{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_zeda-io","slug":"zeda-io","name":"Zeda.io","type":"product","url":"https://zeda.io","page_url":"https://unfragile.ai/zeda-io","categories":["automation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_zeda-io__cap_0","uri":"capability://data.processing.analysis.multi.source.feedback.aggregation.and.centralization","name":"multi-source feedback aggregation and centralization","description":"Ingests customer feedback from 5000+ external data sources (Salesforce, HubSpot, surveys, call transcripts, product analytics, Zapier integrations) into a unified database, normalizing disparate formats and timestamps into a single queryable feedback repository. Uses connector-based architecture to maintain bi-directional sync with source systems while preserving original data context and metadata for traceability.","intents":["I need to stop manually copying feedback from Salesforce, surveys, and call transcripts into spreadsheets","I want a single source of truth for all customer feedback across my entire organization","I need to track which feedback came from which source and customer segment without manual tagging"],"best_for":["product teams managing feedback across 5+ disparate tools","mid-market companies with distributed customer data","GTM teams needing unified customer signal visibility"],"limitations":["No stated limits on feedback volume or concurrent ingestion rate","Connector reliability depends on third-party API stability (Zapier, Salesforce, HubSpot)","Data normalization approach not disclosed — may lose source-specific metadata","No documented SLA for sync latency between source system and Zeda database"],"requires":["Active account with at least one external data source (Salesforce, HubSpot, survey tool, etc.)","API credentials or OAuth tokens for connected systems","14-day free trial or paid subscription ($499/month annual commitment)"],"input_types":["structured CRM data (Salesforce, HubSpot)","survey responses (JSON, CSV)","call transcripts (text, audio transcription)","product analytics events","manual feedback entries","Zapier-routed data from 5000+ apps"],"output_types":["normalized feedback entries with source attribution","unified feedback database with metadata","queryable feedback repository for downstream analysis"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_zeda-io__cap_1","uri":"capability://data.processing.analysis.ai.powered.feedback.categorization.and.tagging","name":"ai-powered feedback categorization and tagging","description":"Automatically classifies ingested feedback into predefined categories (Complaints, Requests, Opportunities, Lost Deals) using an undisclosed AI/ML model, then tags feedback with custom attributes (customer segment, revenue impact, product area). Processes feedback asynchronously to assign structured metadata without requiring manual user labeling, enabling downstream filtering and aggregation.","intents":["I want to automatically sort feedback into complaints vs feature requests vs opportunities without manual review","I need to tag feedback with revenue impact and customer segment so I can prioritize by business value","I want to understand which product areas have the most customer pain points without reading every feedback entry"],"best_for":["product teams with high feedback volume (100+ entries/month)","organizations needing consistent categorization across diverse feedback sources","teams lacking dedicated customer research staff for manual tagging"],"limitations":["Categorization model and accuracy metrics not disclosed — unknown how well it handles domain-specific language","No ability to customize categorization taxonomy beyond the four predefined categories","Accuracy likely degrades on feedback in non-English languages or highly technical domains","No feedback loop mechanism disclosed for users to correct misclassifications and improve model","Revenue impact attribution mechanism unknown — may use heuristics rather than actual customer data"],"requires":["Feedback ingested into Zeda database via one of 5000+ integrations","Paid subscription ($499/month minimum)","Optional: customer segment or revenue data in source systems for impact attribution"],"input_types":["unstructured text feedback (customer interviews, call transcripts, survey responses)","structured CRM data with customer metadata"],"output_types":["categorized feedback entries (Complaints, Requests, Opportunities, Lost Deals)","custom tags and attributes","revenue impact scores (if customer data available)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_zeda-io__cap_10","uri":"capability://data.processing.analysis.customer.segment.and.cohort.analysis","name":"customer segment and cohort analysis","description":"Enables product teams to segment feedback by customer attributes (company size, industry, revenue tier, product usage, churn status) and analyze patterns within cohorts. Uses customer metadata from integrated CRM systems to automatically tag feedback with segment information, enabling comparison of feedback patterns across different customer groups. Supports cohort-based reporting and filtering.","intents":["I want to understand how enterprise customers' feedback differs from SMB customers","I need to identify which customer segments are most at risk of churn based on feedback signals","I want to prioritize features based on feedback from our highest-value customer segments"],"best_for":["B2B SaaS companies with diverse customer segments","organizations with significant revenue variance across customer tiers","product teams making segment-specific roadmap decisions"],"limitations":["Segment definitions depend on CRM data quality — garbage in, garbage out","Automatic segment tagging accuracy not disclosed — may misclassify customers if CRM data is incomplete","Cohort analysis limited to predefined segments — no ability to create ad-hoc cohorts based on custom criteria","Cohort size thresholds not disclosed — may suppress insights from small segments for privacy","No disclosed ability to compare feedback patterns across cohorts statistically (e.g., significance testing)"],"requires":["CRM integration (Salesforce, HubSpot) with customer segment data","Paid subscription ($499/month minimum)","Clean customer metadata in source CRM system"],"input_types":["feedback entries with customer identity","customer segment metadata from CRM"],"output_types":["segment-tagged feedback","cohort-based reports and insights","segment comparison analysis"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_zeda-io__cap_11","uri":"capability://tool.use.integration.integration.with.external.roadmap.and.project.management.tools","name":"integration with external roadmap and project management tools","description":"Exports insights, feature definitions, and roadmap items to external tools (Productboard, Aha, Jira, Linear) via API or direct integrations. Maintains linkage between Zeda insights and external roadmap items, enabling traceability from customer feedback to shipped features. Supports bi-directional sync where available (specific integrations unknown).","intents":["I want to create Jira tickets from customer feedback without manual data entry","I need to link Productboard features back to the customer feedback that inspired them","I want to sync roadmap status updates from Aha back into Zeda so the team sees progress"],"best_for":["teams already using Productboard, Aha, Jira, or Linear for roadmap management","organizations wanting to maintain single source of truth for roadmap decisions","teams needing traceability from customer feedback to shipped features"],"limitations":["Supported integrations not fully disclosed — may be limited to major tools (Productboard, Aha, Jira)","Bi-directional sync support unknown — may be one-way export only","Sync frequency not specified — may be batch sync rather than real-time","Custom field mapping not disclosed — may not support custom fields in external tools","Conflict resolution for bi-directional edits not disclosed"],"requires":["Active account with external roadmap tool (Productboard, Aha, Jira, Linear, etc.)","Paid subscription ($499/month minimum)","API credentials or OAuth tokens for external tool"],"input_types":["feature definitions and roadmap items from Zeda","roadmap status updates from external tools (if bi-directional)"],"output_types":["exported roadmap items in external tool format","linked feedback-to-feature relationships","synced status updates"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_zeda-io__cap_12","uri":"capability://data.processing.analysis.competitive.feedback.and.market.intelligence.collection","name":"competitive feedback and market intelligence collection","description":"Aggregates feedback mentioning competitors or competitive features, enabling product teams to track competitive positioning and identify feature gaps. Uses keyword matching and NLP to identify competitor mentions in customer feedback, then surfaces competitive intelligence in reports and alerts. Supports tracking of specific competitors and competitive features.","intents":["I want to know what customers are saying about our competitors so we can improve our positioning","I need to identify features that competitors have that we're missing based on customer feedback","I want to track when customers mention switching to competitors so I can intervene"],"best_for":["product teams in competitive markets with multiple alternatives","GTM teams needing competitive intelligence for sales enablement","organizations tracking competitive win/loss analysis"],"limitations":["Competitor detection accuracy depends on keyword matching — may miss indirect competitor mentions","Competitive feature extraction not disclosed — unknown if it's automated or manual","No disclosed ability to customize competitor list or feature tracking","Competitive intelligence limited to customer feedback — misses public competitive announcements or market research","Privacy concerns with tracking competitor mentions may require customer consent"],"requires":["Feedback corpus with competitor mentions","Paid subscription ($499/month minimum)","Optional: predefined list of competitors to track"],"input_types":["customer feedback mentioning competitors","competitor names and feature keywords"],"output_types":["competitive feedback summaries","feature gap analysis","competitive intelligence reports"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_zeda-io__cap_2","uri":"capability://search.retrieval.natural.language.query.interface.for.feedback.analysis","name":"natural language query interface for feedback analysis","description":"Provides an 'Ask AI' tool that accepts natural language questions about the aggregated feedback database and returns answers grounded in actual customer data. Uses retrieval-augmented generation (inferred) to search the feedback corpus and synthesize responses, enabling product teams to validate hypotheses or discover patterns without writing database queries or manually reviewing feedback.","intents":["I want to ask 'What are customers saying about our pricing?' and get a summary of relevant feedback","I need to validate a hypothesis like 'Are enterprise customers complaining about integrations?' without manually searching","I want to discover unexpected patterns in customer feedback by asking open-ended questions"],"best_for":["product managers who prefer conversational interfaces over dashboards","teams validating product hypotheses against real customer data","non-technical stakeholders who need quick answers without SQL knowledge"],"limitations":["Query response quality depends on feedback database size and relevance — unknown minimum corpus size for accurate answers","No disclosed mechanism for users to verify which specific feedback entries informed the AI response","Hallucination risk unknown — no stated safeguards against AI inventing customer feedback that doesn't exist","Context window size unknown — may truncate responses or miss relevant feedback if corpus is very large","No ability to export or cite sources for AI-generated answers"],"requires":["Feedback database populated with at least 50+ entries (estimated minimum for meaningful analysis)","Paid subscription ($499/month minimum)","Natural language query capability (no special syntax required)"],"input_types":["natural language questions (e.g., 'What are customers saying about pricing?')"],"output_types":["natural language summaries of feedback patterns","answers grounded in customer data (specific citations unknown)"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_zeda-io__cap_3","uri":"capability://planning.reasoning.predictive.opportunity.and.risk.alerting","name":"predictive opportunity and risk alerting","description":"Analyzes historical feedback patterns using predictive models (specific approach undisclosed) to forecast emerging customer issues, churn risks, and feature opportunities before they become widespread problems. Generates 'Opportunity Radar' reports that surface early signals of customer dissatisfaction or unmet needs, enabling proactive product decisions rather than reactive responses to complaints.","intents":["I want to identify emerging customer pain points before they cause churn","I need to spot feature opportunities that multiple customers are hinting at but haven't explicitly requested","I want to predict which customer segments are at risk of leaving based on feedback signals"],"best_for":["product teams with 6+ months of historical feedback data","organizations prioritizing proactive product strategy over reactive bug fixes","mid-market companies with customer churn concerns"],"limitations":["Predictive model accuracy metrics not disclosed — unknown false positive/negative rates","Minimum historical data requirement unknown — may require 6-12 months of feedback for reliable predictions","Model retraining frequency unknown — may use stale patterns if feedback distribution shifts","No disclosed mechanism for users to validate predictions or provide feedback on accuracy","Churn prediction likely requires customer lifecycle data (contract dates, usage metrics) not always available in feedback systems"],"requires":["Minimum 3-6 months of historical feedback data (estimated)","Paid subscription ($499/month minimum)","Optional: customer lifecycle data (churn dates, contract values) for improved predictions"],"input_types":["historical feedback corpus with timestamps","customer metadata (segment, revenue, churn status)"],"output_types":["Opportunity Radar reports highlighting emerging issues","risk scores for customer segments","predicted feature opportunities with supporting feedback"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_zeda-io__cap_4","uri":"capability://text.generation.language.templated.ai.insight.report.generation","name":"templated ai insight report generation","description":"Generates customizable insight reports that synthesize aggregated feedback into actionable summaries, filtered by customer segment, feedback source, revenue impact, or product area. Uses generative AI to compose narrative reports with supporting data, enabling product teams to share findings with stakeholders without manual synthesis. Reports can be filtered, scheduled, and exported for distribution.","intents":["I need to create a monthly report on customer feedback for the executive team without spending hours writing","I want to generate a report on feedback from enterprise customers specifically to inform our premium tier roadmap","I need to share insights with the sales team about common customer objections without manually compiling data"],"best_for":["product managers creating stakeholder reports","GTM teams sharing customer insights with sales/success","organizations with formal roadmap review cycles requiring documented customer input"],"limitations":["Report templates not customizable — limited to predefined formats","AI-generated narrative quality depends on feedback diversity and clarity — may produce generic summaries if feedback is sparse","No disclosed ability to add custom sections or analysis beyond the templated structure","Export formats unknown — may be limited to PDF/email rather than editable documents","Scheduling frequency not disclosed — unknown if reports can be generated on-demand or only on fixed schedules"],"requires":["Feedback database with at least 50+ entries","Paid subscription ($499/month minimum)","Optional: custom filters (segment, source, revenue tier) for targeted reports"],"input_types":["aggregated feedback corpus","custom filter criteria (segment, source, revenue impact, product area)"],"output_types":["formatted insight reports (PDF, email, dashboard view)","narrative summaries with supporting data","actionable recommendations"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_zeda-io__cap_5","uri":"capability://text.generation.language.ai.assisted.feature.definition.and.roadmap.item.creation","name":"ai-assisted feature definition and roadmap item creation","description":"Converts customer insights and feedback patterns into structured feature definitions that can be directly imported into product roadmap tools. Uses AI to synthesize feedback into feature requirements, acceptance criteria, and business justification, reducing manual work of translating customer feedback into actionable roadmap items. Supports export to external roadmap tools (specific integrations unknown).","intents":["I want to turn a cluster of customer feedback into a properly formatted feature spec without writing it from scratch","I need to create roadmap items with customer justification automatically rather than manually documenting why we're building something","I want to batch-create features from feedback patterns so I can import them into Productboard or Aha"],"best_for":["product teams using external roadmap tools (Productboard, Aha, Jira)","organizations with high feature request volume needing faster spec creation","teams wanting to maintain audit trail of customer feedback → feature mapping"],"limitations":["Export format and compatibility with roadmap tools not disclosed","AI-generated feature specs likely require human review and refinement — not production-ready without editing","No disclosed ability to customize feature template fields or acceptance criteria format","Bi-directional sync with roadmap tools unknown — may be one-way export only","No mechanism disclosed for tracking which feedback informed which feature after export"],"requires":["Feedback corpus with clear feature requests or opportunity signals","Paid subscription ($499/month minimum)","Optional: integration with external roadmap tool (Productboard, Aha, Jira) for direct import"],"input_types":["categorized feedback (Requests, Opportunities)","customer segment and revenue impact data"],"output_types":["structured feature definitions","acceptance criteria","business justification and customer quotes","roadmap item exports (format unknown)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_zeda-io__cap_6","uri":"capability://text.generation.language.ai.generated.release.notes.from.feature.data","name":"ai-generated release notes from feature data","description":"Automatically generates customer-facing release notes from feature definitions, product updates, and associated customer feedback. Uses generative AI to compose narrative release notes that highlight customer benefits and context rather than technical implementation details. Supports customization by audience (customers, internal team, partners) and export to multiple formats.","intents":["I want to generate release notes that highlight customer benefits without manually writing them","I need to create different versions of release notes for different audiences (customers vs internal team)","I want to include customer feedback context in release notes to show we're listening to customer requests"],"best_for":["product teams managing frequent releases (weekly or bi-weekly)","organizations wanting to highlight customer-driven features in release notes","teams lacking dedicated technical writing resources"],"limitations":["Release note quality depends on feature description clarity — garbage in, garbage out","No disclosed ability to customize tone or style per audience","Export formats not specified — may be limited to email/PDF rather than markdown or HTML","No mechanism disclosed for version control or release note approval workflows","AI-generated notes likely require human review for accuracy and brand voice consistency"],"requires":["Feature definitions with clear descriptions and customer context","Paid subscription ($499/month minimum)","Optional: customer feedback quotes to include in release notes"],"input_types":["feature definitions","product update descriptions","associated customer feedback and quotes"],"output_types":["narrative release notes (email, PDF, web format)","audience-specific versions","formatted release note documents"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_zeda-io__cap_7","uri":"capability://automation.workflow.real.time.feedback.monitoring.and.alerting","name":"real-time feedback monitoring and alerting","description":"Monitors incoming feedback streams for critical signals (e.g., churn risk indicators, security concerns, widespread complaints) and sends real-time alerts to designated team members. Uses rule-based or ML-based detection to identify high-priority feedback patterns and escalates them immediately rather than waiting for scheduled reports. Supports custom alert rules and notification channels (email, Slack, webhooks).","intents":["I want to be notified immediately if multiple customers mention a critical bug or security issue","I need to catch churn signals early so I can reach out to at-risk customers before they leave","I want to know when a new feature request gains traction across multiple customers"],"best_for":["product teams managing high-volume feedback streams","organizations with customer success teams needing early churn warnings","companies prioritizing rapid response to critical customer issues"],"limitations":["Alert rule configuration and customization not disclosed — may be limited to predefined rules","False positive rate unknown — may generate alert fatigue if sensitivity is too high","Notification channels supported not fully specified — Slack integration mentioned but others unknown","No disclosed mechanism for users to tune alert sensitivity or suppress non-critical patterns","Latency between feedback ingestion and alert delivery not specified — may not be truly real-time"],"requires":["Feedback ingestion pipeline configured for at least one data source","Paid subscription ($499/month minimum)","Optional: Slack workspace or webhook endpoint for alert delivery"],"input_types":["incoming feedback stream","custom alert rule definitions"],"output_types":["real-time alerts (email, Slack, webhook)","alert summaries with supporting feedback"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_zeda-io__cap_8","uri":"capability://search.retrieval.feedback.search.and.filtering.with.metadata","name":"feedback search and filtering with metadata","description":"Provides a queryable interface to search and filter the centralized feedback database using multiple dimensions: customer segment, revenue impact, product area, feedback source, sentiment, and custom tags. Uses full-text search combined with metadata filtering to enable product teams to quickly locate relevant feedback without manual review. Supports saved searches and filters for recurring queries.","intents":["I need to find all feedback from enterprise customers about our API to inform a technical roadmap","I want to see which product areas have the most complaints so I can prioritize fixes","I need to pull all feedback mentioning 'integration' across all sources to understand integration demand"],"best_for":["product teams with large feedback volumes (1000+ entries)","organizations needing ad-hoc analysis without waiting for reports","teams using feedback to inform multiple decisions (roadmap, support, marketing)"],"limitations":["Search performance not disclosed — unknown if latency degrades with very large feedback volumes","Full-text search capabilities not specified — unknown if it supports phrase search, wildcards, or boolean operators","Metadata filtering limited to predefined dimensions — no ability to create custom filter dimensions","No disclosed ability to save complex filter combinations or create reusable search templates","Search result ranking algorithm not disclosed — may not surface most relevant results first"],"requires":["Feedback database populated with at least 100+ entries","Paid subscription ($499/month minimum)","Optional: metadata enrichment (customer segment, revenue tier) for advanced filtering"],"input_types":["search queries (free-text and structured)","filter criteria (segment, source, sentiment, product area)"],"output_types":["filtered feedback results","result summaries with metadata","export of search results"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_zeda-io__cap_9","uri":"capability://automation.workflow.team.collaboration.and.approval.workflows","name":"team collaboration and approval workflows","description":"Enables multiple team members to collaborate on feedback analysis, insights, and roadmap decisions within Zeda. Supports role-based access control, comment threads on feedback entries, and approval workflows for publishing insights or features. Maintains audit trails of who made changes and when, reducing coordination overhead across distributed teams.","intents":["I want my product team to review and comment on customer feedback before we make decisions","I need to require approval from leadership before publishing insights to stakeholders","I want to track who made changes to feature definitions and when for accountability"],"best_for":["distributed product teams across multiple time zones","organizations with formal approval processes for roadmap decisions","teams needing audit trails for compliance or governance"],"limitations":["Role-based access control granularity not disclosed — unknown if roles are predefined or customizable","Approval workflow configuration not specified — may be limited to simple approve/reject rather than conditional workflows","Comment threading and discussion features not detailed — unknown if they support @mentions, rich formatting, or notifications","Concurrent editing support unknown — may not support real-time collaborative editing like Google Docs","Integration with external approval systems (Jira, Asana) not disclosed"],"requires":["Paid subscription ($499/month minimum)","Team member accounts (per-seat pricing unknown)","Optional: integration with identity provider (SSO) for access control"],"input_types":["feedback entries, insights, feature definitions","user comments and approvals"],"output_types":["commented feedback with discussion threads","approval status and audit trails","published insights and features"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Active account with at least one external data source (Salesforce, HubSpot, survey tool, etc.)","API credentials or OAuth tokens for connected systems","14-day free trial or paid subscription ($499/month annual commitment)","Feedback ingested into Zeda database via one of 5000+ integrations","Paid subscription ($499/month minimum)","Optional: customer segment or revenue data in source systems for impact attribution","CRM integration (Salesforce, HubSpot) with customer segment data","Clean customer metadata in source CRM system","Active account with external roadmap tool (Productboard, Aha, Jira, Linear, etc.)","API credentials or OAuth tokens for external tool"],"failure_modes":["No stated limits on feedback volume or concurrent ingestion rate","Connector reliability depends on third-party API stability (Zapier, Salesforce, HubSpot)","Data normalization approach not disclosed — may lose source-specific metadata","No documented SLA for sync latency between source system and Zeda database","Categorization model and accuracy metrics not disclosed — unknown how well it handles domain-specific language","No ability to customize categorization taxonomy beyond the four predefined categories","Accuracy likely degrades on feedback in non-English languages or highly technical domains","No feedback loop mechanism disclosed for users to correct misclassifications and improve model","Revenue impact attribution mechanism unknown — may use heuristics rather than actual customer data","Segment definitions depend on CRM data quality — garbage in, garbage out","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.25,"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":"2026-05-24T12:16:34.117Z","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=zeda-io","compare_url":"https://unfragile.ai/compare?artifact=zeda-io"}},"signature":"3r53feiy7szQQqsTTBd1ov92lVgRJMUBpS/bIwLkX6IZEhXeSB4QbvJVx6qQzsW8fkG2aI25M9lCLy+2+K6FCA==","signedAt":"2026-06-21T10:28:49.354Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/zeda-io","artifact":"https://unfragile.ai/zeda-io","verify":"https://unfragile.ai/api/v1/verify?slug=zeda-io","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"}}