{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_emma-ai","slug":"emma-ai","name":"Emma AI","type":"product","url":"https://getemma.ai","page_url":"https://unfragile.ai/emma-ai","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_emma-ai__cap_0","uri":"capability://automation.workflow.no.code.chatbot.builder.with.visual.workflow.designer","name":"no-code chatbot builder with visual workflow designer","description":"Provides a drag-and-drop interface for constructing chatbot conversation flows without writing code, using a node-based graph editor to define intents, responses, and conditional branching logic. The builder abstracts away NLP pipeline configuration and intent routing, allowing non-technical users to map user inputs to bot actions through visual connectors and configuration panels rather than code or YAML.","intents":["I want to build a chatbot without hiring a developer or learning programming","I need to quickly prototype a customer support bot and test it with real users","I want to modify chatbot behavior on the fly without redeploying code"],"best_for":["non-technical business users and SMB teams","product managers prototyping conversational experiences","customer success teams building support bots"],"limitations":["visual builder abstracts away advanced NLP tuning — limited control over intent confidence thresholds or entity extraction patterns","complex multi-turn conversations with heavy branching logic become difficult to manage visually (no code export or version control integration)","no programmatic access to builder state — cannot automate chatbot creation or bulk updates via API"],"requires":["web browser with modern JavaScript support","Emma AI account with active subscription","basic understanding of conversation design (no coding required)"],"input_types":["text (user utterances and bot responses)","configuration parameters (intent names, response templates)"],"output_types":["chatbot conversation flow definition","deployable bot configuration"],"categories":["automation-workflow","no-code-platform"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_emma-ai__cap_1","uri":"capability://tool.use.integration.live.business.data.connection.and.real.time.knowledge.injection","name":"live business data connection and real-time knowledge injection","description":"Enables chatbots to query and retrieve information from connected business data sources (databases, APIs, knowledge bases) at runtime, injecting live context into bot responses without requiring manual knowledge base uploads or periodic retraining. The system likely uses a connector framework to abstract different data source types and a retrieval layer to fetch relevant information based on user queries, similar to RAG patterns but integrated directly into the conversation flow.","intents":["I want my chatbot to answer questions about current inventory, pricing, or order status without manual updates","I need the bot to pull customer data from our CRM to personalize responses","I want to connect the bot to our help desk system so it can check ticket status in real-time"],"best_for":["businesses with dynamic data that changes frequently (inventory, pricing, customer records)","customer support teams needing real-time access to ticketing or CRM systems","e-commerce and SaaS companies with live order/subscription data"],"limitations":["data connection latency adds 200-500ms per query — not suitable for sub-second response requirements","no built-in caching or query optimization — repeated queries to the same data source may cause performance degradation under high load","limited data source types supported — may require custom API connectors for proprietary or legacy systems","no fine-grained access control — connected data is accessible to all bot conversations (potential security/privacy risk)"],"requires":["API credentials or database connection strings for target data sources","network connectivity from Emma AI infrastructure to data sources","data source must expose queryable API or database interface"],"input_types":["API endpoints (REST, GraphQL)","database connection strings (SQL databases)","knowledge base URLs or document repositories"],"output_types":["structured data (JSON, records)","text snippets injected into bot responses"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_emma-ai__cap_10","uri":"capability://automation.workflow.pre.built.templates.and.industry.specific.bot.starter.packs","name":"pre-built templates and industry-specific bot starter packs","description":"Provides pre-configured chatbot templates for common use cases (customer support, FAQ, lead qualification, booking) with predefined intents, responses, and integrations. Users can select a template, customize it for their business, and deploy without building from scratch, significantly reducing time-to-launch for standard bot scenarios.","intents":["I want to launch a customer support bot quickly without designing conversation flows from scratch","I need a template that already has common support intents (order status, returns, billing) configured","I want to see best practices for bot design through example templates"],"best_for":["small businesses and startups with limited bot design expertise","teams launching their first chatbot and needing a starting point","organizations with standard use cases (FAQ, support, booking)"],"limitations":["templates are generic — require significant customization for domain-specific language and business logic","limited template variety — may not cover niche industries or complex use cases","templates are static — cannot be updated or versioned, so improvements are not propagated to existing bots","no template marketplace or community contributions — limited to Emma AI's pre-built templates"],"requires":["Emma AI account with template access","basic understanding of the bot's use case to customize the template"],"input_types":["template selection and customization parameters"],"output_types":["pre-configured bot with intents, responses, and integrations"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_emma-ai__cap_11","uri":"capability://tool.use.integration.api.based.bot.invocation.and.programmatic.integration","name":"api-based bot invocation and programmatic integration","description":"Exposes REST APIs to invoke chatbots programmatically, allowing external applications to send messages and receive responses without embedding a chat widget. The system provides endpoints for message submission, conversation history retrieval, and bot configuration management, enabling integration with custom applications, mobile apps, or backend systems.","intents":["I want to integrate the chatbot into our mobile app without using a pre-built widget","I need to send messages to the bot from our backend system and process responses programmatically","I want to retrieve conversation history via API for custom analytics or compliance purposes"],"best_for":["developers building custom applications with bot integration","teams with existing chat infrastructure wanting to add bot capabilities","organizations needing programmatic bot access for backend workflows"],"limitations":["API rate limits may restrict high-volume message submission — not suitable for real-time streaming conversations","no webhook support for asynchronous events — must poll API for conversation updates","API documentation may be limited — requires trial-and-error to understand request/response formats","no SDK for popular languages — requires manual HTTP client implementation"],"requires":["API key or authentication token","HTTP client library (curl, requests, axios, etc.)","knowledge of REST API conventions"],"input_types":["JSON request bodies with user messages and metadata","API parameters (conversation ID, user ID, etc.)"],"output_types":["JSON responses with bot messages and metadata","conversation history in structured format"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_emma-ai__cap_12","uri":"capability://safety.moderation.user.authentication.and.conversation.privacy.controls","name":"user authentication and conversation privacy controls","description":"Manages user identity and access control for chatbot conversations, supporting authentication methods (login, SSO, anonymous) and enforcing privacy policies. The system isolates conversations by user, prevents unauthorized access to conversation history, and complies with data retention and deletion policies without requiring manual configuration.","intents":["I want to ensure that users can only see their own conversation history, not other users' conversations","I need to authenticate users before they can access sensitive information through the bot","I want to comply with GDPR by allowing users to delete their conversation data on request"],"best_for":["businesses handling sensitive customer data (financial, healthcare, personal)","enterprises with strict privacy and compliance requirements","organizations using bots for authenticated customer portals"],"limitations":["authentication is limited to basic methods (login, SSO) — no support for advanced MFA or biometric authentication","conversation isolation is user-level only — no role-based access control for team members","data deletion is not instantaneous — may take time to propagate across all storage systems","no audit logging — cannot track who accessed which conversations or when"],"requires":["user identity provider (built-in login, SSO provider, or external auth service)","privacy policy and data retention rules","compliance requirements (GDPR, HIPAA, etc.)"],"input_types":["user credentials or authentication tokens","user identity and metadata"],"output_types":["authenticated user sessions","isolated conversation access","deletion confirmations"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_emma-ai__cap_2","uri":"capability://tool.use.integration.multi.channel.chatbot.deployment.and.conversation.routing","name":"multi-channel chatbot deployment and conversation routing","description":"Deploys trained chatbots across multiple communication channels (web chat, Slack, Teams, WhatsApp, etc.) from a single bot definition, automatically routing incoming messages to the appropriate handler and maintaining conversation context across channels. The system abstracts channel-specific protocols and message formats, allowing the same bot logic to operate on different platforms without duplication.","intents":["I want to deploy my chatbot on our website, Slack workspace, and customer support channels simultaneously","I need customers to start a conversation on web chat and continue it in Slack without losing context","I want to manage all bot conversations from a single dashboard regardless of which channel they came from"],"best_for":["omnichannel customer support teams","enterprises with distributed communication platforms","businesses targeting users across web, mobile, and messaging apps"],"limitations":["channel-specific features (rich media, interactive buttons) may not translate uniformly across all platforms — requires channel-specific response templates","conversation context persistence across channels requires state management — may not work reliably if channels are disconnected or user sessions expire","limited to pre-integrated channels — custom or proprietary communication platforms require additional API work","message formatting and character limits vary by channel — long responses may be truncated or reformatted unpredictably"],"requires":["API credentials or OAuth tokens for each target channel","channel-specific configuration (webhook URLs, bot tokens, app IDs)","Emma AI account with multi-channel deployment feature enabled"],"input_types":["channel-specific message formats (Slack JSON, WhatsApp text, Teams adaptive cards)","user identity and session information"],"output_types":["channel-native message formats","conversation transcripts and analytics"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_emma-ai__cap_3","uri":"capability://planning.reasoning.intent.recognition.and.natural.language.understanding.with.training.data.management","name":"intent recognition and natural language understanding with training data management","description":"Recognizes user intents from natural language input and routes conversations to appropriate bot responses using an underlying NLU model, with a UI for managing training examples and intent definitions. The system likely uses a pre-trained language model (possibly fine-tuned on conversational data) with a classification layer, allowing users to add training examples through the UI to improve intent accuracy without retraining from scratch.","intents":["I want the bot to understand variations of the same user request (e.g., 'What's my balance?' vs 'How much money do I have?')","I need to add new intents and train the bot with example phrases without technical expertise","I want to see which intents the bot is confident about and which need more training data"],"best_for":["businesses with diverse customer communication styles","support teams handling multiple intent categories","organizations needing to improve bot accuracy over time through iterative training"],"limitations":["NLU accuracy depends on training data quality and quantity — insufficient examples (< 5-10 per intent) lead to poor generalization","no access to underlying model weights or confidence scores — cannot implement custom confidence thresholds or fallback logic","intent recognition is single-turn only — no multi-turn context understanding for complex conversations","limited to predefined intent categories — cannot dynamically create new intents based on user feedback without manual UI interaction"],"requires":["training examples for each intent (minimum 3-5 phrases per intent recommended)","clear intent definitions and naming conventions","Emma AI account with NLU training feature"],"input_types":["text utterances (user messages)","intent labels and training examples"],"output_types":["intent classification with confidence scores","routing decisions to bot responses"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_emma-ai__cap_4","uri":"capability://data.processing.analysis.conversation.analytics.and.performance.monitoring.dashboard","name":"conversation analytics and performance monitoring dashboard","description":"Aggregates conversation metrics (message volume, intent distribution, user satisfaction, resolution rates) and displays them in a dashboard with filtering and drill-down capabilities. The system tracks conversation metadata (duration, channel, user demographics) and bot performance indicators (intent accuracy, fallback rates, response latency) to help teams identify improvement areas and monitor bot health.","intents":["I want to see how many conversations my bot handled this week and which intents were most common","I need to identify conversations where the bot failed to understand the user so I can improve training data","I want to track customer satisfaction scores and correlate them with bot performance metrics"],"best_for":["bot managers and team leads monitoring bot performance","customer success teams optimizing bot accuracy","business analysts measuring chatbot ROI and impact"],"limitations":["analytics are aggregated and anonymized — cannot drill down to individual user conversations for privacy reasons","no real-time alerting — metrics are updated on a delay (likely hourly or daily), not in real-time","limited customization of dashboard widgets — cannot create custom metrics or KPIs without API access","no predictive analytics — cannot forecast conversation volume or identify emerging issues before they impact users"],"requires":["Emma AI account with analytics feature enabled","sufficient conversation volume to generate meaningful metrics (minimum 50-100 conversations recommended)"],"input_types":["conversation logs and metadata","user feedback and satisfaction ratings"],"output_types":["dashboard visualizations (charts, tables, KPIs)","exportable reports (CSV, PDF)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_emma-ai__cap_5","uri":"capability://memory.knowledge.conversation.context.persistence.and.multi.turn.dialogue.management","name":"conversation context persistence and multi-turn dialogue management","description":"Maintains conversation state across multiple user turns, allowing the bot to reference previous messages and build context-aware responses. The system stores conversation history (user messages, bot responses, extracted entities) in a session store and retrieves relevant context when generating responses, enabling the bot to handle follow-up questions and maintain coherent multi-turn conversations.","intents":["I want the bot to remember what the user asked in the previous turn and reference it in follow-up responses","I need the bot to extract information from multiple turns (e.g., 'I want to order a pizza' → 'What size?' → 'Large') and compile it into a complete request","I want conversations to persist if the user closes the chat and returns later"],"best_for":["complex support scenarios requiring multi-step information gathering","e-commerce bots handling product recommendations and order placement","onboarding flows requiring sequential user input"],"limitations":["context window is limited — very long conversations (100+ turns) may lose early context or hit token limits","context is not shared across channels — if a user switches from web chat to Slack, conversation history is not available","no built-in context summarization — long conversations may become unwieldy and slow down response generation","context persistence requires external storage — no built-in database, so requires configuration of state backend"],"requires":["session storage backend (database, cache, or Emma AI managed storage)","conversation history retention policy (how long to keep old conversations)"],"input_types":["user messages and conversation history","extracted entities and context variables"],"output_types":["context-aware bot responses","conversation transcripts with full history"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_emma-ai__cap_6","uri":"capability://data.processing.analysis.entity.extraction.and.slot.filling.for.structured.information.capture","name":"entity extraction and slot-filling for structured information capture","description":"Automatically extracts structured information (entities like names, dates, amounts, locations) from user messages and fills predefined slots in the conversation flow. The system uses NER (Named Entity Recognition) or pattern-based extraction to identify relevant data and validates it against slot constraints, enabling the bot to gather required information for transactions or support requests without explicit prompting.","intents":["I want the bot to extract a customer's name, email, and phone number from a single message without asking three separate questions","I need the bot to recognize dates and times in natural language (e.g., 'next Tuesday at 2pm') and convert them to structured format","I want the bot to validate extracted information (e.g., email format, phone number length) and re-prompt if invalid"],"best_for":["customer support bots collecting contact information","booking and reservation systems capturing dates and times","e-commerce bots extracting product preferences and quantities"],"limitations":["entity extraction accuracy depends on training data — rare or domain-specific entities may not be recognized reliably","no support for complex entity relationships — cannot extract hierarchical or nested entities (e.g., 'I want 2 large pizzas and 1 small salad')","slot-filling is sequential only — cannot fill multiple slots in parallel or reorder slot-filling based on user input","no custom entity types without retraining — limited to predefined entity categories (person, location, date, etc.)"],"requires":["slot definitions with names, types, and validation rules","training examples for custom entity types (if applicable)"],"input_types":["natural language text with embedded entities","slot definitions and validation rules"],"output_types":["extracted entities with confidence scores","filled slot values in structured format"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_emma-ai__cap_7","uri":"capability://text.generation.language.response.templating.and.dynamic.content.personalization","name":"response templating and dynamic content personalization","description":"Allows bot responses to be templated with variables and conditional logic, enabling personalized and context-aware replies. The system supports variable substitution (e.g., {{customer_name}}, {{order_id}}) and conditional blocks (if-then-else) to generate different responses based on extracted entities, user attributes, or conversation context without requiring separate response definitions for each variation.","intents":["I want the bot to greet users by name in responses (e.g., 'Hi {{customer_name}}, how can I help?')","I need different responses based on user type (e.g., 'As a premium member, you get...' vs 'As a standard member, you get...')","I want to show different product recommendations based on the user's purchase history or preferences"],"best_for":["customer support bots personalizing interactions","e-commerce bots recommending products based on user data","onboarding bots tailoring content to user role or industry"],"limitations":["templating logic is limited to simple variable substitution and if-then-else — no complex transformations or calculations","no access to external data within templates — cannot call APIs or query databases from response templates","template syntax may be unfamiliar to non-technical users — requires learning a templating language (Jinja, Handlebars, etc.)","no template versioning or A/B testing — cannot easily test different response variations"],"requires":["variable definitions and data sources (user attributes, conversation context, extracted entities)","template syntax knowledge (if using advanced features)"],"input_types":["response templates with variable placeholders","user attributes and context variables"],"output_types":["personalized bot responses with substituted variables","conditional response variations"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_emma-ai__cap_8","uri":"capability://automation.workflow.fallback.handling.and.escalation.to.human.agents","name":"fallback handling and escalation to human agents","description":"Detects when the bot cannot understand a user request or resolve an issue and automatically escalates to a human agent or fallback response. The system monitors intent confidence scores, detects repeated failed attempts, and triggers escalation workflows (e.g., creating a support ticket, routing to a live agent queue) without user intervention.","intents":["I want the bot to hand off to a human agent when it can't understand the user after 2-3 attempts","I need conversations that reach a fallback state to be logged and reviewed by my team","I want to route escalated conversations to specific agents based on topic or priority"],"best_for":["customer support teams handling complex or edge-case issues","businesses requiring human oversight for sensitive conversations","teams using bots as a first-line filter before human support"],"limitations":["escalation triggers are rule-based (confidence threshold, attempt count) — cannot use ML to predict when escalation is needed","no built-in agent routing logic — escalation goes to a generic queue, not to specific agents with relevant expertise","escalation creates context loss — human agents may not have full conversation history or bot reasoning","no feedback loop — escalated conversations are not automatically used to improve bot training"],"requires":["fallback response templates","escalation destination (human agent queue, ticketing system, email)","escalation trigger configuration (confidence threshold, attempt count, keywords)"],"input_types":["intent confidence scores","conversation history and failed attempts","user input and bot responses"],"output_types":["escalation events and tickets","human agent notifications","conversation transcripts for review"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_emma-ai__cap_9","uri":"capability://data.processing.analysis.bot.training.and.iterative.improvement.through.conversation.feedback","name":"bot training and iterative improvement through conversation feedback","description":"Collects user feedback on bot responses (thumbs up/down, ratings, comments) and uses this data to identify training gaps and suggest improvements. The system analyzes failed conversations, low-confidence intents, and negative feedback to recommend new training examples or intent refinements, enabling continuous bot improvement without manual analysis.","intents":["I want to see which bot responses users rated negatively so I can improve them","I need to identify conversations where the bot misunderstood the user and use them as training examples","I want to track bot accuracy improvements over time as I add more training data"],"best_for":["teams iteratively improving bot accuracy","organizations with high conversation volume generating training signals","customer-facing bots where user satisfaction is critical"],"limitations":["feedback collection is optional — users may not provide ratings, limiting training signal quality","no automated retraining — suggested improvements must be manually reviewed and applied by users","feedback bias — negative feedback may be skewed toward edge cases or user frustration rather than actual bot errors","no A/B testing framework — cannot systematically test improvements before deploying them"],"requires":["feedback collection UI (ratings, comments) integrated into bot","sufficient feedback volume to identify patterns (minimum 100+ rated conversations)"],"input_types":["user feedback (ratings, comments)","conversation logs and bot responses","intent confidence scores"],"output_types":["improvement recommendations","training data suggestions","accuracy metrics and trends"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["web browser with modern JavaScript support","Emma AI account with active subscription","basic understanding of conversation design (no coding required)","API credentials or database connection strings for target data sources","network connectivity from Emma AI infrastructure to data sources","data source must expose queryable API or database interface","Emma AI account with template access","basic understanding of the bot's use case to customize the template","API key or authentication token","HTTP client library (curl, requests, axios, etc.)"],"failure_modes":["visual builder abstracts away advanced NLP tuning — limited control over intent confidence thresholds or entity extraction patterns","complex multi-turn conversations with heavy branching logic become difficult to manage visually (no code export or version control integration)","no programmatic access to builder state — cannot automate chatbot creation or bulk updates via API","data connection latency adds 200-500ms per query — not suitable for sub-second response requirements","no built-in caching or query optimization — repeated queries to the same data source may cause performance degradation under high load","limited data source types supported — may require custom API connectors for proprietary or legacy systems","no fine-grained access control — connected data is accessible to all bot conversations (potential security/privacy risk)","templates are generic — require significant customization for domain-specific language and business logic","limited template variety — may not cover niche industries or complex use cases","templates are static — cannot be updated or versioned, so improvements are not propagated to existing bots","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"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":"2026-05-24T12:16:30.284Z","last_scraped_at":"2026-04-05T13:23:42.561Z","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=emma-ai","compare_url":"https://unfragile.ai/compare?artifact=emma-ai"}},"signature":"0Vs18z6RWo1NPl2T2J6A5qo7Fh1eaowhjAn/43vmauJeV36nwrhMpFN2sTfPrh8ph/lkVte3oc6cBvgMkgQnCw==","signedAt":"2026-06-21T10:43:42.306Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/emma-ai","artifact":"https://unfragile.ai/emma-ai","verify":"https://unfragile.ai/api/v1/verify?slug=emma-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"}}