{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_smitty","slug":"smitty","name":"Smitty","type":"product","url":"https://www.smitty.ai","page_url":"https://unfragile.ai/smitty","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_smitty__cap_0","uri":"capability://tool.use.integration.multi.channel.conversation.routing.and.aggregation","name":"multi-channel conversation routing and aggregation","description":"Centralizes incoming conversations from web chat widgets, email, and messaging platforms (SMS, WhatsApp, Messenger) into a unified inbox, automatically routing messages to appropriate handlers based on channel origin and conversation state. Uses a message queue architecture to normalize payloads across heterogeneous channel APIs and maintain conversation continuity across platform boundaries.","intents":["I want to handle customer inquiries from multiple platforms without switching between separate dashboards","I need to ensure a customer's conversation history persists when they switch from web chat to email","I want to automatically assign incoming messages to the right team member based on channel and content"],"best_for":["small support teams managing 3-5 communication channels simultaneously","businesses transitioning from manual email/chat management to unified inbox","founders needing quick multi-channel presence without custom integration work"],"limitations":["No native support for custom channel integrations beyond pre-built connectors — extending to proprietary platforms requires API requests","Conversation context is limited to current session; no cross-session learning or pattern recognition across channels","Rate limiting on channel APIs may cause message delays during traffic spikes if queue processing isn't scaled"],"requires":["Active accounts on at least one messaging platform (web, email, SMS, WhatsApp, or Messenger)","API credentials for each channel to be connected","Smitty workspace with admin permissions to configure routing rules"],"input_types":["text messages","email threads","structured metadata from channel APIs (sender ID, timestamp, channel type)"],"output_types":["unified conversation thread","routed message with channel metadata","agent assignment notification"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_smitty__cap_1","uri":"capability://text.generation.language.intent.based.chatbot.response.generation.with.template.fallback","name":"intent-based chatbot response generation with template fallback","description":"Processes incoming user messages through a lightweight intent classifier (likely keyword/pattern-based or simple ML model) to map queries to predefined response templates or knowledge base articles. Falls back to escalation or generic responses when confidence is below threshold. Does not implement advanced NLP like entity extraction or semantic understanding, limiting nuance in complex multi-turn scenarios.","intents":["I want to automatically answer common support questions like 'What are your hours?' or 'How do I reset my password?' without human intervention","I need to route complex questions to a human agent when the bot isn't confident in its response","I want to customize bot responses for my specific business without writing code"],"best_for":["support teams handling high-volume repetitive inquiries (FAQs, account resets, billing questions)","businesses with straightforward, non-ambiguous customer questions","teams without NLP expertise who need quick automation without ML infrastructure"],"limitations":["Intent recognition is shallow — cannot disambiguate between similar queries or understand context-dependent meanings (e.g., 'I can't log in' vs 'I forgot my password')","No entity extraction — cannot parse structured information like order numbers, dates, or customer IDs from messages for contextual responses","Template-based responses lack personalization — all users receive identical answers regardless of account history or previous interactions","No multi-turn conversation memory — each message is classified independently, losing context from previous exchanges in the same conversation"],"requires":["Smitty account with chatbot creation permissions","Pre-defined response templates or knowledge base articles to map intents to","Minimum 5-10 training examples per intent for reasonable classifier accuracy"],"input_types":["natural language text messages","user metadata (optional, for routing decisions)"],"output_types":["template-based text response","escalation signal to human agent","confidence score for intent match"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_smitty__cap_10","uri":"capability://automation.workflow.conversation.scheduling.and.appointment.booking","name":"conversation scheduling and appointment booking","description":"Enables chatbots to collect appointment details (date, time, customer name, contact info) through guided conversation flows and automatically schedule them in a calendar or external scheduling system. Supports calendar integrations (Google Calendar, Outlook) and sends confirmation emails/SMS to customers. Prevents double-booking by checking availability before confirming.","intents":["I want the chatbot to book appointments without requiring customers to use a separate scheduling tool","I need appointment confirmations sent automatically to customers via email or SMS","I want to prevent double-booking by checking real-time calendar availability"],"best_for":["service businesses (salons, clinics, consultants) handling high appointment volume","support teams using chatbots to pre-screen and schedule customer calls","businesses wanting to reduce no-shows through automated reminders"],"limitations":["Calendar integration is limited to Google Calendar and Outlook — no support for other scheduling systems","No automatic reminders — customers may forget appointments without manual follow-up","Timezone handling may be error-prone — customers in different timezones may book at incorrect times","No support for recurring appointments or complex scheduling rules (e.g., 'only available on weekdays')","Cancellation and rescheduling require manual intervention or separate bot flows"],"requires":["Smitty workspace with appointment booking feature enabled","Calendar integration configured (Google Calendar or Outlook API credentials)","Calendar with available time slots defined","Email or SMS provider for sending confirmations"],"input_types":["user message with appointment intent","customer name, email, phone","preferred date and time","service type or duration"],"output_types":["calendar event created","confirmation email/SMS sent to customer","appointment details stored in Smitty"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_smitty__cap_11","uri":"capability://tool.use.integration.customer.data.enrichment.and.crm.integration","name":"customer data enrichment and crm integration","description":"Integrates with CRM systems (Salesforce, HubSpot, Pipedrive) to look up customer information based on email or phone number, enriching chatbot context with account history, previous interactions, and customer metadata. Bot can reference this data in responses (e.g., 'Hi John, I see you purchased X last month'). Supports bidirectional sync to update CRM with new conversation data.","intents":["I want the chatbot to recognize returning customers and personalize responses based on their history","I need to automatically log chatbot conversations in our CRM for sales and support teams","I want the bot to access customer account information to provide contextual support"],"best_for":["sales and support teams using CRM systems to manage customer relationships","businesses with complex customer data that should inform bot responses","teams wanting to close the loop between chatbot interactions and CRM records"],"limitations":["CRM integration is limited to major platforms (Salesforce, HubSpot, Pipedrive) — no support for custom or niche CRM systems","Data lookup latency adds 200-500ms per message — may slow down response times","No automatic conflict resolution — if customer data in CRM conflicts with chat input, unclear which takes precedence","Privacy concerns — storing customer data in Smitty may violate data residency or compliance requirements","Sync is not real-time — changes in CRM may take minutes to reflect in chatbot context"],"requires":["Smitty workspace with CRM integration feature enabled","CRM account (Salesforce, HubSpot, or Pipedrive) with API access","CRM API credentials configured in Smitty","Customer records in CRM with email or phone number for lookup"],"input_types":["customer email or phone number from chat","CRM query (customer name, account ID)","conversation context"],"output_types":["enriched customer profile (name, account history, previous interactions)","personalized bot response","conversation logged in CRM"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_smitty__cap_2","uri":"capability://memory.knowledge.knowledge.base.integration.and.article.retrieval","name":"knowledge base integration and article retrieval","description":"Indexes customer-provided documentation, FAQs, and help articles into a searchable knowledge base that the chatbot queries to ground responses. Uses keyword or basic semantic search (likely TF-IDF or simple embeddings) to retrieve relevant articles when answering user questions. Supports bulk import of articles via CSV/markdown and manual creation through a web UI.","intents":["I want the chatbot to pull answers from my existing help documentation instead of hardcoding responses","I need to update bot answers by editing knowledge base articles without rebuilding the chatbot","I want to import my company's FAQ or support docs in bulk to seed the knowledge base"],"best_for":["support teams with existing documentation who want to repurpose it for chatbot answers","businesses with frequently updated policies or product information that need single-source-of-truth","teams managing 50-500 knowledge articles without need for advanced semantic search"],"limitations":["Search relevance is limited to keyword matching or basic embeddings — cannot understand semantic similarity between 'password reset' and 'account access recovery'","No automatic knowledge base updates — changes to source documents require manual re-indexing or re-import","Scaling to 10,000+ articles may degrade search performance without pagination or filtering","No version control or audit trail for knowledge base changes — difficult to track who modified articles and when"],"requires":["Smitty knowledge base feature enabled in workspace","Articles in supported formats (markdown, plain text, CSV with title/content columns, or manual web UI entry)","Minimum 10-20 articles for meaningful search results; 100+ recommended for diverse query coverage"],"input_types":["markdown or plain text articles","CSV files with article metadata","manual text entry via web UI"],"output_types":["ranked list of relevant articles","article excerpt or full text","confidence score for relevance match"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_smitty__cap_3","uri":"capability://automation.workflow.conversation.handoff.to.human.agents.with.context.preservation","name":"conversation handoff to human agents with context preservation","description":"Detects when a chatbot conversation should escalate to a human agent (via explicit user request, low intent confidence, or predefined escalation rules) and transfers the conversation thread with full message history and user metadata to an available agent. Maintains conversation continuity so the agent sees the complete context without requiring the user to repeat information.","intents":["I want the bot to recognize when it can't help and smoothly hand off to a human without losing conversation history","I need to ensure agents have full context about what the bot already tried before taking over","I want to route escalations to specific team members based on department or skill"],"best_for":["support teams using bots to filter simple queries and escalate complex ones","businesses where bot-to-human handoff is critical to customer experience","teams with 2-20 agents managing escalations from multiple channels"],"limitations":["No intelligent agent assignment — escalations are routed to first available agent rather than best-fit based on expertise or conversation topic","Handoff latency depends on agent availability — no queue management or SLA enforcement if all agents are busy","No post-escalation analytics — difficult to measure bot effectiveness or identify gaps in bot training based on escalation patterns","Context preservation is limited to message history — no automatic inclusion of customer account data, previous tickets, or CRM information"],"requires":["Smitty workspace with at least one human agent account","Agent availability status (online/offline) configured in workspace settings","Optional: escalation rules defined (e.g., 'route billing questions to finance team')"],"input_types":["escalation trigger (user request, confidence threshold, rule match)","conversation history with all messages and metadata","user profile data"],"output_types":["escalation notification to agent","conversation thread transferred to agent inbox","acknowledgment message to user"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_smitty__cap_4","uri":"capability://text.generation.language.multi.language.chatbot.support.with.translation","name":"multi-language chatbot support with translation","description":"Enables chatbots to handle conversations in multiple languages by automatically detecting incoming message language and translating to a configured primary language for intent classification, then translating bot responses back to the user's language. Uses third-party translation APIs (likely Google Translate or similar) rather than maintaining proprietary language models.","intents":["I want my chatbot to serve customers in multiple languages without creating separate bots for each language","I need the bot to automatically detect what language a customer is using and respond in kind","I want to manage bot responses in English but have them automatically translated to customer languages"],"best_for":["global businesses serving customers across multiple regions and languages","teams without multilingual content creation resources","support operations handling 5-20 languages without dedicated translation staff"],"limitations":["Translation quality depends on third-party API — idioms, cultural context, and domain-specific terminology may be lost or mistranslated","Language detection is imperfect for short messages or code-mixed text (e.g., 'hola world') — may misclassify language and produce incorrect translations","Each translation adds latency (typically 200-500ms per API call) — multi-language conversations may feel slower than single-language","No support for right-to-left languages (Arabic, Hebrew) in UI — text may display incorrectly in chat widgets","Translation API costs scale with message volume — high-traffic multilingual bots may incur significant translation fees"],"requires":["Smitty workspace with multi-language feature enabled","Third-party translation API credentials (Google Translate, Azure Translator, or similar)","Primary language configured for bot logic and response templates","List of target languages to support"],"input_types":["natural language text in any supported language","language code or auto-detection"],"output_types":["translated message in user's language","detected language code","translation confidence score"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_smitty__cap_5","uri":"capability://tool.use.integration.web.chat.widget.deployment.and.customization","name":"web chat widget deployment and customization","description":"Provides a pre-built, embeddable chat widget that businesses can add to their website with a single script tag. Supports basic visual customization (colors, logo, position) through a no-code UI builder. Widget communicates with Smitty backend via WebSocket or polling to send/receive messages and maintain conversation state across page reloads.","intents":["I want to add a chat interface to my website without building a custom UI or hiring a frontend developer","I need the chat widget to match my brand colors and logo","I want the chat to persist across page navigation so customers don't lose their conversation"],"best_for":["non-technical founders and small business owners adding chat to websites","marketing teams needing quick chat deployment without engineering resources","businesses with simple branding requirements (colors, logo, basic positioning)"],"limitations":["Customization is limited to basic styling — no control over layout, message bubble design, or advanced CSS without custom code","Widget performance depends on page load time — heavy websites may experience chat lag or delayed message delivery","No offline mode — chat is unavailable if backend is down or user loses internet connection","Mobile responsiveness is pre-built but not fully customizable — may not fit all mobile layouts perfectly","Analytics are basic — no heatmaps, user journey tracking, or detailed engagement metrics"],"requires":["Smitty workspace with web chat feature enabled","Website with ability to add custom script tags (most CMS platforms support this)","Optional: custom domain for chat API calls (for advanced security requirements)"],"input_types":["visual customization settings (colors, logo URL, position)","user messages from chat widget","page metadata (URL, referrer, user ID)"],"output_types":["embeddable script tag","rendered chat widget on webpage","message events sent to Smitty backend"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_smitty__cap_6","uri":"capability://data.processing.analysis.conversation.analytics.and.performance.metrics","name":"conversation analytics and performance metrics","description":"Tracks and reports on chatbot conversation metrics including message volume, response times, user satisfaction (via post-chat surveys), escalation rates, and intent distribution. Aggregates data into dashboards showing trends over time and identifying high-volume intents or common escalation triggers. Metrics are calculated server-side from conversation logs and presented via web UI.","intents":["I want to see how many conversations my bot is handling and what percentage are being escalated","I need to identify which types of questions my bot struggles with so I can improve its training","I want to measure customer satisfaction with bot responses to justify continued investment"],"best_for":["support managers measuring bot ROI and effectiveness","teams iterating on bot training based on performance data","businesses reporting chatbot metrics to stakeholders or executives"],"limitations":["Analytics are descriptive, not predictive — no forecasting of conversation volume or escalation trends","No cohort analysis — cannot segment metrics by user type, geography, or conversation topic","Satisfaction data is limited to optional post-chat surveys — no sentiment analysis of actual messages","Data retention may be limited (e.g., 90 days) — long-term trend analysis requires external data warehouse","No integration with external analytics tools — metrics are siloed within Smitty UI"],"requires":["Smitty workspace with analytics feature enabled","Minimum 50-100 conversations to generate meaningful metrics","Optional: post-chat survey enabled to collect satisfaction ratings"],"input_types":["conversation logs with timestamps and metadata","user satisfaction survey responses","escalation events"],"output_types":["dashboard with KPI cards (message volume, escalation rate, avg response time)","trend charts over time","intent distribution breakdown","exportable reports (CSV or PDF)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_smitty__cap_7","uri":"capability://tool.use.integration.email.integration.for.asynchronous.customer.support","name":"email integration for asynchronous customer support","description":"Connects Smitty chatbot to email inboxes, allowing customers to initiate or continue conversations via email. Incoming emails are parsed, converted to chat messages, and routed to the chatbot or human agents. Bot responses are sent back as email replies, maintaining conversation threading. Supports both inbound email parsing and outbound SMTP delivery.","intents":["I want customers to be able to email my support address and have the chatbot respond automatically","I need to maintain conversation continuity when customers switch between chat and email","I want to avoid managing separate email and chat inboxes"],"best_for":["support teams receiving high email volume and wanting to automate responses","businesses where customers prefer email over chat","teams consolidating multiple communication channels into one platform"],"limitations":["Email parsing is fragile — quoted text, forwarded messages, and complex formatting may confuse intent classification","Response latency is higher than chat (minutes vs seconds) — not suitable for real-time support expectations","No support for email attachments — customers cannot upload files or images for troubleshooting","Threading may break if customer forwards email to multiple recipients or uses different email clients","Spam filtering may block bot responses — legitimate emails may land in junk folders"],"requires":["Smitty workspace with email integration feature","Email account credentials (IMAP/SMTP) or email forwarding rules configured","Domain verification for outbound email delivery (SPF, DKIM, DMARC records)"],"input_types":["incoming email messages (MIME format)","email metadata (sender, subject, timestamp)"],"output_types":["parsed email as chat message","outbound email reply","conversation thread in unified inbox"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_smitty__cap_8","uri":"capability://tool.use.integration.sms.and.whatsapp.messaging.integration","name":"sms and whatsapp messaging integration","description":"Connects Smitty chatbot to SMS and WhatsApp APIs to enable two-way messaging with customers on mobile platforms. Incoming SMS/WhatsApp messages are converted to chat format, routed to the bot or agents, and responses are sent back as SMS/WhatsApp messages. Supports media messages (images, documents) on WhatsApp.","intents":["I want to reach customers on their preferred messaging apps (WhatsApp, SMS) instead of forcing them to web chat","I need to send proactive notifications or reminders via SMS or WhatsApp","I want to handle customer support conversations on mobile messaging platforms"],"best_for":["businesses in regions where WhatsApp is dominant (Europe, Latin America, Asia)","support teams handling high SMS volume (appointment reminders, order updates)","mobile-first businesses where customers rarely use web chat"],"limitations":["SMS is limited to 160 characters per message — long responses must be split across multiple messages, degrading UX","WhatsApp media support is limited to images and documents — no video or audio messages","Message delivery is not guaranteed — SMS may be delayed or lost on poor networks","Carrier filtering may block bot messages as spam — legitimate support messages may not reach customers","WhatsApp Business API requires business verification and approval from Meta — setup is not instant"],"requires":["Smitty workspace with SMS/WhatsApp integration enabled","SMS provider account (Twilio, AWS SNS, or similar) with API credentials","WhatsApp Business Account with API access (requires Meta business verification)","Phone number(s) for SMS/WhatsApp"],"input_types":["SMS messages (text only)","WhatsApp messages (text, images, documents)","message metadata (sender phone, timestamp)"],"output_types":["SMS reply (text, up to 160 characters per segment)","WhatsApp reply (text, images, documents)","delivery status (sent, delivered, read)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_smitty__cap_9","uri":"capability://planning.reasoning.bot.training.via.conversation.examples.and.feedback","name":"bot training via conversation examples and feedback","description":"Allows users to improve bot accuracy by providing conversation examples (user message + expected bot response) and marking bot responses as correct or incorrect. Uses this feedback to retrain intent classifiers and improve response matching. Training data is stored and used to update bot models, though the retraining mechanism (batch vs online learning) is not specified.","intents":["I want to teach the bot new intents by providing examples of customer questions and correct answers","I want to mark bot mistakes so it learns not to repeat them","I want to improve bot accuracy over time without hiring ML engineers"],"best_for":["support teams iteratively improving bot performance based on real conversations","businesses with domain-specific terminology or unique customer questions","teams without ML expertise who need simple feedback mechanisms"],"limitations":["Training feedback is not real-time — retraining may take hours or days, so improvements are not immediate","No active learning — system does not suggest which conversations to label for maximum impact","Feedback quality depends on user accuracy — incorrect labels can degrade bot performance","No version control for training data — difficult to rollback to previous bot versions if new training degrades performance","Scalability is unclear — no information on how many training examples are needed for meaningful improvement"],"requires":["Smitty workspace with bot training feature enabled","Access to conversation history to select examples","Minimum 10-20 labeled examples per intent for meaningful training"],"input_types":["user message text","expected bot response","feedback label (correct/incorrect)","conversation context (optional)"],"output_types":["updated intent classifier","improved response matching","training metrics (accuracy, coverage)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Active accounts on at least one messaging platform (web, email, SMS, WhatsApp, or Messenger)","API credentials for each channel to be connected","Smitty workspace with admin permissions to configure routing rules","Smitty account with chatbot creation permissions","Pre-defined response templates or knowledge base articles to map intents to","Minimum 5-10 training examples per intent for reasonable classifier accuracy","Smitty workspace with appointment booking feature enabled","Calendar integration configured (Google Calendar or Outlook API credentials)","Calendar with available time slots defined","Email or SMS provider for sending confirmations"],"failure_modes":["No native support for custom channel integrations beyond pre-built connectors — extending to proprietary platforms requires API requests","Conversation context is limited to current session; no cross-session learning or pattern recognition across channels","Rate limiting on channel APIs may cause message delays during traffic spikes if queue processing isn't scaled","Intent recognition is shallow — cannot disambiguate between similar queries or understand context-dependent meanings (e.g., 'I can't log in' vs 'I forgot my password')","No entity extraction — cannot parse structured information like order numbers, dates, or customer IDs from messages for contextual responses","Template-based responses lack personalization — all users receive identical answers regardless of account history or previous interactions","No multi-turn conversation memory — each message is classified independently, losing context from previous exchanges in the same conversation","Calendar integration is limited to Google Calendar and Outlook — no support for other scheduling systems","No automatic reminders — customers may forget appointments without manual follow-up","Timezone handling may be error-prone — customers in different timezones may book at incorrect times","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2833333333333333,"quality":0.6799999999999999,"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":"2026-05-24T12:16:33.096Z","last_scraped_at":"2026-04-05T13:23:42.562Z","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=smitty","compare_url":"https://unfragile.ai/compare?artifact=smitty"}},"signature":"gPPX/+3HFtEzVxcarlTr4wUcYhj/JBW204jw6dOAHeRToW7J/vr+ymi4U7vSeb5DUfrbJHvlEaUMA7161XGuCw==","signedAt":"2026-06-20T14:05:36.507Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/smitty","artifact":"https://unfragile.ai/smitty","verify":"https://unfragile.ai/api/v1/verify?slug=smitty","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"}}