{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_osher-ai","slug":"osher-ai","name":"Osher.ai","type":"product","url":"https://osher.ai","page_url":"https://unfragile.ai/osher-ai","categories":["automation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_osher-ai__cap_0","uri":"capability://text.generation.language.conversation.aware.customer.support.automation","name":"conversation-aware customer support automation","description":"Automates customer support interactions by analyzing conversation context and intent to generate contextually appropriate responses. The system maintains conversation state across multiple turns, allowing it to understand customer history and provide personalized support without requiring manual ticket routing. It integrates with existing support channels (email, chat, messaging platforms) to intercept and respond to incoming customer inquiries with minimal human intervention.","intents":["I want to automatically respond to common customer support questions without hiring additional support staff","I need to reduce response time for customer inquiries by pre-filtering and auto-responding to routine requests","I want to maintain conversation context across multiple customer interactions to provide personalized support"],"best_for":["small-to-medium businesses with 10-500 monthly support tickets","customer support teams looking to reduce manual response workload","businesses with repetitive customer inquiries (FAQs, account issues, billing questions)"],"limitations":["Requires training data or configuration for domain-specific terminology; generic models may misinterpret industry jargon","Cannot handle complex multi-step issues requiring human judgment or escalation without explicit handoff rules","Response quality degrades when customer inquiries fall outside trained intent categories"],"requires":["Integration with at least one customer communication channel (email, Slack, Discord, web chat, etc.)","Historical customer support data or FAQ documentation for training/configuration","API credentials for connected support platforms"],"input_types":["text (customer messages, emails, chat messages)","structured metadata (customer ID, account status, previous tickets)"],"output_types":["text (auto-generated support responses)","structured data (confidence scores, intent classification, escalation flags)"],"categories":["text-generation-language","customer-support-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_osher-ai__cap_1","uri":"capability://automation.workflow.multi.channel.message.routing.and.triage","name":"multi-channel message routing and triage","description":"Routes incoming customer messages from multiple communication channels (email, chat, social media, messaging apps) to appropriate support queues or automated handlers based on intent, priority, and content analysis. The system classifies messages by urgency, category, and complexity to determine whether they should be auto-responded, queued for human review, or escalated. Integration points connect to popular support platforms and communication tools via APIs or webhooks.","intents":["I want to automatically sort incoming support requests by priority so urgent issues reach humans first","I need to consolidate customer messages from multiple channels into a single triage system","I want to route simple questions to automated responses and complex issues to human agents"],"best_for":["businesses receiving support inquiries across 3+ communication channels","support teams with limited capacity needing intelligent prioritization","organizations wanting to reduce time-to-first-response for urgent issues"],"limitations":["Routing accuracy depends on quality of training data; misclassification can send urgent issues to automation","Requires explicit configuration of routing rules and priority thresholds; no universal defaults work for all industries","Cannot handle ambiguous requests that require clarification before routing"],"requires":["API access to at least 2 communication channels (Slack, email, Discord, Zendesk, Intercom, etc.)","Configuration of routing rules and priority thresholds","Webhook endpoints or polling infrastructure for message ingestion"],"input_types":["text (customer messages)","structured metadata (sender, channel, timestamp, customer history)"],"output_types":["routing decisions (queue assignment, priority level, escalation flag)","structured data (intent classification, confidence scores)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_osher-ai__cap_2","uri":"capability://automation.workflow.workflow.automation.with.conditional.logic.and.state.management","name":"workflow automation with conditional logic and state management","description":"Enables creation of custom automation workflows that execute conditional logic based on customer data, message content, and system state. Workflows are defined through a visual builder or configuration interface that chains together actions (send message, update database, trigger external API, escalate to human) with conditional branches based on customer attributes, intent classification, or external data lookups. State is maintained across workflow steps to enable multi-step automation sequences.","intents":["I want to create a workflow that checks customer account status before auto-responding to billing questions","I need to trigger different responses based on whether a customer is a new user or returning customer","I want to automatically create tickets and notify my team when certain types of issues are detected"],"best_for":["support teams with 5-50 distinct automation scenarios","businesses needing conditional logic beyond simple keyword matching","teams wanting to avoid custom code development for workflow automation"],"limitations":["Visual workflow builders have limited expressiveness compared to code; complex logic may require custom development","State management is typically in-memory or short-lived; workflows cannot reliably maintain state across system restarts","Debugging failed workflows can be difficult without detailed execution logs and error tracking"],"requires":["Access to workflow builder interface (web UI or API)","Integration with data sources (customer database, CRM, external APIs)","Configuration of conditional rules and action mappings"],"input_types":["structured data (customer attributes, message metadata)","text (customer messages for content-based conditions)"],"output_types":["workflow execution results (actions taken, state changes)","structured data (workflow logs, error reports)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_osher-ai__cap_3","uri":"capability://memory.knowledge.customer.context.and.history.retrieval","name":"customer context and history retrieval","description":"Retrieves and surfaces relevant customer history, account information, and previous interactions to inform automated responses and human agent decisions. The system queries connected data sources (CRM, ticketing system, customer database) to fetch customer profile, purchase history, previous support tickets, and account status. Retrieved context is injected into prompt templates or made available to support agents to enable personalized, informed interactions without requiring manual lookup.","intents":["I want automated responses to reference customer purchase history and account status","I need support agents to see customer context automatically without manual database lookups","I want to personalize responses based on customer tier, lifetime value, or previous issues"],"best_for":["businesses with existing CRM or customer database systems","support teams needing to reduce context-switching and manual lookups","organizations wanting to provide personalized support at scale"],"limitations":["Requires integration with customer data sources; data freshness depends on sync frequency","Privacy and compliance concerns when retrieving sensitive customer data; GDPR/CCPA compliance needed","Context retrieval latency adds 200-500ms per request; not suitable for real-time chat with strict latency requirements"],"requires":["API access to customer database, CRM, or ticketing system","Data schema mapping to identify relevant customer attributes","Proper authentication and authorization for data access"],"input_types":["customer identifier (email, ID, phone number)","query parameters (time range, data type filters)"],"output_types":["structured customer data (profile, account status, purchase history)","formatted context for injection into prompts or agent interfaces"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_osher-ai__cap_4","uri":"capability://data.processing.analysis.intent.classification.and.entity.extraction.for.support.queries","name":"intent classification and entity extraction for support queries","description":"Analyzes customer messages to classify intent (billing question, technical issue, account access, product inquiry, complaint) and extract relevant entities (product name, account number, error code, date) using NLP models trained on support-domain data. Classification results inform routing decisions, response selection, and escalation rules. Entity extraction enables structured data capture from unstructured customer messages for downstream processing and ticket creation.","intents":["I want to automatically categorize incoming support requests by type (billing, technical, account, etc.)","I need to extract key information from customer messages (order number, error code) for ticket creation","I want to detect complaint or escalation signals in customer messages to prioritize urgent issues"],"best_for":["support teams receiving diverse inquiry types requiring intelligent categorization","businesses needing structured data extraction from unstructured customer messages","organizations wanting to reduce manual ticket tagging and categorization"],"limitations":["Classification accuracy varies by domain; models trained on general support data may misclassify industry-specific issues","Entity extraction is brittle for non-standard formats; customer messages with typos or unusual phrasing reduce accuracy","Requires labeled training data or fine-tuning for high accuracy; out-of-the-box models may have 70-80% accuracy"],"requires":["Support message data for model training or fine-tuning (optional but recommended)","Configuration of intent categories and entity types relevant to business domain"],"input_types":["text (customer messages, emails, chat messages)"],"output_types":["structured data (intent classification, confidence scores, extracted entities)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_osher-ai__cap_5","uri":"capability://automation.workflow.escalation.and.handoff.to.human.agents","name":"escalation and handoff to human agents","description":"Manages escalation of complex or sensitive customer issues from automated handling to human support agents. The system detects escalation triggers (confidence threshold, intent type, customer sentiment, explicit escalation request) and routes conversations to available agents with full context. Handoff includes conversation history, customer information, and classification results to enable seamless agent takeover without requiring customers to repeat information.","intents":["I want automated responses to detect when an issue is too complex and route to a human agent","I need to escalate angry or frustrated customers to senior support staff","I want to ensure customers never repeat information when handed off from automation to a human"],"best_for":["support teams using automation for first-line response but needing human escalation paths","businesses with tiered support (junior agents, senior agents, specialists)","organizations wanting to improve customer satisfaction by reducing customer effort"],"limitations":["Escalation detection is imperfect; some issues will be incorrectly escalated (false positives) or missed (false negatives)","Requires integration with agent assignment system; no built-in load balancing or queue management","Handoff latency can be high if agents are unavailable; no automatic fallback to alternative channels"],"requires":["Integration with support team communication system (Slack, email, ticketing system)","Agent availability tracking or queue management system","Configuration of escalation rules and trigger thresholds"],"input_types":["conversation history (messages, metadata)","customer data (account status, previous interactions)","classification results (intent, confidence, sentiment)"],"output_types":["escalation decision (escalate/handle, priority level, assigned agent)","formatted handoff context (conversation summary, customer info, action items)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_osher-ai__cap_6","uri":"capability://text.generation.language.response.generation.with.template.and.knowledge.base.integration","name":"response generation with template and knowledge base integration","description":"Generates contextually appropriate customer support responses by combining LLM-based text generation with retrieval from knowledge bases, FAQ databases, and response templates. The system retrieves relevant knowledge base articles or pre-approved response templates based on intent classification, then uses LLM to personalize and adapt the response to the specific customer context. Generated responses are validated against safety guidelines before sending.","intents":["I want to auto-generate responses to common questions using my FAQ and knowledge base","I need to ensure responses are consistent with company policy and brand voice","I want to personalize template responses with customer-specific information"],"best_for":["support teams with existing knowledge bases or FAQ systems","businesses needing consistent, on-brand responses at scale","organizations wanting to reduce response time while maintaining quality"],"limitations":["Response quality depends on knowledge base quality; incomplete or outdated knowledge bases produce poor responses","LLM-generated responses may hallucinate or introduce inaccuracies not present in source material","Requires manual review and approval workflows to ensure responses meet quality standards before sending"],"requires":["Knowledge base or FAQ system with searchable content","Response templates or approved response patterns","Integration with LLM API (OpenAI, Anthropic, or self-hosted model)"],"input_types":["customer message (text)","customer context (profile, history)","classification results (intent, entities)"],"output_types":["generated response text","confidence score and source attribution (which knowledge base articles were used)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_osher-ai__cap_7","uri":"capability://data.processing.analysis.sentiment.analysis.and.customer.emotion.detection","name":"sentiment analysis and customer emotion detection","description":"Analyzes customer messages to detect emotional tone, frustration level, and sentiment (positive, negative, neutral) to inform response strategy and escalation decisions. The system classifies sentiment at message and conversation level, tracking sentiment trends across multiple interactions. Detected sentiment triggers different response templates (empathetic tone for frustrated customers, celebratory tone for positive feedback) and escalation rules (immediate escalation for highly frustrated customers).","intents":["I want to detect angry or frustrated customers and escalate them to senior support staff","I need to respond with appropriate tone based on customer emotion","I want to track customer satisfaction trends across support interactions"],"best_for":["support teams wanting to improve customer satisfaction through emotion-aware responses","businesses needing to identify at-risk customers for proactive retention","organizations tracking support quality metrics including customer sentiment"],"limitations":["Sentiment detection is imperfect for sarcasm, cultural context, and mixed emotions; accuracy typically 75-85%","Requires sufficient message length for accurate detection; single-word or very short messages are unreliable","Cannot detect emotion from non-text channels (tone of voice, facial expressions) without additional input"],"requires":["Text input from customer messages"],"input_types":["text (customer messages, emails, chat messages)"],"output_types":["sentiment classification (positive, negative, neutral)","emotion scores (frustration, anger, satisfaction)","trend analysis (sentiment over time)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_osher-ai__cap_8","uri":"capability://data.processing.analysis.analytics.and.performance.metrics.dashboard","name":"analytics and performance metrics dashboard","description":"Provides visibility into automation performance through dashboards and reports tracking key support metrics: automation rate (% of issues handled without human intervention), response time, customer satisfaction, escalation rate, and cost savings. The system aggregates data from support interactions, automation logs, and customer feedback to calculate metrics and identify trends. Dashboards enable support managers to monitor automation effectiveness and identify areas for improvement.","intents":["I want to see what percentage of support requests are being handled by automation","I need to track response time improvements from automation","I want to measure ROI and cost savings from implementing support automation"],"best_for":["support managers and business leaders evaluating automation ROI","teams wanting data-driven insights into automation performance","organizations tracking customer satisfaction and support quality metrics"],"limitations":["Metrics are only as accurate as underlying data; garbage in, garbage out","Attribution is difficult for multi-step workflows; unclear which automation step drove outcomes","Real-time dashboards require continuous data ingestion; batch reporting has inherent latency"],"requires":["Integration with support system to collect interaction data","Customer feedback mechanism (surveys, ratings) for satisfaction metrics","Access to cost data (agent salaries, software costs) for ROI calculation"],"input_types":["structured data (support metrics, automation logs, customer feedback)"],"output_types":["dashboard visualizations (charts, tables, KPI cards)","reports (PDF, CSV exports)","alerts (threshold-based notifications for anomalies)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_osher-ai__cap_9","uri":"capability://tool.use.integration.integration.with.popular.support.and.communication.platforms","name":"integration with popular support and communication platforms","description":"Provides native integrations with widely-used support and communication tools (Zendesk, Intercom, Slack, Discord, email, WhatsApp, etc.) via pre-built connectors that handle authentication, message ingestion, and response delivery. Integrations use platform-specific APIs and webhooks to enable real-time message processing and response delivery without requiring custom development. The system abstracts platform differences to provide a unified interface for automation across multiple channels.","intents":["I want to connect my existing support system without rebuilding my workflow","I need automation to work across multiple communication channels simultaneously","I want to avoid custom API integration work and use pre-built connectors"],"best_for":["businesses already using popular support platforms (Zendesk, Intercom, etc.)","teams wanting to add automation without replacing existing systems","organizations using multiple communication channels needing unified automation"],"limitations":["Limited to supported platforms; custom or niche systems require custom development","Integration quality varies by platform; some platforms have rate limits or API restrictions","Requires API credentials and proper authentication setup; misconfiguration can break integrations"],"requires":["Account with supported platform (Zendesk, Intercom, Slack, Discord, etc.)","API credentials or OAuth tokens for authentication","Proper webhook configuration for real-time message delivery"],"input_types":["platform-specific message formats (Slack messages, Zendesk tickets, email, etc.)"],"output_types":["platform-specific response formats (Slack messages, Zendesk comments, email replies, etc.)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Integration with at least one customer communication channel (email, Slack, Discord, web chat, etc.)","Historical customer support data or FAQ documentation for training/configuration","API credentials for connected support platforms","API access to at least 2 communication channels (Slack, email, Discord, Zendesk, Intercom, etc.)","Configuration of routing rules and priority thresholds","Webhook endpoints or polling infrastructure for message ingestion","Access to workflow builder interface (web UI or API)","Integration with data sources (customer database, CRM, external APIs)","Configuration of conditional rules and action mappings","API access to customer database, CRM, or ticketing system"],"failure_modes":["Requires training data or configuration for domain-specific terminology; generic models may misinterpret industry jargon","Cannot handle complex multi-step issues requiring human judgment or escalation without explicit handoff rules","Response quality degrades when customer inquiries fall outside trained intent categories","Routing accuracy depends on quality of training data; misclassification can send urgent issues to automation","Requires explicit configuration of routing rules and priority thresholds; no universal defaults work for all industries","Cannot handle ambiguous requests that require clarification before routing","Visual workflow builders have limited expressiveness compared to code; complex logic may require custom development","State management is typically in-memory or short-lived; workflows cannot reliably maintain state across system restarts","Debugging failed workflows can be difficult without detailed execution logs and error tracking","Requires integration with customer data sources; data freshness depends on sync frequency","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"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":"2026-05-24T12:16:32.436Z","last_scraped_at":"2026-04-05T13:23:42.551Z","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=osher-ai","compare_url":"https://unfragile.ai/compare?artifact=osher-ai"}},"signature":"vfsDcw5Ql8X4nJgwR+miALnMgnAky+eQ2Bm3Z9wlMAPsY8pgFZZ27+//Nzchh4wddoQG9SErdRe/OZU1W7iECw==","signedAt":"2026-06-21T05:02:24.620Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/osher-ai","artifact":"https://unfragile.ai/osher-ai","verify":"https://unfragile.ai/api/v1/verify?slug=osher-ai","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"}}