control with per-user license management","description":"V7 Go implements role-based access control (RBAC) with customizable roles and per-user license management. Roles define permissions for workflow creation, agent execution, data access, and reporting. License tiers control feature access (basic agents vs. premium agents, read-only vs. edit permissions). User provisioning is managed through the platform's Account Management interface, with support for team hierarchies and department-level access controls.","intents":["I need to restrict certain team members to read-only access while allowing analysts to create and modify workflows","I want to control which agents each user can access based on their role (e.g., legal team only sees Legal Agent)","I need to manage per-user licensing costs by assigning different license tiers to different team members"],"best_for":["enterprises with multiple teams (deal team, legal, compliance) requiring role-based access","organizations managing per-user licensing costs and needing granular permission controls","teams with compliance requirements for audit trails and access control"],"limitations":["RBAC implementation details are undocumented — unclear if roles are pre-defined or fully customizable","No mention of SSO/SAML integration — unclear if enterprise identity providers (Okta, Azure AD) are supported","Audit logging for access control not documented — no mention of who accessed what data or when","Team hierarchy and department-level controls mentioned but not detailed — unclear how nested teams or matrix organizations are supported","License tier features not fully documented — unclear which features are gated behind premium tiers"],"requires":["V7 Go account with admin access","User email addresses for provisioning","Decision on role assignments and license tier per user"],"input_types":["user email and identity information","role assignment (admin, analyst, viewer, etc.)","license tier selection (basic, premium, enterprise)"],"output_types":["user access tokens/sessions","permission matrices (which users can access which agents/workflows)","license usage reports (active users per tier)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"v7__cap_5","uri":"capability://data.processing.analysis.financial.document.extraction.and.analysis.with.domain.specific.entity.recognition","name":"financial document extraction and analysis with domain-specific entity recognition","description":"The Financial Agent extracts and analyzes financial documents (term sheets, cap tables, financial statements, pitch decks) using domain-specific entity recognition trained on financial terminology and deal structures. The agent identifies key metrics (valuation, funding amount, investor names, terms), extracts cap table data, and flags financial red flags (mismatched valuations, dilution anomalies). Extraction is performed via multi-step LLM reasoning chains that parse both structured tables and unstructured text, with confidence scoring for each extracted field.","intents":["I need to automatically extract valuation, funding amount, and investor names from term sheets and pitch decks","I want to parse cap tables and flag dilution or ownership anomalies automatically","I need to identify financial red flags (mismatched valuations, inconsistent terms) across multiple deal documents"],"best_for":["venture capital and private equity teams processing deal documents at scale","investment committees needing rapid financial document review and risk assessment","deal teams automating cap table parsing and investor tracking"],"limitations":["Financial Agent is pre-built and not customizable — cannot add custom extraction rules or financial metrics","Supported document types not fully documented — unclear if agent handles all financial document formats (e.g., XBRL, SEC filings)","Confidence scoring methodology is undocumented — unclear how confidence is calculated and whether low-confidence extractions are flagged for human review","No mention of multi-currency or multi-jurisdiction support — unclear if agent handles international deals with different accounting standards","Red flag detection rules are opaque — cannot customize which anomalies trigger alerts"],"requires":["V7 Go account with Financial Agent enabled","Financial documents in supported formats (PDF, Word, Excel, PowerPoint)","Integration with data sources (PitchBook, Dealroom) or manual document upload"],"input_types":["financial documents (term sheets, cap tables, financial statements, pitch decks)","document metadata (source, deal stage, investor names)","structured data (from PitchBook/Dealroom APIs)"],"output_types":["extracted financial metrics (JSON: valuation, funding amount, investor names, terms)","cap table data (ownership percentages, share classes, dilution calculations)","red flag alerts (mismatched valuations, inconsistent terms, anomalies)","confidence scores (per-field confidence for extracted data)","summary reports (deal overview, key terms, risk assessment)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"v7__cap_6","uri":"capability://data.processing.analysis.legal.document.analysis.with.contract.clause.extraction.and.risk.flagging","name":"legal document analysis with contract clause extraction and risk flagging","description":"The Legal Agent analyzes contracts and legal documents by extracting key clauses (liability, indemnification, termination, IP ownership), identifying non-standard terms, and flagging legal risks. The agent uses clause-level entity recognition to locate specific contract sections, extracts clause text and associated obligations, and compares against standard templates to identify deviations. Risk flags include missing standard protections, unfavorable terms, and potential conflicts with other documents in the deal.","intents":["I need to extract key contract clauses (liability, indemnification, termination) automatically from legal documents","I want to identify non-standard or unfavorable terms that deviate from our standard templates","I need to flag legal risks (missing protections, conflicting terms) across multiple contracts in a deal"],"best_for":["legal departments and in-house counsel automating contract review","deal teams identifying legal risks before signing","compliance teams ensuring contracts meet organizational standards"],"limitations":["Legal Agent is pre-built with standard clause definitions — cannot customize clause extraction rules or add jurisdiction-specific clauses","Supported document types not fully documented — unclear if agent handles all legal document types (e.g., NDAs, employment agreements, regulatory filings)","Jurisdiction handling not documented — unclear if agent understands differences between US, UK, EU legal frameworks","Risk flagging rules are opaque — cannot customize which terms trigger alerts or define custom risk thresholds","No mention of version control or amendment tracking — unclear if agent can compare contract versions or track changes"],"requires":["V7 Go account with Legal Agent enabled","Legal documents in supported formats (PDF, Word)","Integration with data sources or manual document upload"],"input_types":["legal documents (contracts, NDAs, agreements, regulatory filings)","document metadata (document type, jurisdiction, counterparty)","standard templates (for deviation detection)"],"output_types":["extracted clauses (JSON: clause type, clause text, associated obligations)","risk flags (missing standard protections, unfavorable terms, conflicts)","deviation analysis (comparison to standard templates)","summary reports (contract overview, key risks, recommended actions)","clause-level confidence scores"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"v7__cap_7","uri":"capability://data.processing.analysis.insurance.underwriting.document.analysis.with.risk.assessment","name":"insurance underwriting document analysis with risk assessment","description":"The Insurance Agent analyzes underwriting documents (applications, medical records, property assessments, loss history) to extract risk factors and generate underwriting recommendations. The agent identifies key risk indicators (health conditions, property hazards, claims history), calculates risk scores based on extracted factors, and flags cases requiring additional underwriting review. Analysis combines structured data extraction (from forms) with unstructured text analysis (medical notes, assessments) to provide comprehensive risk assessment.","intents":["I need to automatically extract risk factors from insurance applications and supporting documents","I want to calculate risk scores and identify high-risk cases requiring manual underwriting review","I need to flag missing information or inconsistencies in underwriting documents"],"best_for":["insurance underwriting teams automating document review and risk assessment","insurers processing high-volume applications and needing rapid risk scoring","compliance teams ensuring underwriting decisions meet regulatory requirements"],"limitations":["Insurance Agent is pre-built with standard risk factors — cannot customize risk scoring rules or add product-specific risk factors","Supported document types not fully documented — unclear if agent handles all underwriting document types (e.g., medical records, property assessments, loss histories)","Risk score methodology is undocumented — unclear how risk factors are weighted and combined into final scores","Regulatory compliance handling not documented — unclear if agent ensures compliance with insurance regulations (e.g., fair lending, anti-discrimination)","No mention of state-specific underwriting rules — unclear if agent understands differences between state insurance regulations"],"requires":["V7 Go account with Insurance Agent enabled","Underwriting documents in supported formats (PDF, Word, Excel)","Integration with document sources or manual document upload"],"input_types":["underwriting documents (applications, medical records, property assessments, loss history)","structured form data (applicant information, coverage details)","unstructured text (medical notes, property assessments, claims descriptions)"],"output_types":["extracted risk factors (JSON: health conditions, property hazards, claims history, etc.)","risk scores (numeric score and risk category: low, medium, high)","underwriting recommendations (approve, approve with conditions, refer for manual review, decline)","missing information flags (incomplete applications, missing documents)","summary reports (risk assessment, key factors, recommended actions)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"v7__cap_8","uri":"capability://automation.workflow.workflow.execution.scheduling.with.event.based.and.time.based.triggers","name":"workflow execution scheduling with event-based and time-based triggers","description":"V7 Go supports multiple trigger modes for workflow execution: event-based triggers (file arrival in data room, new PitchBook deal, Zapier webhook), scheduled triggers (daily/weekly batch processing), and manual triggers (user-initiated execution). Trigger configuration is defined per workflow, with support for conditional logic (e.g., only trigger if document type matches pattern). Execution is queued and processed asynchronously, with status tracking and completion notifications.","intents":["I want to automatically run my due diligence workflow every time a new deal appears in PitchBook","I need to schedule batch processing of documents every morning at 9 AM","I want to trigger analysis when files arrive in a specific OneDrive folder or data room"],"best_for":["deal teams automating repetitive document processing workflows","organizations with high document volume requiring scheduled batch processing","teams using event-driven architecture and seeking workflow automation integration"],"limitations":["Trigger latency not specified — no SLA for time between event occurrence and workflow execution","Conditional trigger logic is mentioned but not detailed — unclear what conditions are supported (file type matching, metadata filtering, etc.)","No mention of trigger retry logic — unclear behavior if trigger fails or workflow execution fails","Scheduled trigger granularity not specified — unclear if sub-hourly scheduling (e.g., every 15 minutes) is supported","No mention of trigger rate limiting — unclear if there are limits on concurrent workflow executions or trigger frequency"],"requires":["V7 Go account with workflow automation enabled","Trigger source configuration (data room API, PitchBook API, Zapier account, OneDrive integration)","Workflow definition with trigger rules configured"],"input_types":["event payloads (file upload notifications, API events, Zapier webhooks)","schedule definitions (cron expressions or UI-based scheduling)","trigger conditions (file type, metadata filters)"],"output_types":["workflow execution queue (pending, running, completed, failed)","execution status notifications (email, webhook, dashboard)","execution logs (trigger time, execution duration, results)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"v7__cap_9","uri":"capability://data.processing.analysis.document.metadata.extraction.and.enrichment.with.source.tracking","name":"document metadata extraction and enrichment with source tracking","description":"V7 Go automatically extracts and enriches document metadata during ingestion, including source system (PitchBook, Dealroom, OneDrive), upload timestamp, document type classification, and associated deal/company information. Metadata is used for routing (which agent to invoke), filtering (which documents to process), and tracking (audit trail of document origin). Enrichment includes linking documents to CRM records and adding contextual information from source systems (company name, deal stage, investor names).","intents":["I want to automatically classify documents by type (term sheet, cap table, financial statement) during ingestion","I need to track which documents came from which source (PitchBook vs. data room) for audit purposes","I want to enrich documents with deal context (company name, investor names, deal stage) from source systems"],"best_for":["organizations processing documents from multiple sources and needing unified metadata","teams requiring audit trails and document provenance tracking","deal teams needing document context (deal stage, investor names) for routing and analysis"],"limitations":["Document type classification is automatic but customization options unknown — cannot define custom document types or classification rules","Metadata enrichment is limited to documented source systems (PitchBook, Dealroom, OneDrive) — cannot enrich from custom data sources","Metadata schema is undocumented — unclear what fields are extracted and whether custom metadata fields can be added","No mention of metadata validation — unclear if extracted metadata is validated against source systems or if inconsistencies are flagged"],"requires":["V7 Go account with document ingestion enabled","Integration with source systems (PitchBook, Dealroom, OneDrive) for metadata enrichment","Document upload or API integration for metadata extraction"],"input_types":["documents (PDF, Word, Excel, PowerPoint)","source system metadata (PitchBook deal info, Dealroom folder structure, OneDrive properties)","file properties (filename, upload timestamp, file size)"],"output_types":["extracted metadata (JSON: document type, source, timestamp, associated deal/company)","enriched metadata (linked CRM records, deal context, investor names)","metadata tags (for filtering and routing)","audit trail (document origin, enrichment history)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"v7__headline","uri":"capability://data.processing.analysis.ai.training.data.platform.with.auto.annotation.and.human.review","name":"ai training data platform with auto-annotation and human review","description":"V7 is an AI training data platform that combines automated annotation with human review for images, videos, and documents, streamlining the dataset creation process for machine learning applications.","intents":["best AI training data platform","AI dataset creation for machine learning","auto-annotation tools for images and videos","dataset management solutions for AI","human-reviewed annotation services","integrated model training datasets"],"best_for":["data scientists","machine learning engineers"],"limitations":["dependent on human review","specific to certain industries"],"requires":["internet connection","user training"],"input_types":["images","videos","documents"],"output_types":["annotated datasets","validated labels"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["V7 Go platform account with appropriate license tier","Integration credentials for data sources (PitchBook, Dealroom, OneDrive, data room APIs)","Zapier account or direct API access for trigger configuration (if using event-based activation)","Destination system credentials (CRM, OneDrive, data room) for output routing","V7 Go account with data source integrations enabled","API credentials or OAuth tokens for each data source (PitchBook, Dealroom, OneDrive, etc.)","Zapier account if using event-based triggers from third-party apps","Appropriate file permissions in source systems (read access to documents)","V7 Go account with workflow monitoring enabled","Email address or webhook URL for notifications"],"failure_modes":["Agents are pre-built and domain-specific — no custom agent creation or fine-tuning documented; users cannot modify agent reasoning logic","Workflow definitions are stored in V7 platform with unknown export format — high vendor lock-in if migration is needed","No real-time API inference mode — workflows are asynchronous, not suitable for sub-second latency requirements","Agent behavior and model versions are opaque — no control over which LLM backbone or version is used","No multi-tenancy documentation — unclear if agents can be isolated per customer or shared across organizations","Supported data sources are limited to documented integrations (PitchBook, Dealroom, OneDrive, data rooms) — custom source connectors not documented","Trigger latency not specified — no SLA for time between document arrival and agent activation","No bulk historical ingestion documented — appears to support forward-looking triggers only, not backfill of existing document libraries","Metadata extraction is automatic but customization options unknown — cannot specify custom extraction rules per source","Error handling and retry logic not documented — unclear if failed executions are automatically retried or require manual intervention","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.118Z","last_scraped_at":null,"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=v7","compare_url":"https://unfragile.ai/compare?artifact=v7"}},"signature":"UOvTj4mjaPzP1vRCfW6//QBbxm1xo9LMQAHZsjujI3a6CgjzmTMA8RQ/5kLQP56BhU+ZGfLYPDVrQTwnTeG8Cg==","signedAt":"2026-06-21T07:34:32.295Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/v7","artifact":"https://unfragile.ai/v7","verify":"https://unfragile.ai/api/v1/verify?slug=v7","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"}}