{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_parabolic","slug":"parabolic","name":"Parabolic","type":"product","url":"https://www.growparabolic.com","page_url":"https://unfragile.ai/parabolic","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_parabolic__cap_0","uri":"capability://planning.reasoning.intelligent.ticket.triage.and.routing","name":"intelligent-ticket-triage-and-routing","description":"Automatically analyzes incoming support tickets using NLP to extract intent, urgency, and category signals, then routes them to the most appropriate agent or queue based on learned patterns and skill matching. The system likely uses text classification models trained on historical ticket data to identify ticket type, priority level, and required expertise, reducing manual sorting overhead and ensuring faster first-response times by eliminating queue bottlenecks.","intents":["I want to automatically sort incoming tickets by type and urgency without manual triage","I need tickets routed to the right agent based on their expertise to reduce back-and-forth","I want to identify high-priority tickets immediately so critical issues don't get buried"],"best_for":["Support teams with 50-500+ monthly tickets handling mixed issue types","Startups without dedicated triage staff looking to reduce manual sorting","Teams using generic helpdesk systems without built-in smart routing"],"limitations":["Routing accuracy depends on historical ticket volume and labeling quality—teams with <100 labeled tickets may see poor initial performance","No visibility into how routing rules are learned or customized; likely uses black-box classification","Requires integration with existing helpdesk platform; unclear which systems are supported beyond basic API webhooks"],"requires":["Active support ticket stream (minimum 20-50 tickets/month for meaningful pattern detection)","Helpdesk platform with webhook or API support for ticket ingestion","Historical ticket data (optional but recommended for better routing accuracy)"],"input_types":["text (ticket subject, description, customer message)","structured metadata (priority flags, customer tier, issue category tags)"],"output_types":["routing decision (agent ID, queue name, priority level)","structured ticket metadata (inferred category, urgency score, skill requirements)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_parabolic__cap_1","uri":"capability://memory.knowledge.ai.powered.ticket.resolution.suggestions","name":"ai-powered-ticket-resolution-suggestions","description":"Analyzes ticket content and knowledge base articles to suggest or auto-generate resolution steps for common issues, reducing agent resolution time by providing contextual answers without requiring manual knowledge base searches. The system likely uses semantic search or retrieval-augmented generation (RAG) to match incoming tickets against historical resolutions and knowledge base entries, then surfaces the most relevant solutions with confidence scores to agents or customers.","intents":["I want AI to suggest answers to common support questions so agents can resolve faster","I need to reduce repetitive manual responses to frequently asked questions","I want customers to see suggested solutions before contacting support"],"best_for":["Support teams with high volume of repetitive or FAQ-type tickets","Teams with existing knowledge bases or documented resolutions they want to leverage","Organizations looking to reduce average resolution time without hiring more agents"],"limitations":["Suggestion quality depends on knowledge base completeness and relevance—sparse or outdated KBs will produce poor suggestions","No indication of how suggestions are ranked or filtered; may surface irrelevant or conflicting solutions without clear confidence scoring","Likely requires manual knowledge base maintenance; no indication of automatic knowledge base generation or updating from resolved tickets"],"requires":["Existing knowledge base or historical ticket resolution data","Minimum 50-100 documented resolutions for meaningful semantic matching","Integration with helpdesk platform for ticket content access"],"input_types":["text (ticket subject, description, customer message)","structured knowledge base (articles, FAQs, resolution templates)"],"output_types":["ranked suggestion list (article/resolution with relevance score and confidence)","suggested response text (auto-generated or templated resolution)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_parabolic__cap_2","uri":"capability://text.generation.language.automated.ticket.response.generation","name":"automated-ticket-response-generation","description":"Generates contextually appropriate initial or follow-up responses to support tickets using language models, potentially with guardrails to ensure responses stay within policy boundaries and maintain brand voice. The system likely uses prompt engineering or fine-tuning to generate responses that match the support team's tone and include relevant information from the ticket context, knowledge base, or customer history, with optional human review workflows before sending.","intents":["I want to auto-generate initial acknowledgment responses to reduce first-response time","I need AI to draft responses for simple issues so agents can focus on complex problems","I want to ensure all responses follow our support policies and brand voice"],"best_for":["Support teams with high volume of simple or repetitive tickets","Organizations with well-defined support policies and response templates","Teams looking to reduce response time SLAs without hiring additional agents"],"limitations":["Generated responses may hallucinate or provide inaccurate information if knowledge base is incomplete or outdated","No indication of human review workflows; unclear whether responses are auto-sent or require approval","Risk of tone/brand voice inconsistency if model is not fine-tuned on team's historical responses","May struggle with context-dependent issues requiring customer history or account-specific information"],"requires":["Integration with helpdesk platform for ticket content and customer context","Optional: historical response examples for fine-tuning or prompt engineering","Optional: knowledge base or policy documents for grounding responses"],"input_types":["text (ticket subject, description, customer message)","structured context (customer tier, account history, previous interactions)","knowledge base or policy documents (optional)"],"output_types":["generated response text (ready to send or for human review)","confidence score or quality metrics (optional)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_parabolic__cap_3","uri":"capability://planning.reasoning.ticket.priority.and.urgency.detection","name":"ticket-priority-and-urgency-detection","description":"Automatically identifies and flags high-priority or urgent tickets based on linguistic signals, customer metadata, and historical patterns, ensuring critical issues surface immediately rather than being buried in the queue. The system likely uses multi-signal classification combining text analysis (keywords like 'urgent', 'down', 'broken'), customer tier/SLA data, and learned patterns from historical ticket escalations to assign urgency scores and trigger alerts.","intents":["I want to automatically flag critical or urgent tickets so they don't get missed","I need to identify high-value customer issues immediately based on customer tier","I want to detect outages or widespread issues from ticket language patterns"],"best_for":["Support teams with mixed ticket volumes where critical issues can get buried","SaaS or service companies with SLA commitments requiring rapid escalation","Teams handling both routine and critical issues without dedicated triage staff"],"limitations":["Urgency detection may produce false positives if customers use urgent language for non-critical issues","Relies on consistent customer tier/SLA metadata; missing or incorrect metadata reduces accuracy","No indication of how urgency thresholds are set or customized per organization","May miss novel or unusual critical issues that don't match learned patterns"],"requires":["Historical ticket data with urgency/escalation labels for training","Customer metadata (tier, SLA, account value) for context-aware scoring","Minimum 100-200 labeled examples of urgent vs routine tickets"],"input_types":["text (ticket subject, description, customer message)","structured metadata (customer tier, SLA level, account value, issue category)"],"output_types":["urgency score (numeric or categorical: low/medium/high/critical)","alert or escalation trigger (boolean or routing decision)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_parabolic__cap_4","uri":"capability://data.processing.analysis.support.metrics.and.performance.analytics","name":"support-metrics-and-performance-analytics","description":"Tracks and visualizes key support metrics like resolution time, first-response time, ticket volume trends, and agent performance, providing dashboards and insights to identify bottlenecks and optimization opportunities. The system likely aggregates ticket data from the helpdesk platform and applies statistical analysis or trend detection to surface actionable insights like which issue types take longest to resolve or which agents have highest satisfaction scores.","intents":["I want to see how AI automation is impacting our resolution time and ticket volume","I need to identify which issue types or agents are bottlenecks","I want to track team performance metrics and set improvement targets"],"best_for":["Support managers and team leads tracking automation ROI","Organizations with 50+ monthly tickets wanting to identify optimization opportunities","Teams using multiple helpdesk systems wanting unified analytics"],"limitations":["Analytics quality depends on data completeness and consistency in helpdesk platform","No indication of real-time vs batch analytics; likely has reporting lag","Unclear what metrics are available or customizable; may be limited to standard KPIs","No indication of predictive analytics or forecasting capabilities"],"requires":["Integration with helpdesk platform for ticket data access","Minimum 30-50 days of ticket history for meaningful trend analysis","Consistent ticket tagging and metadata for segmentation"],"input_types":["structured ticket data (timestamps, resolution time, agent ID, category, customer tier)","optional: customer satisfaction scores or CSAT data"],"output_types":["dashboard visualizations (charts, tables, KPI cards)","trend reports (resolution time trends, volume forecasts, agent performance rankings)","alerts (SLA violations, unusual patterns)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_parabolic__cap_5","uri":"capability://tool.use.integration.helpdesk.platform.integration.and.data.sync","name":"helpdesk-platform-integration-and-data-sync","description":"Integrates with existing helpdesk platforms (Zendesk, Intercom, Jira Service Management, etc.) via APIs or webhooks to ingest ticket data, sync routing decisions, and push generated responses back to the platform. The system likely uses event-driven architecture with webhooks for real-time ticket ingestion and bidirectional sync to ensure ticket state remains consistent across Parabolic and the helpdesk platform without manual data entry.","intents":["I want to connect Parabolic to my existing helpdesk without replacing it","I need ticket data to sync automatically so agents see AI suggestions in their workflow","I want AI-generated responses to appear in my helpdesk without manual copy-paste"],"best_for":["Teams with existing helpdesk investments wanting to add AI without migration","Organizations using Zendesk, Intercom, or similar platforms","Support teams wanting minimal workflow disruption during AI adoption"],"limitations":["Integration coverage unclear—no visible list of supported helpdesk platforms; may only support popular systems","Sync latency unknown; real-time sync may have delays affecting agent experience","No indication of data transformation or mapping capabilities; may require custom field setup","Unclear how conflicts are handled if ticket state changes in both systems simultaneously"],"requires":["Active account with supported helpdesk platform (Zendesk, Intercom, Jira Service Management, etc.)","API credentials or OAuth token for helpdesk platform","Webhook configuration or API polling setup (depending on integration method)"],"input_types":["webhook events (ticket created, updated, closed)","API responses (ticket details, customer metadata, agent assignments)"],"output_types":["ticket updates (routing decisions, priority changes, assigned agent)","response text (pushed to ticket comments or reply field)","structured metadata (inferred category, urgency score)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_parabolic__cap_6","uri":"capability://search.retrieval.customer.self.service.ticket.resolution","name":"customer-self-service-ticket-resolution","description":"Enables customers to resolve issues themselves through AI-powered suggestions or automated responses before creating support tickets, reducing inbound ticket volume and improving customer satisfaction. The system likely surfaces suggested solutions on a customer portal or chatbot interface, allowing customers to self-serve common issues without contacting support, with escalation to human agents for unresolved issues.","intents":["I want customers to find answers to common questions without contacting support","I need to reduce inbound ticket volume by enabling self-service resolution","I want to improve customer satisfaction by providing instant answers"],"best_for":["SaaS or service companies with high volume of FAQ-type questions","Organizations with mature knowledge bases or documentation","Teams looking to reduce support costs through customer self-service"],"limitations":["Self-service effectiveness depends on knowledge base quality and discoverability","No indication of how customers are guided to self-service vs escalation","May frustrate customers if suggestions are irrelevant or if escalation path is unclear","Unclear how self-service interactions are tracked or fed back into support analytics"],"requires":["Customer-facing portal or chatbot interface","Knowledge base or FAQ documentation","Integration with helpdesk platform for escalation to human agents"],"input_types":["customer query text (free-form or structured)","customer metadata (optional: account tier, previous interactions)"],"output_types":["suggested solutions (ranked by relevance)","escalation decision (boolean: can self-serve or needs human agent)","ticket creation (if escalated)"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Active support ticket stream (minimum 20-50 tickets/month for meaningful pattern detection)","Helpdesk platform with webhook or API support for ticket ingestion","Historical ticket data (optional but recommended for better routing accuracy)","Existing knowledge base or historical ticket resolution data","Minimum 50-100 documented resolutions for meaningful semantic matching","Integration with helpdesk platform for ticket content access","Integration with helpdesk platform for ticket content and customer context","Optional: historical response examples for fine-tuning or prompt engineering","Optional: knowledge base or policy documents for grounding responses","Historical ticket data with urgency/escalation labels for training"],"failure_modes":["Routing accuracy depends on historical ticket volume and labeling quality—teams with <100 labeled tickets may see poor initial performance","No visibility into how routing rules are learned or customized; likely uses black-box classification","Requires integration with existing helpdesk platform; unclear which systems are supported beyond basic API webhooks","Suggestion quality depends on knowledge base completeness and relevance—sparse or outdated KBs will produce poor suggestions","No indication of how suggestions are ranked or filtered; may surface irrelevant or conflicting solutions without clear confidence scoring","Likely requires manual knowledge base maintenance; no indication of automatic knowledge base generation or updating from resolved tickets","Generated responses may hallucinate or provide inaccurate information if knowledge base is incomplete or outdated","No indication of human review workflows; unclear whether responses are auto-sent or require approval","Risk of tone/brand voice inconsistency if model is not fine-tuned on team's historical responses","May struggle with context-dependent issues requiring customer history or account-specific information","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"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":"2026-05-24T12:16:32.437Z","last_scraped_at":"2026-04-05T13:23:42.560Z","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=parabolic","compare_url":"https://unfragile.ai/compare?artifact=parabolic"}},"signature":"xkIPIA1LLbw/cRY1fvgGGVGNhXcqbJogz44G36Xa1JmvLoKWxzkpiQkq86gc95x7OenbfR1YBGcq3cADV7jQBQ==","signedAt":"2026-06-21T07:52:25.506Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/parabolic","artifact":"https://unfragile.ai/parabolic","verify":"https://unfragile.ai/api/v1/verify?slug=parabolic","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"}}