{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_stammer","slug":"stammer","name":"Stammer","type":"product","url":"https://stammer.ai","page_url":"https://unfragile.ai/stammer","categories":["app-builders"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_stammer__cap_0","uri":"capability://automation.workflow.no.code.ai.chatbot.builder.with.visual.workflow.editor","name":"no-code ai chatbot builder with visual workflow editor","description":"Provides a drag-and-drop interface for agencies to construct conversational AI flows without writing code. The builder likely uses a node-based graph system where agencies connect intent recognition, response generation, and API call nodes to define chatbot behavior. Responses are powered by underlying LLM inference (model selection unclear from available data), with visual state management replacing traditional prompt engineering and code deployment.","intents":["I want to build a customer support chatbot for my client without hiring a developer","I need to rapidly prototype multiple chatbot variations to test with different client segments","I want to modify chatbot behavior on-the-fly without redeploying code or waiting for engineering cycles"],"best_for":["Mid-market agencies with 5-50 person teams building AI services for SMB clients","Non-technical agency founders or account managers who own client relationships but lack AI engineering","Agencies seeking sub-2-week time-to-launch for chatbot MVPs"],"limitations":["Visual workflow abstraction likely hides advanced LLM capabilities (few-shot prompting, token budgeting, model-specific optimizations)","Freemium tier probably restricts node complexity, conversation history depth, or number of custom intents","No indication of support for multi-turn reasoning or complex branching logic beyond simple if-then flows"],"requires":["Web browser with modern JavaScript support (Chrome, Firefox, Safari, Edge)","API credentials for underlying LLM provider (OpenAI, Anthropic, or proprietary model — unclear from data)","Client data/knowledge base in text, PDF, or structured format for training chatbot context"],"input_types":["text (conversation flows, intent definitions)","documents (PDF, TXT, Markdown for knowledge base ingestion)","structured data (CSV/JSON for FAQ mapping)"],"output_types":["deployed chatbot widget (embeddable iframe or API endpoint)","conversation logs (structured JSON with intent, response, confidence scores)","analytics dashboard (conversation volume, intent distribution, user satisfaction metrics)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stammer__cap_1","uri":"capability://automation.workflow.white.label.chatbot.deployment.with.agency.branding","name":"white-label chatbot deployment with agency branding","description":"Enables agencies to deploy chatbots under their own brand identity without exposing Stammer infrastructure or branding. This likely involves customizable UI theming (colors, logos, fonts), domain mapping (custom subdomain or embedded widget), and client-facing analytics dashboards branded with agency colors. The deployment architecture probably uses containerized instances or multi-tenant isolation with per-client configuration overrides.","intents":["I want my clients to see only my agency's branding, not Stammer's, so they think I built this in-house","I need to maintain my client relationship and brand authority while outsourcing the AI infrastructure","I want to offer AI chatbots as a premium service under my own product name"],"best_for":["Agencies with established brand recognition seeking to add AI services without diluting brand identity","Reseller partners who want to white-label Stammer as a backend without client visibility","Agencies managing 10+ concurrent client chatbot deployments and needing per-client branding"],"limitations":["White-label customization likely limited to UI/UX theming; underlying model behavior and LLM provider remain fixed","Freemium tier probably disables white-label features entirely, forcing upgrade to paid plans","Custom domain mapping may require DNS configuration and SSL certificate management, adding operational overhead","No indication of support for custom model fine-tuning or proprietary LLM integration per client"],"requires":["Paid Stammer plan (white-label likely gated behind premium tier)","Custom domain or subdomain for chatbot deployment","Agency branding assets (logo, color palette, fonts in web-compatible formats)","DNS access to configure CNAME or A records for custom domain"],"input_types":["design assets (PNG/SVG logos, hex color codes, font files)","domain configuration (DNS records, SSL certificates)","client metadata (company name, industry, custom instructions)"],"output_types":["branded chatbot widget (embeddable HTML/JavaScript or iframe)","custom domain endpoint (https://chatbot.agency-domain.com)","client-facing analytics dashboard (branded UI with conversation metrics)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stammer__cap_10","uri":"capability://text.generation.language.multi.language.support.and.localization","name":"multi-language support and localization","description":"Enables chatbots to support multiple languages, with automatic language detection and response translation. The platform likely detects user language from initial message and routes to language-specific response templates or uses LLM-based translation. Agencies can define responses in multiple languages or rely on automatic translation, with language-specific knowledge bases and intent definitions.","intents":["I want my chatbot to serve customers in multiple languages without building separate chatbots","I need to localize responses for different regions (e.g., Spanish for Latin America vs Spain)","I want to automatically detect customer language and respond appropriately"],"best_for":["Agencies serving global clients with multilingual customer bases","Clients in non-English-speaking markets wanting to automate customer support","Teams managing chatbots across multiple regions and languages"],"limitations":["Automatic translation quality depends on LLM capabilities; domain-specific terminology may be mistranslated","Freemium tier likely supports only English or a single language; additional languages require paid upgrade","Language detection may fail for code-mixed messages (e.g., 'Hola, can you help me?')","No indication of support for right-to-left languages (Arabic, Hebrew) or complex scripts (Chinese, Japanese)","Localization beyond translation (currency, date formats, cultural nuances) likely not supported"],"requires":["Response templates in target languages (or reliance on automatic translation)","Language-specific knowledge bases (optional; can use automatic translation)","Stammer account with multi-language feature enabled (likely paid tier)","LLM support for target languages (most modern LLMs support 50+ languages)"],"input_types":["user messages in any language","response templates in multiple languages","language-specific knowledge bases (optional)"],"output_types":["language-detected user message (with confidence score)","translated responses (in detected language)","language-specific conversation logs"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stammer__cap_11","uri":"capability://planning.reasoning.chatbot.training.and.iterative.improvement.workflow","name":"chatbot training and iterative improvement workflow","description":"Provides tools for agencies to review conversation logs, identify failure cases, and iteratively improve chatbot performance. The platform likely surfaces low-confidence conversations, user feedback, and intent misclassifications, allowing agencies to add training examples, refine intent definitions, or adjust response templates. Changes are deployed without downtime, and performance improvements are tracked over time.","intents":["I want to see which conversations failed and understand why so I can fix the chatbot","I need to add training examples to improve intent recognition accuracy","I want to measure chatbot improvement over time as I make changes"],"best_for":["Agencies iterating on chatbot quality and wanting data-driven improvement","Clients with high-volume conversations generating rich training data","Teams managing multiple chatbots and needing systematic improvement processes"],"limitations":["Training workflow is manual; no automated retraining or model updates","Freemium tier likely provides limited access to conversation logs or training tools","No indication of support for active learning (automatically selecting high-value examples for labeling)","Training data quality depends on manual review; biased or incorrect labels degrade model performance","Retraining latency unclear; changes may take hours or days to propagate to production"],"requires":["Active chatbot deployment with conversation traffic","User feedback mechanism (thumbs up/down, explicit corrections)","Stammer account with training/improvement features enabled (likely paid tier)","Time investment for manual review and labeling of conversation logs"],"input_types":["conversation logs (with intent labels, confidence scores, user feedback)","training examples (user messages with correct intent labels)","intent definitions (refined based on failure analysis)"],"output_types":["improved intent classifier (retrained on new examples)","updated response templates (based on failure analysis)","performance metrics (accuracy improvement over time)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stammer__cap_12","uri":"capability://automation.workflow.client.management.and.multi.tenant.workspace.organization","name":"client management and multi-tenant workspace organization","description":"Provides workspace and permission management for agencies to organize multiple client chatbots, assign team members to specific clients, and control access levels (admin, editor, viewer). The platform likely uses role-based access control (RBAC) with per-client isolation, allowing agencies to manage billing, usage, and team assignments at the client level. Agencies can invite team members, set permissions, and track usage per client.","intents":["I want to organize my chatbots by client and control which team members can access each one","I need to track usage and costs per client for billing and profitability analysis","I want to invite contractors or team members to work on specific client projects with limited permissions"],"best_for":["Agencies managing 5+ concurrent client chatbot projects","Teams with multiple roles (account managers, developers, support staff) needing granular access control","Agencies wanting to track per-client profitability and usage metrics"],"limitations":["Freemium tier likely limits number of team members or clients per workspace","No indication of support for advanced permission models (e.g., per-feature access, time-based access)","Billing and usage tracking may be basic; no integration with accounting systems for automated invoicing","Team member invitations may require manual approval; no automated onboarding workflows"],"requires":["Stammer account with team/workspace features enabled (likely paid tier)","Team member email addresses for invitations","Client organization and naming conventions"],"input_types":["team member email addresses and role assignments","client names and metadata","permission definitions (admin, editor, viewer)"],"output_types":["workspace organization (clients grouped by agency or team)","team member access logs (who accessed which client, when)","usage and billing reports (per client, per team member)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stammer__cap_2","uri":"capability://memory.knowledge.knowledge.base.ingestion.and.rag.powered.context.retrieval","name":"knowledge base ingestion and rag-powered context retrieval","description":"Allows agencies to upload client documents (PDFs, web pages, FAQs, product documentation) which are chunked, embedded, and stored in a vector database. During chatbot conversations, user queries are embedded and matched against the knowledge base using semantic similarity search, with retrieved documents injected into the LLM prompt as context. This retrieval-augmented generation (RAG) approach grounds chatbot responses in client-specific information rather than relying solely on the base LLM's training data.","intents":["I want my chatbot to answer questions about my client's specific products, pricing, and policies without hallucinating","I need to update chatbot knowledge without retraining or redeploying — just upload new documents","I want to ensure chatbot responses cite sources from our knowledge base so clients trust the answers"],"best_for":["Agencies building customer support chatbots for clients with proprietary product knowledge","Clients with frequently updated documentation (pricing, policies, product specs) that need real-time chatbot accuracy","Teams wanting to reduce hallucination risk by grounding responses in verified source material"],"limitations":["Vector search quality depends on document chunking strategy and embedding model; poor chunking leads to irrelevant context injection","Freemium tier likely limits knowledge base size (e.g., 10 documents, 1MB total) or retrieval frequency","No indication of support for real-time document updates; knowledge base may require manual refresh cycles","Semantic search can fail on domain-specific terminology or numerical queries (e.g., 'products under $50')","Retrieved context is injected into prompt, consuming token budget and increasing latency per query"],"requires":["Client documents in supported formats (PDF, TXT, Markdown, HTML, or web URLs)","Minimum document quality (clear text, not scanned images without OCR)","Stammer account with knowledge base feature enabled (likely paid tier)","API key or credentials for document source if using web scraping (e.g., Sitemap URL)"],"input_types":["documents (PDF, TXT, Markdown, HTML, DOCX)","web URLs (for automatic crawling and indexing)","structured data (CSV/JSON with Q&A pairs or product catalogs)"],"output_types":["vector embeddings (stored in internal vector database)","retrieved context chunks (text snippets injected into LLM prompt)","source citations (document name, page number, or URL reference in chatbot response)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stammer__cap_3","uri":"capability://automation.workflow.multi.channel.chatbot.deployment.web.messaging.voice","name":"multi-channel chatbot deployment (web, messaging, voice)","description":"Enables deployment of the same chatbot logic across multiple communication channels — web widget, SMS, WhatsApp, Slack, Teams, or voice (phone/IVR). The platform likely uses a channel abstraction layer that translates between different message formats and APIs while maintaining consistent conversation state and context across channels. Each channel integration handles protocol-specific requirements (character limits for SMS, rich formatting for Slack, audio transcription for voice).","intents":["I want my client's chatbot to meet customers where they are — web, WhatsApp, SMS, and Slack","I need to maintain conversation context when a customer switches from SMS to web chat mid-conversation","I want to deploy once and reach multiple channels without rebuilding the chatbot logic for each platform"],"best_for":["Agencies building omnichannel customer support solutions for clients with diverse user bases","Clients wanting to reduce support costs by automating FAQs across all communication channels","Teams managing multiple client deployments and needing to scale across channels efficiently"],"limitations":["Freemium tier likely supports only web widget; SMS, WhatsApp, and voice require paid upgrades","Channel-specific limitations (SMS character limits, WhatsApp media restrictions, voice transcription accuracy) may require manual response tuning per channel","Conversation state synchronization across channels adds latency and complexity; no guarantee of real-time consistency","Voice channel support (if offered) likely requires additional setup (phone number provisioning, IVR configuration) with carrier dependencies","Rich formatting (buttons, carousels, images) may not translate across all channels, requiring fallback text responses"],"requires":["Stammer account with multi-channel feature enabled (likely paid tier)","API credentials for each channel (Twilio for SMS/WhatsApp, Slack API token, Teams bot registration, etc.)","Phone number provisioning for SMS/voice (via Twilio or similar carrier integration)","Webhook endpoints or OAuth tokens for channel integrations"],"input_types":["chatbot configuration (same workflow used across all channels)","channel-specific credentials (API keys, phone numbers, bot tokens)","message templates (with channel-specific formatting rules)"],"output_types":["deployed chatbot instances on each channel (web widget, SMS number, WhatsApp business account, Slack bot, etc.)","unified conversation logs (aggregated across channels with channel metadata)","channel-specific analytics (message volume, response time, satisfaction per channel)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stammer__cap_4","uri":"capability://data.processing.analysis.conversation.analytics.and.performance.monitoring.dashboard","name":"conversation analytics and performance monitoring dashboard","description":"Provides real-time and historical analytics on chatbot conversations, including intent recognition accuracy, user satisfaction metrics, conversation drop-off points, and response latency. The dashboard likely tracks metrics like conversation completion rate, average session duration, top intents, and user feedback (thumbs up/down). Agencies can drill down into individual conversations to debug failures or identify training opportunities for the chatbot.","intents":["I want to see how well my client's chatbot is performing — what percentage of conversations are successful?","I need to identify which intents the chatbot struggles with so I can improve them","I want to show my client ROI metrics (cost savings, customer satisfaction) to justify the AI investment"],"best_for":["Agencies managing multiple client chatbots and needing centralized performance visibility","Clients wanting to measure chatbot ROI and justify continued investment in AI automation","Teams iterating on chatbot quality and needing data-driven insights to prioritize improvements"],"limitations":["Freemium tier likely provides basic metrics only (conversation count, response time); advanced analytics (intent accuracy, user satisfaction trends) probably gated behind paid plans","Analytics are retrospective; no real-time alerting for chatbot failures or performance degradation","User satisfaction metrics depend on explicit feedback (thumbs up/down); implicit satisfaction signals (conversation abandonment) may be inaccurate","No indication of support for custom metrics or integration with external analytics platforms (Mixpanel, Amplitude)","Data retention policy unclear; historical data may be purged after 30-90 days on free tier"],"requires":["Active chatbot deployment with conversation traffic","Stammer account with analytics feature enabled (likely included in paid tiers)","Optional: user feedback mechanism enabled in chatbot UI (thumbs up/down buttons)"],"input_types":["conversation logs (automatically captured from deployed chatbots)","user feedback (explicit ratings, implicit signals like conversation abandonment)","chatbot configuration (intent definitions, response templates for correlation analysis)"],"output_types":["dashboard visualizations (conversation volume, intent distribution, satisfaction scores, response latency)","detailed conversation transcripts (with intent labels, confidence scores, retrieved context)","performance reports (CSV/PDF exports with trends, comparisons across time periods or client segments)","alerts (optional; for critical metrics like chatbot downtime or accuracy drops)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stammer__cap_5","uri":"capability://tool.use.integration.api.integration.and.custom.action.execution","name":"api integration and custom action execution","description":"Allows chatbots to call external APIs or trigger custom actions during conversations — e.g., look up customer account status, create support tickets, process payments, or fetch real-time data. The platform likely provides a node-based interface for defining API calls (HTTP method, headers, payload) with variable substitution from conversation context. Responses from APIs are parsed and injected back into the conversation flow or used to conditionally branch the chatbot logic.","intents":["I want my chatbot to look up customer account information and personalize responses based on their history","I need the chatbot to create support tickets or orders in my client's backend system without human handoff","I want to fetch real-time data (inventory, pricing, availability) to provide accurate answers"],"best_for":["Agencies building chatbots that need to interact with client backend systems (CRM, ticketing, e-commerce)","Clients wanting to automate workflows (ticket creation, payment processing) through conversational interfaces","Teams integrating chatbots with existing business logic and data sources"],"limitations":["Freemium tier likely disables API integration entirely or limits to a small number of calls per month","No indication of support for complex authentication (OAuth 2.0, mutual TLS); likely limited to API keys and basic auth","Error handling and retry logic may be basic; no support for exponential backoff or circuit breakers","API response parsing probably limited to JSON; no support for XML or custom formats","Latency: API calls add 500ms-2s per conversation turn, impacting perceived chatbot responsiveness","No built-in rate limiting or quota management; agencies must implement client-side throttling"],"requires":["External API endpoint with documented request/response format","API credentials (API key, OAuth token, or basic auth credentials)","Stammer account with API integration feature enabled (likely paid tier)","Client backend system with accessible API (REST preferred; SOAP/GraphQL support unclear)"],"input_types":["API endpoint URL and HTTP method (GET, POST, PUT, DELETE)","request headers and payload template (with variable substitution from conversation context)","authentication credentials (API key, OAuth token, basic auth)"],"output_types":["API response data (JSON parsed and available for conditional logic or response injection)","conversation flow branching (based on API response status or data values)","action results (e.g., ticket ID, order confirmation, account balance)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stammer__cap_6","uri":"capability://planning.reasoning.intent.recognition.and.conversation.routing","name":"intent recognition and conversation routing","description":"Automatically classifies user messages into predefined intents (e.g., 'billing question', 'product inquiry', 'complaint') and routes conversations to appropriate responses or escalation paths. The platform likely uses the underlying LLM to perform intent classification with few-shot examples, or a lightweight classifier trained on agency-provided intent definitions. Confidence scores determine whether to execute the matched intent or escalate to human support.","intents":["I want my chatbot to understand what the customer is asking about and respond appropriately","I need to route complex or sensitive conversations to human agents without the chatbot getting confused","I want to track which intents are most common so I can improve chatbot coverage"],"best_for":["Agencies building customer support chatbots with 10-50 distinct intents","Clients wanting to automate high-volume, repetitive inquiries (FAQs, account lookups, billing questions)","Teams iterating on chatbot quality and needing to understand conversation patterns"],"limitations":["Intent classification accuracy depends on training data quality and intent definition clarity; ambiguous intents lead to misclassification","Freemium tier likely limits number of custom intents (e.g., 5-10 intents) or requires upgrading for more","No indication of support for multi-intent conversations (e.g., customer asking about both billing AND product features in one message)","Confidence thresholds for escalation may be difficult to tune; too low = unnecessary human handoff, too high = chatbot errors","Intent definitions require manual curation; no automated intent discovery from conversation logs"],"requires":["Intent definitions (name, description, example user messages)","Response templates or API integrations for each intent","Stammer account (likely included in all tiers, but advanced features may require paid plan)"],"input_types":["user messages (text input from chatbot conversations)","intent definitions (name, description, example utterances)","response templates or action definitions per intent"],"output_types":["intent classification (intent name, confidence score)","routed response or action (based on matched intent)","escalation signal (if confidence below threshold or intent not matched)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stammer__cap_7","uri":"capability://automation.workflow.human.handoff.and.escalation.management","name":"human handoff and escalation management","description":"Enables seamless escalation from chatbot to human agents when conversations exceed chatbot capabilities or confidence thresholds. The platform likely maintains conversation context (history, intent, customer info) and routes to available agents via email, Slack, or integrated ticketing systems. Agencies can define escalation rules (e.g., 'if confidence < 0.7, escalate to human') and track handoff metrics (escalation rate, time to human response).","intents":["I want my chatbot to know when to ask for human help instead of giving wrong answers","I need to route escalated conversations to the right team (support, billing, technical) without losing context","I want to measure how often chatbots escalate so I can improve coverage"],"best_for":["Agencies building hybrid chatbot + human support solutions","Clients wanting to reduce support costs while maintaining quality for complex issues","Teams managing multiple support channels and needing unified escalation routing"],"limitations":["Escalation rules are static; no dynamic routing based on agent availability or skill matching","Conversation context may be lost if escalation integrations (Slack, email, ticketing) don't preserve full history","No indication of support for warm handoff (chatbot introduces agent to customer); likely cold transfer","Freemium tier probably disables escalation features or limits to email-only routing","SLA tracking and escalation timeout management unclear; no indication of automatic re-routing if agent doesn't respond"],"requires":["Human support team or ticketing system (Zendesk, Jira, Intercom, Slack, email)","Escalation rule definitions (confidence thresholds, intent-based routing, keyword triggers)","Integration credentials for support platform (API key, webhook URL, Slack bot token, etc.)","Stammer account with escalation feature enabled (likely paid tier)"],"input_types":["conversation context (message history, intent, customer info, confidence scores)","escalation rules (confidence thresholds, intent triggers, keyword patterns)","support team configuration (email addresses, Slack channels, ticketing system details)"],"output_types":["escalation ticket or message (sent to support team with full conversation context)","escalation metrics (rate, average time to human response, resolution rate)","conversation transcript (for agent reference and quality assurance)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stammer__cap_8","uri":"capability://memory.knowledge.conversation.history.and.context.management","name":"conversation history and context management","description":"Maintains conversation state across multiple turns, allowing the chatbot to reference previous messages and build context for coherent multi-turn interactions. The platform likely stores conversation history in a database with per-user session management, and injects relevant history into the LLM prompt to maintain context. Agencies can configure context window size (how many previous messages to include) and conversation timeout (when to start a new session).","intents":["I want my chatbot to remember what the customer said earlier in the conversation","I need the chatbot to handle follow-up questions that reference previous context","I want to preserve conversation history for compliance, training, or customer service review"],"best_for":["Agencies building conversational chatbots that require multi-turn interactions","Clients in regulated industries (finance, healthcare) needing conversation audit trails","Teams wanting to improve chatbot quality by analyzing conversation patterns"],"limitations":["Conversation history increases token consumption per turn, raising inference costs and latency","Freemium tier likely limits conversation history retention (e.g., 30 days) or context window size (e.g., 5 previous messages)","No indication of support for user authentication or session management across devices; history may not sync if user switches browsers","Privacy concerns: conversation history stored server-side may violate data residency or compliance requirements","Context window size is fixed; no dynamic summarization of long conversations to reduce token usage"],"requires":["User identification mechanism (email, phone, or anonymous session ID)","Stammer account with conversation history feature enabled (likely included in paid tiers)","Optional: compliance requirements for data retention and encryption"],"input_types":["user messages (text input from chatbot conversations)","user identifier (email, phone, session ID)","context configuration (history window size, conversation timeout)"],"output_types":["conversation history (stored in database with timestamps and metadata)","context-aware responses (LLM responses informed by previous messages)","conversation transcripts (for export, compliance, or customer review)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stammer__cap_9","uri":"capability://text.generation.language.customizable.response.templates.and.conditional.logic","name":"customizable response templates and conditional logic","description":"Allows agencies to define response templates with variable substitution and conditional branching based on conversation context, user attributes, or API responses. Templates likely support Handlebars or Jinja2-style syntax for injecting variables (e.g., 'Hello {{customer_name}}, your balance is {{account_balance}}'). Conditional logic enables different responses based on intent confidence, user segment, or API response data (e.g., 'if inventory > 0, say product is available; else, offer backorder').","intents":["I want to personalize chatbot responses with customer data (name, account status, purchase history)","I need different responses based on conditions (inventory status, user segment, time of day)","I want to A/B test different response variations to improve customer satisfaction"],"best_for":["Agencies building personalized chatbots that reference customer data","Clients wanting to optimize chatbot responses through A/B testing","Teams managing complex response logic without writing code"],"limitations":["Template syntax may be limited (no complex logic, loops, or function calls); agencies may need to use API integrations for advanced logic","Freemium tier likely disables conditional logic or limits to simple if-then rules","No indication of support for dynamic template generation or template versioning for A/B testing","Variable substitution errors (missing variables, type mismatches) may cause chatbot failures without clear error messages"],"requires":["Response template definitions with variable placeholders","Variable sources (conversation context, API responses, user attributes)","Conditional rule definitions (if-then logic based on variables)","Stammer account (likely included in all tiers)"],"input_types":["response templates (text with variable placeholders and conditional syntax)","variable definitions (source, type, default value)","conditional rules (if-then logic with variable comparisons)"],"output_types":["rendered responses (templates with variables substituted and conditionals evaluated)","response variations (for A/B testing or personalization)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support (Chrome, Firefox, Safari, Edge)","API credentials for underlying LLM provider (OpenAI, Anthropic, or proprietary model — unclear from data)","Client data/knowledge base in text, PDF, or structured format for training chatbot context","Paid Stammer plan (white-label likely gated behind premium tier)","Custom domain or subdomain for chatbot deployment","Agency branding assets (logo, color palette, fonts in web-compatible formats)","DNS access to configure CNAME or A records for custom domain","Response templates in target languages (or reliance on automatic translation)","Language-specific knowledge bases (optional; can use automatic translation)","Stammer account with multi-language feature enabled (likely paid tier)"],"failure_modes":["Visual workflow abstraction likely hides advanced LLM capabilities (few-shot prompting, token budgeting, model-specific optimizations)","Freemium tier probably restricts node complexity, conversation history depth, or number of custom intents","No indication of support for multi-turn reasoning or complex branching logic beyond simple if-then flows","White-label customization likely limited to UI/UX theming; underlying model behavior and LLM provider remain fixed","Freemium tier probably disables white-label features entirely, forcing upgrade to paid plans","Custom domain mapping may require DNS configuration and SSL certificate management, adding operational overhead","No indication of support for custom model fine-tuning or proprietary LLM integration per client","Automatic translation quality depends on LLM capabilities; domain-specific terminology may be mistranslated","Freemium tier likely supports only English or a single language; additional languages require paid upgrade","Language detection may fail for code-mixed messages (e.g., 'Hola, can you help me?')","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:33.648Z","last_scraped_at":"2026-04-05T13:23:42.559Z","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=stammer","compare_url":"https://unfragile.ai/compare?artifact=stammer"}},"signature":"F3ESJgiS9TvNhLYTYPSgDoaBMhRR8UHTvZrbsy68P+7mA13udAu5CmiR8qtSScTH+iqRYsbhdMFWtiicSvYKCg==","signedAt":"2026-06-21T14:35:16.774Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stammer","artifact":"https://unfragile.ai/stammer","verify":"https://unfragile.ai/api/v1/verify?slug=stammer","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"}}