{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_hoory","slug":"hoory","name":"Hoory","type":"product","url":"https://producthunt.hoory.com","page_url":"https://unfragile.ai/hoory","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_hoory__cap_0","uri":"capability://planning.reasoning.intelligent.ticket.classification.and.routing","name":"intelligent-ticket-classification-and-routing","description":"Automatically categorizes incoming customer support inquiries using NLP-based intent detection and routes them to appropriate support channels, teams, or automated response handlers based on learned patterns from historical ticket data. The system learns from existing support workflows rather than imposing rigid category schemas, enabling it to adapt to domain-specific terminology and business processes without manual configuration.","intents":["I need to automatically sort incoming support tickets by type so high-priority issues reach the right team first","I want to reduce manual ticket triage time by having the system pre-categorize inquiries before human review","I need to route common questions to automated responses while escalating complex issues to support staff"],"best_for":["ecommerce and SaaS startups with growing ticket volume but limited support staff","teams managing support across multiple channels (email, chat, forms) needing unified routing","businesses with domain-specific support categories that generic tools don't understand"],"limitations":["accuracy depends on historical ticket volume and labeling quality — new businesses with <100 tickets may see misclassification rates >15%","no explicit control over routing rules — routing is learned rather than rule-based, making it difficult to enforce hard constraints","multi-language support quality unknown; likely performs better on English tickets than non-English inquiries"],"requires":["existing support ticket history or sample tickets for training","integration with current support platform (email, ticketing system, or form submission endpoint)","API credentials for destination systems (support queues, automation handlers)"],"input_types":["plain text (email body, chat message, form submission)","structured metadata (customer tier, account history, previous interactions)"],"output_types":["routing decision with confidence score","suggested category label","priority level assignment"],"categories":["planning-reasoning","customer-support-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hoory__cap_1","uri":"capability://text.generation.language.ai.generated.support.responses.with.context.awareness","name":"ai-generated-support-responses-with-context-awareness","description":"Generates contextually relevant support responses to customer inquiries by combining the customer's question with historical ticket context, product knowledge, and company-specific support tone/guidelines. Uses retrieval-augmented generation (RAG) to pull relevant past resolutions and knowledge base articles, then synthesizes responses that maintain consistency with existing support quality standards while reducing response time from hours to seconds.","intents":["I want to automatically respond to common support questions without waiting for a human agent","I need to ensure AI-generated responses match our brand voice and support quality standards","I want to reduce response time for straightforward inquiries while keeping complex issues for human review"],"best_for":["small-to-mid-size SaaS and ecommerce teams handling high-volume repetitive inquiries","businesses operating across multiple time zones needing 24/7 response capability","support teams with limited staff but consistent support processes they want to scale"],"limitations":["no publicly disclosed hallucination detection or confidence scoring — risk of generating plausible-sounding but incorrect product information","context window limitations may prevent full ticket history inclusion for customers with long interaction histories","requires manual review workflow to catch errors before customer delivery; no built-in quality gates or automated fact-checking against product documentation","tone/style customization likely limited to prompt engineering rather than fine-tuning, reducing control over response personality"],"requires":["knowledge base or historical ticket database with resolved issues","API integration with support platform to fetch customer context and ticket history","optional: company style guide or tone guidelines provided as system prompt","human review queue for generated responses before sending (recommended)"],"input_types":["customer inquiry text","customer account/history metadata","product documentation or knowledge base articles","previous ticket resolutions"],"output_types":["generated response text","confidence score (if available)","suggested follow-up actions"],"categories":["text-generation-language","customer-support-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hoory__cap_2","uri":"capability://tool.use.integration.multi.channel.support.aggregation.and.normalization","name":"multi-channel-support-aggregation-and-normalization","description":"Unifies customer inquiries from multiple sources (email, web forms, chat, social media) into a single normalized ticket format that can be processed by routing and response generation systems. Handles protocol-specific parsing (SMTP headers, webhook payloads, API responses) and normalizes customer identity across channels, enabling consistent support experience regardless of inquiry source.","intents":["I receive support inquiries across email, chat, and web forms and need to manage them in one place","I want to track the same customer across multiple support channels without duplicate tickets","I need to ensure inquiries from all channels go through the same AI routing and response pipeline"],"best_for":["ecommerce businesses receiving inquiries via email, chat, and marketplace messaging","SaaS companies with support across multiple channels (in-app chat, email, community forums)","teams that have grown organically and accumulated support channels without centralized management"],"limitations":["integration breadth unknown — likely supports major platforms (Gmail, Slack, Zendesk) but may lack connectors for niche or legacy systems","customer identity matching relies on email/phone matching; no sophisticated identity resolution for customers using different email addresses across channels","rate limiting on source APIs may cause delays in aggregating high-volume inquiries from multiple channels simultaneously","no built-in deduplication for cross-channel inquiries (e.g., customer emails support AND posts in community forum about same issue)"],"requires":["API credentials for each support channel (email provider, chat platform, form service, etc.)","customer identity field (email or phone) consistent across channels for deduplication","webhook or polling infrastructure to continuously fetch new inquiries from sources"],"input_types":["email messages (SMTP/IMAP)","webhook payloads from chat platforms","form submissions (HTTP POST)","API responses from support platforms"],"output_types":["normalized ticket object with standardized fields","customer identity record","channel source metadata"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hoory__cap_3","uri":"capability://safety.moderation.sentiment.analysis.and.escalation.triggering","name":"sentiment-analysis-and-escalation-triggering","description":"Analyzes customer inquiry text and metadata to detect emotional tone (frustration, urgency, satisfaction) and automatically escalates tickets to human agents when sentiment crosses predefined thresholds or specific keywords indicate critical issues. Uses NLP-based sentiment classification combined with rule-based triggers to identify high-priority situations that require immediate human intervention rather than automated response.","intents":["I want to catch angry or frustrated customers before they escalate further and ensure they get human support","I need to identify urgent issues (outages, security concerns) that shouldn't be handled by AI","I want to prevent AI responses from being sent to customers in critical situations"],"best_for":["support teams handling sensitive customer issues where wrong responses cause damage","businesses with SLAs requiring human response to high-priority tickets within specific timeframes","teams wanting to use AI for volume but maintain human oversight for risky situations"],"limitations":["sentiment detection accuracy varies by language and cultural context; may misclassify sarcasm or formal tone as negative","threshold tuning requires manual iteration — no guidance on optimal escalation sensitivity for different business types","keyword-based escalation rules are brittle and require maintenance as new issue types emerge","no context about customer lifetime value or account tier — escalates based on sentiment alone, potentially over-escalating low-value customers"],"requires":["definition of escalation thresholds (sentiment score cutoffs, keyword lists)","human support queue or escalation endpoint to route flagged tickets","optional: customer account data to enable tier-based escalation rules"],"input_types":["customer inquiry text","customer metadata (account age, tier, previous interactions)","optional: conversation history for context"],"output_types":["sentiment score (0-1 or categorical)","escalation decision (boolean)","escalation reason/justification","suggested priority level"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hoory__cap_4","uri":"capability://text.generation.language.multi.language.support.with.automatic.translation","name":"multi-language-support-with-automatic-translation","description":"Detects customer inquiry language and automatically translates inquiries to support team's primary language for processing, then translates generated responses back to customer's original language before delivery. Enables support teams to handle global customers without requiring multilingual staff, using neural machine translation (NMT) integrated into the request/response pipeline.","intents":["I have customers in multiple countries but my support team only speaks English","I want to provide 24/7 support globally without hiring multilingual staff","I need to maintain consistent support quality across different language customer bases"],"best_for":["ecommerce and SaaS companies with international customer bases","startups scaling globally but with limited budget for multilingual support hiring","teams wanting to test international markets without upfront localization investment"],"limitations":["translation quality varies significantly by language pair — European languages likely better supported than Asian or low-resource languages","context-specific terminology may be mistranslated (e.g., product names, technical jargon); requires domain-specific translation models or manual glossaries","cultural nuances in support tone may be lost in translation, potentially causing customer dissatisfaction despite technically correct responses","no explicit support for regional dialects or colloquialisms — may struggle with informal language or slang","latency impact from translation adds ~500-1000ms per request, potentially noticeable in real-time chat scenarios"],"requires":["language detection model (likely built-in)","neural machine translation API or model (likely OpenAI, Google Translate, or similar)","API credentials for translation service","optional: custom glossaries or terminology databases for domain-specific accuracy"],"input_types":["customer inquiry text in any supported language","optional: language hint or explicit language specification"],"output_types":["detected language code","translated inquiry text","translated response text","optional: confidence score for translation quality"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hoory__cap_5","uri":"capability://memory.knowledge.knowledge.base.integration.and.auto.linking","name":"knowledge-base-integration-and-auto-linking","description":"Automatically identifies relevant knowledge base articles, documentation, or FAQ entries related to customer inquiries and includes them in generated responses or suggests them to support agents. Uses semantic similarity matching (embeddings-based retrieval) to find related content without requiring explicit keyword matching, enabling customers to self-serve and reducing support load for common questions.","intents":["I want to point customers to relevant documentation instead of explaining the same thing repeatedly","I need to reduce support volume by helping customers find answers themselves","I want to ensure generated responses include links to authoritative documentation"],"best_for":["SaaS companies with extensive product documentation and API references","ecommerce businesses with FAQ sections covering common questions","support teams wanting to reduce volume by improving self-service discoverability"],"limitations":["requires existing knowledge base or documentation — no value for businesses without documented support content","semantic matching may surface tangentially related articles rather than directly relevant ones, requiring human curation","knowledge base must be kept current; outdated documentation will be suggested and damage credibility","no built-in freshness scoring — old articles may rank higher than recently updated ones","embedding-based retrieval adds latency (~200-500ms) to response generation pipeline"],"requires":["existing knowledge base, FAQ, or documentation in accessible format (web pages, markdown, API)","knowledge base indexing/embedding infrastructure (likely built-in but requires initial setup)","API access to knowledge base content or web scraping capability","optional: manual curation or relevance feedback to improve matching quality"],"input_types":["customer inquiry text","knowledge base content (articles, FAQs, documentation pages)"],"output_types":["ranked list of relevant articles with similarity scores","suggested links to include in response","optional: article excerpts for inline inclusion"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hoory__cap_6","uri":"capability://memory.knowledge.conversation.history.context.management","name":"conversation-history-context-management","description":"Maintains and retrieves conversation history for each customer across support interactions, enabling AI systems to understand context from previous exchanges and provide coherent multi-turn support conversations. Implements context windowing to fit relevant history within LLM token limits while prioritizing recent and semantically important exchanges, preventing context loss while managing computational costs.","intents":["I want AI responses to reference previous interactions so customers don't have to repeat themselves","I need to maintain conversation coherence across multiple support exchanges","I want to avoid AI responses that contradict previous support decisions or information"],"best_for":["support scenarios requiring multi-turn conversations (troubleshooting, onboarding, complex issues)","businesses with high customer lifetime value where context matters for satisfaction","teams wanting to provide personalized support that references customer history"],"limitations":["context window limits prevent including full conversation history for long-term customers — requires intelligent summarization or pruning","no explicit mechanism for handling contradictory information across conversation history","storage and retrieval latency for large conversation histories may impact response time","privacy concerns with storing full conversation history — requires compliance with data retention policies","no built-in deduplication or compression of repetitive exchanges"],"requires":["persistent storage for conversation history (database, vector store, or file system)","customer identity tracking to associate conversations with specific customers","context retrieval mechanism (likely semantic search or recency-based ranking)","optional: conversation summarization to compress history within token limits"],"input_types":["new customer inquiry","customer identity","optional: time range or conversation ID for filtering"],"output_types":["relevant conversation history (full or summarized)","context metadata (timestamps, participants, resolution status)","optional: summary of key points from history"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hoory__cap_7","uri":"capability://data.processing.analysis.performance.analytics.and.automation.quality.monitoring","name":"performance-analytics-and-automation-quality-monitoring","description":"Tracks metrics on AI-generated responses and automated routing decisions (response time, customer satisfaction, escalation rates, resolution rates) and provides dashboards showing automation effectiveness. Enables identification of failure patterns (e.g., specific inquiry types where AI performs poorly) and supports A/B testing of different response generation strategies or routing rules.","intents":["I want to measure whether AI automation is actually reducing support costs and improving response times","I need to identify which types of inquiries the AI handles well vs. poorly","I want to track customer satisfaction with AI-generated responses vs. human responses"],"best_for":["teams making business decisions about automation investment and ROI","support managers wanting data-driven insights into automation effectiveness","organizations with mature support operations wanting to optimize automation quality"],"limitations":["satisfaction metrics depend on customer feedback collection — no automatic satisfaction detection without explicit surveys","attribution challenges: difficult to isolate impact of AI automation from other factors (team quality, product changes, customer base shifts)","no built-in benchmarking against industry standards — metrics are relative to own baseline","dashboard likely provides high-level metrics but may lack drill-down capability for detailed failure analysis","lag in data availability — real-time metrics may not be available, limiting rapid iteration"],"requires":["integration with support platform to capture ticket metadata and outcomes","optional: customer satisfaction survey integration (CSAT, NPS, or custom surveys)","optional: human review feedback to label AI response quality","sufficient historical data (weeks to months) to establish baseline and detect trends"],"input_types":["ticket metadata (routing decision, response time, resolution status)","customer feedback (satisfaction ratings, follow-up inquiries)","human review feedback (response quality assessment)"],"output_types":["dashboard with key metrics (response time, resolution rate, escalation rate, satisfaction)","trend analysis and anomaly detection","failure pattern identification","optional: A/B test results and recommendations"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hoory__cap_8","uri":"capability://automation.workflow.human.in.the.loop.review.and.override.workflow","name":"human-in-the-loop-review-and-override-workflow","description":"Implements a review queue where support agents can inspect AI-generated responses before sending to customers, approve/reject/edit responses, and provide feedback that improves future AI performance. Enables gradual automation adoption by allowing teams to maintain quality control while building confidence in AI capabilities, with optional auto-approval for high-confidence responses.","intents":["I want to use AI to draft responses but maintain human oversight before sending","I need to catch AI errors before they reach customers","I want to gradually increase automation as I build confidence in AI quality"],"best_for":["support teams new to AI automation wanting to maintain quality control","businesses with high customer expectations where errors are costly","organizations wanting to use AI as productivity tool for agents rather than full replacement"],"limitations":["review workflow adds latency — responses take longer to send than fully automated approach","requires human attention and decision-making, limiting scalability benefits of automation","no built-in guidance on when to approve vs. reject — agents must develop judgment through experience","feedback from reviews likely not automatically incorporated into model retraining — requires manual analysis to improve future responses","review queue may become bottleneck if volume exceeds agent capacity"],"requires":["support agent access to review interface","queue management system to route responses for review","optional: confidence scoring to enable auto-approval for high-confidence responses","optional: feedback collection mechanism to capture agent edits and rejections"],"input_types":["AI-generated response text","customer inquiry and context","optional: confidence score or quality metrics"],"output_types":["agent decision (approve/reject/edit)","edited response text (if modified)","feedback/reason for rejection","optional: quality assessment for model improvement"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hoory__cap_9","uri":"capability://text.generation.language.customizable.response.templates.and.tone.guidelines","name":"customizable-response-templates-and-tone-guidelines","description":"Allows support teams to define response templates, tone guidelines, and company-specific language preferences that constrain AI response generation to match brand voice and support standards. Implements template-guided generation where AI fills in variable sections while maintaining overall structure and tone, reducing hallucination risk and ensuring consistency across responses.","intents":["I want AI responses to sound like my support team, not generic LLM output","I need to ensure responses follow company policies and legal requirements","I want to standardize response quality across different support agents and AI systems"],"best_for":["brands with strong voice and tone requirements (luxury, casual, formal, etc.)","regulated industries requiring specific language in support responses","teams wanting to maintain consistency across human and AI responses"],"limitations":["template-based generation may produce stilted or repetitive responses if templates are too rigid","customization likely limited to prompt engineering rather than fine-tuning, reducing control over response personality","no automatic validation that generated responses comply with guidelines — requires manual review","templates must be maintained and updated as company policies or tone preferences change","over-constraining with templates may reduce AI's ability to handle novel or edge-case inquiries"],"requires":["definition of response templates with variable placeholders","tone guidelines or brand voice documentation","optional: example responses demonstrating desired style","optional: policy or legal language requirements"],"input_types":["response template with placeholders","tone guidelines (text description or examples)","customer inquiry and context to fill template variables"],"output_types":["generated response matching template structure and tone","optional: tone compliance score"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["existing support ticket history or sample tickets for training","integration with current support platform (email, ticketing system, or form submission endpoint)","API credentials for destination systems (support queues, automation handlers)","knowledge base or historical ticket database with resolved issues","API integration with support platform to fetch customer context and ticket history","optional: company style guide or tone guidelines provided as system prompt","human review queue for generated responses before sending (recommended)","API credentials for each support channel (email provider, chat platform, form service, etc.)","customer identity field (email or phone) consistent across channels for deduplication","webhook or polling infrastructure to continuously fetch new inquiries from sources"],"failure_modes":["accuracy depends on historical ticket volume and labeling quality — new businesses with <100 tickets may see misclassification rates >15%","no explicit control over routing rules — routing is learned rather than rule-based, making it difficult to enforce hard constraints","multi-language support quality unknown; likely performs better on English tickets than non-English inquiries","no publicly disclosed hallucination detection or confidence scoring — risk of generating plausible-sounding but incorrect product information","context window limitations may prevent full ticket history inclusion for customers with long interaction histories","requires manual review workflow to catch errors before customer delivery; no built-in quality gates or automated fact-checking against product documentation","tone/style customization likely limited to prompt engineering rather than fine-tuning, reducing control over response personality","integration breadth unknown — likely supports major platforms (Gmail, Slack, Zendesk) but may lack connectors for niche or legacy systems","customer identity matching relies on email/phone matching; no sophisticated identity resolution for customers using different email addresses across channels","rate limiting on source APIs may cause delays in aggregating high-volume inquiries from multiple channels simultaneously","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":"2026-05-24T12:16:30.893Z","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=hoory","compare_url":"https://unfragile.ai/compare?artifact=hoory"}},"signature":"a8usPCvr1XDVU9T4GRN/dw5bdrOlgG3sCf/BFt7MJW+CGPh/HnWjdURFlCppXNPPZ45PYdWNuJaQAo6QZYThBQ==","signedAt":"2026-06-20T19:34:11.715Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hoory","artifact":"https://unfragile.ai/hoory","verify":"https://unfragile.ai/api/v1/verify?slug=hoory","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"}}