{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_supportlogic","slug":"supportlogic","name":"Supportlogic","type":"product","url":"https://www.supportlogic.com","page_url":"https://unfragile.ai/supportlogic","categories":["data-analysis","data-pipelines"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_supportlogic__cap_0","uri":"capability://customer.support.multi.channel.sentiment.analysis","name":"multi-channel sentiment analysis","description":"Analyzes customer sentiment across multiple support channels (chat, email, tickets) simultaneously to detect emotional tone and satisfaction levels. Uses AI to identify sentiment signals that may be missed by human review.","intents":["I want to understand how customers feel across all our support interactions","I need to detect negative sentiment patterns in our support data","I want to identify frustrated customers before they churn"],"best_for":["support teams","customer success managers","SaaS companies with high support volume"],"limitations":["works better for general SaaS language than highly technical or niche industry jargon","requires sufficient volume of interactions to be effective"],"requires":["integration with support platform","historical or ongoing support conversation data","multiple support channels"],"input_types":["chat transcripts","email threads","support tickets"],"output_types":["sentiment scores","emotional tone classifications"],"categories":["customer-support","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_supportlogic__cap_1","uri":"capability://customer.support.escalation.risk.prediction","name":"escalation risk prediction","description":"Predicts which customer interactions are likely to escalate based on sentiment signals and conversation patterns. Identifies at-risk customers before they become critical issues or churn.","intents":["I want to know which customers are at risk of escalating their issues","I need to prevent support tickets from becoming major problems","I want to identify customers likely to churn based on their support interactions"],"best_for":["support teams","customer retention specialists","mid-market to enterprise SaaS companies"],"limitations":["prediction accuracy depends on historical data volume","may miss escalation signals in niche industries with unique language patterns"],"requires":["sufficient historical support interaction data","integration with support platform","ongoing conversation monitoring"],"input_types":["support conversation history","customer interaction patterns","sentiment signals"],"output_types":["escalation risk scores","risk classifications","probability percentages"],"categories":["customer-support","predictive-analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_supportlogic__cap_2","uri":"capability://customer.support.real.time.escalation.alerts","name":"real-time escalation alerts","description":"Generates immediate alerts when high-risk escalation signals are detected in active customer conversations. Routes alerts to appropriate agents or teams for proactive intervention.","intents":["I want to be notified immediately when a customer is becoming frustrated","I need to intervene in conversations before they escalate","I want alerts routed to the right team member who can help"],"best_for":["support agents","support managers","customer success teams"],"limitations":["alert fatigue possible if thresholds not properly tuned","effectiveness depends on team responsiveness"],"requires":["real-time integration with support platform","configured alert routing rules","team availability for intervention"],"input_types":["live support conversations","sentiment analysis results"],"output_types":["alert notifications","risk indicators","recommended actions"],"categories":["customer-support","notifications"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_supportlogic__cap_3","uri":"capability://integrations.support.platform.integration","name":"support platform integration","description":"Connects directly to major support platforms (Zendesk, Salesforce, Intercom) to access and analyze customer support data without manual exports or workarounds. Maintains ongoing data synchronization.","intents":["I want to use Supportlogic without exporting data manually","I need my support platform data to automatically feed into sentiment analysis","I want seamless integration with my existing support tools"],"best_for":["support teams using Zendesk, Salesforce, or Intercom","companies wanting minimal implementation friction"],"limitations":["limited to supported platforms","integration quality may vary by platform"],"requires":["active account on supported support platform","API access credentials","proper permissions configuration"],"input_types":["support platform APIs","conversation data","customer metadata"],"output_types":["synchronized data","integrated alerts","unified analytics"],"categories":["integrations","customer-support"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_supportlogic__cap_4","uri":"capability://customer.support.churn.risk.identification","name":"churn risk identification","description":"Identifies customers at high risk of churning based on sentiment deterioration, support interaction patterns, and escalation signals. Flags accounts that need retention intervention.","intents":["I want to identify customers likely to cancel their subscription","I need to know which accounts are at risk of churn","I want to prioritize retention efforts on high-risk customers"],"best_for":["customer success teams","account managers","SaaS companies with recurring revenue"],"limitations":["prediction accuracy varies by industry and customer segment","requires sufficient interaction history"],"requires":["historical support data","customer subscription/account data","sentiment analysis results"],"input_types":["support conversations","customer interaction history","account metadata"],"output_types":["churn risk scores","at-risk customer lists","risk segments"],"categories":["customer-support","retention"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_supportlogic__cap_5","uri":"capability://analytics.customer.sentiment.signal.extraction","name":"customer sentiment signal extraction","description":"Extracts specific sentiment signals and emotional indicators from unstructured support conversations. Identifies frustration, satisfaction, confusion, and other emotional states embedded in customer language.","intents":["I want to understand what emotions customers are expressing in their support tickets","I need to extract key sentiment indicators from conversations","I want to measure customer satisfaction from their own words"],"best_for":["support analysts","customer insights teams","product teams"],"limitations":["may miss sarcasm or context-dependent sentiment","less effective with highly technical or specialized language"],"requires":["support conversation text","nlp processing capability"],"input_types":["text conversations","chat logs","email messages"],"output_types":["sentiment signals","emotional indicators","signal confidence scores"],"categories":["analytics","customer-support"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_supportlogic__cap_6","uri":"capability://customer.support.proactive.intervention.routing","name":"proactive intervention routing","description":"Routes escalation alerts and intervention recommendations to the appropriate agent, team, or manager based on configured rules and customer context. Ensures the right person handles at-risk situations.","intents":["I want alerts sent to the right person who can help","I need escalations routed based on customer value or issue type","I want to ensure high-risk customers get priority attention"],"best_for":["support managers","team leads","support operations"],"limitations":["effectiveness depends on routing rule configuration","requires clear escalation policies"],"requires":["configured routing rules","team member availability data","customer context information"],"input_types":["escalation alerts","customer data","team assignments"],"output_types":["routed notifications","assigned tasks","escalation queues"],"categories":["customer-support","workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_supportlogic__cap_7","uri":"capability://analytics.revenue.protection.analysis","name":"revenue protection analysis","description":"Analyzes support interactions to identify revenue risks, including churn signals, expansion opportunities, and at-risk accounts. Connects customer sentiment to financial impact.","intents":["I want to understand how support issues impact our revenue","I need to identify which customers represent the biggest churn risk to our MRR","I want to see the financial impact of escalations and dissatisfaction"],"best_for":["finance teams","executive leadership","customer success leaders","SaaS companies with recurring revenue"],"limitations":["requires integration of support data with financial data","impact calculations depend on data quality"],"requires":["support interaction data","customer account value data","subscription/revenue information"],"input_types":["support conversations","customer account data","revenue data"],"output_types":["revenue risk scores","financial impact estimates","at-risk revenue reports"],"categories":["analytics","business-intelligence"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["integration with support platform","historical or ongoing support conversation data","multiple support channels","sufficient historical support interaction data","ongoing conversation monitoring","real-time integration with support platform","configured alert routing rules","team availability for intervention","active account on supported support platform","API access credentials"],"failure_modes":["works better for general SaaS language than highly technical or niche industry jargon","requires sufficient volume of interactions to be effective","prediction accuracy depends on historical data volume","may miss escalation signals in niche industries with unique language patterns","alert fatigue possible if thresholds not properly tuned","effectiveness depends on team responsiveness","limited to supported platforms","integration quality may vary by platform","prediction accuracy varies by industry and customer segment","requires sufficient interaction history","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.39999999999999997,"quality":0.77,"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":"2026-05-24T12:16:33.648Z","last_scraped_at":"2026-04-05T13:23:42.541Z","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=supportlogic","compare_url":"https://unfragile.ai/compare?artifact=supportlogic"}},"signature":"z894mDeReLsZNuTpvjsDGO2F6IZJW7ORGaoNQh4fcOGvCRBMDNgDicv8seRthLtuJh3WKsduaJ+sFQZ15eetBA==","signedAt":"2026-06-19T13:07:09.246Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/supportlogic","artifact":"https://unfragile.ai/supportlogic","verify":"https://unfragile.ai/api/v1/verify?slug=supportlogic","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"}}