{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_automatic-chat","slug":"automatic-chat","name":"Automatic Chat","type":"product","url":"https://automatic.chat","page_url":"https://unfragile.ai/automatic-chat","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_automatic-chat__cap_0","uri":"capability://text.generation.language.website.embedded.conversational.ai.chatbot","name":"website-embedded conversational ai chatbot","description":"Deploys a JavaScript-based chat widget that embeds directly into website DOM, intercepting visitor interactions through event listeners and routing queries to a cloud-hosted LLM inference backend. The widget maintains session state via browser localStorage and communicates with the backend via REST/WebSocket APIs, enabling real-time bidirectional conversation without page reloads. Handles multi-turn context by maintaining conversation history in the session and sending relevant prior messages to the LLM for coherent follow-up responses.","intents":["I want to add a chat interface to my website without building custom UI or managing infrastructure","I need visitors to get instant answers to common questions without waiting for human support","I want to reduce support ticket volume by automating FAQ-type interactions"],"best_for":["small to mid-sized e-commerce companies with high FAQ volume","SaaS teams wanting 24/7 after-hours support without hiring night-shift staff","non-technical founders who need rapid deployment without engineering overhead"],"limitations":["Widget injection adds ~50-100ms to initial page load due to JavaScript parsing and DOM manipulation","No built-in offline mode — requires active internet connection to function","Limited to text-based interactions; no native support for file uploads, image analysis, or voice input","Session state stored in browser localStorage is vulnerable to XSS attacks if not properly sandboxed","Context window limited by backend LLM constraints — cannot maintain coherent conversations beyond ~20 turns without summarization"],"requires":["Website with HTML/JavaScript support (any CMS or static site)","Active internet connection for both website and visitors","Automatic Chat account with valid API credentials","No specific browser version requirements (ES5+ JavaScript support)"],"input_types":["plain text user messages","structured conversation metadata (visitor ID, page URL, timestamp)"],"output_types":["plain text LLM-generated responses","structured JSON containing response text, confidence scores, and fallback suggestions"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_automatic-chat__cap_1","uri":"capability://memory.knowledge.knowledge.base.ingestion.and.semantic.retrieval","name":"knowledge base ingestion and semantic retrieval","description":"Accepts customer-provided documentation, FAQs, or product knowledge in multiple formats (text, markdown, PDF, web URLs) and converts them into vector embeddings via a semantic encoder. These embeddings are stored in a vector database indexed for fast similarity search. When a visitor asks a question, the system retrieves the top-K most relevant knowledge base documents using cosine similarity, then passes them as context to the LLM to ground responses in actual company information rather than hallucinated generic answers.","intents":["I want the chatbot to answer questions using my actual product documentation, not generic LLM knowledge","I need to reduce hallucinations by grounding responses in verified company information","I want to update chatbot knowledge without retraining or redeploying the underlying model"],"best_for":["SaaS companies with extensive product documentation that changes frequently","e-commerce businesses with large product catalogs and variant-specific FAQs","support teams wanting to enforce consistent, policy-compliant responses"],"limitations":["Retrieval quality depends on knowledge base quality — garbage in, garbage out; poorly written or outdated docs will produce poor responses","Semantic search may fail for domain-specific jargon or acronyms not well-represented in training data","No automatic knowledge base versioning — updating docs can break responses that relied on previous versions","Embedding generation adds ~100-500ms latency per query depending on document count and embedding model size","Maximum knowledge base size likely constrained by vector database capacity and query latency requirements (typically <10GB uncompressed text)"],"requires":["Structured knowledge base in supported format (markdown, PDF, plain text, or web-crawlable URLs)","Regular knowledge base maintenance process to keep documentation current","API credentials for Automatic Chat with knowledge base upload permissions"],"input_types":["markdown files","PDF documents","plain text","web URLs for crawling","structured FAQ JSON"],"output_types":["vector embeddings (typically 384-1536 dimensions)","ranked list of relevant documents with similarity scores","LLM responses grounded in retrieved context"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_automatic-chat__cap_2","uri":"capability://memory.knowledge.multi.turn.conversation.context.management","name":"multi-turn conversation context management","description":"Maintains conversation history across multiple user messages by storing prior exchanges in a session-scoped context buffer. Before generating each response, the system constructs a prompt that includes recent conversation history (typically last 5-10 turns) along with system instructions and retrieved knowledge base context. Uses a sliding window approach to prevent context explosion — older messages are progressively dropped as the conversation grows, with optional summarization to preserve key information from discarded turns.","intents":["I want the chatbot to remember what the user asked earlier in the conversation","I need follow-up questions to be answered in context of previous messages, not treated as isolated queries","I want to avoid repeating information the chatbot already provided in earlier turns"],"best_for":["support scenarios requiring clarification or multi-step troubleshooting","product inquiry flows where users ask progressively more specific questions","any use case where conversation coherence matters more than individual response quality"],"limitations":["Context window is finite — conversations beyond ~20-30 turns will lose coherence as older messages are dropped","No automatic conversation summarization — requires manual configuration or external summarization service to preserve context from dropped messages","Session state is ephemeral — if user closes browser or session expires, conversation history is lost (no built-in persistence across sessions)","Including full conversation history in each LLM prompt adds ~50-200ms latency per turn depending on history length","No deduplication of context — if user repeats a question, the system may generate redundant responses"],"requires":["Browser with localStorage or equivalent session storage support","Backend session management infrastructure (typically Redis or in-memory store)","LLM with sufficient context window to accommodate conversation history + knowledge base context + system prompt"],"input_types":["current user message (text)","prior conversation turns (text)","system instructions and context (text)"],"output_types":["LLM-generated response incorporating prior context","updated conversation history with new turn appended"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_automatic-chat__cap_3","uri":"capability://automation.workflow.human.handoff.and.escalation.routing","name":"human handoff and escalation routing","description":"Detects when a conversation exceeds the chatbot's capability (e.g., user expresses frustration, asks for human support, or query falls outside knowledge base) and automatically routes the conversation to a human agent. The system can integrate with ticketing systems (Zendesk, Intercom, Freshdesk) or email queues to create support tickets with full conversation history, visitor metadata, and context. Optionally maintains a queue of pending escalations with priority scoring based on urgency signals in user messages.","intents":["I want the chatbot to know when to stop trying and hand off to a human","I need escalated conversations to include full context so humans don't repeat questions","I want to track which types of queries the chatbot can't handle to improve its training"],"best_for":["support teams that need a hybrid human-AI model rather than pure automation","companies with existing ticketing systems (Zendesk, Intercom, Freshdesk) that want to integrate chatbot escalations","teams wanting to measure chatbot effectiveness by tracking escalation rates and reasons"],"limitations":["Escalation detection relies on heuristics (keywords, sentiment analysis, explicit 'talk to human' requests) which are error-prone — may escalate unnecessarily or miss genuine escalation needs","No built-in learning loop — escalation patterns are not automatically fed back to improve chatbot training","Requires integration with external ticketing system — adds complexity and potential failure points if ticketing API is unavailable","Conversation history may be too long or poorly formatted for human agents to quickly digest, reducing efficiency gains","No automatic priority routing — all escalations treated equally regardless of urgency or customer value"],"requires":["Integration with supported ticketing system (Zendesk, Intercom, Freshdesk, or email backend)","API credentials for ticketing system with ticket creation permissions","Human agents available to receive escalated conversations (defeats 24/7 automation if no night shift coverage)"],"input_types":["conversation history (text)","visitor metadata (ID, email, page URL)","escalation trigger signals (keywords, sentiment scores, explicit requests)"],"output_types":["support ticket in external system","notification to human agent","conversation transcript with metadata"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_automatic-chat__cap_4","uri":"capability://data.processing.analysis.visitor.identification.and.session.tracking","name":"visitor identification and session tracking","description":"Automatically identifies website visitors through multiple signals: browser cookies, localStorage tokens, email capture forms, or CRM integration (if available). Assigns each visitor a unique session ID and tracks metadata including page URL, referrer, device type, and conversation history. This data is stored server-side and associated with the conversation, enabling support teams to see visitor context when reviewing escalated tickets or analyzing chatbot performance.","intents":["I want to know who is talking to the chatbot so I can follow up with them later","I need to see what page the visitor was on when they asked a question","I want to track repeat visitors and personalize responses based on prior interactions"],"best_for":["e-commerce sites wanting to correlate chatbot interactions with purchase history","SaaS companies tracking user engagement and support needs","support teams needing visitor context to provide personalized escalations"],"limitations":["Cookie-based tracking fails if user has third-party cookies disabled or uses private browsing mode","No automatic CRM integration — requires manual configuration and API credentials for each CRM","Privacy regulations (GDPR, CCPA) require explicit consent before tracking visitor data — may reduce tracking coverage","Session data is typically ephemeral — old sessions may be automatically purged after 30-90 days","No built-in cross-device tracking — if user switches devices, they appear as a new visitor"],"requires":["Website with JavaScript execution enabled","Compliance with privacy regulations (GDPR, CCPA) — requires consent management","Optional: CRM API credentials for deeper visitor context"],"input_types":["browser cookies and localStorage","visitor email (if captured)","page metadata (URL, referrer, device type)"],"output_types":["visitor ID (unique identifier)","session metadata (start time, pages visited, conversation history)","visitor profile (if CRM integrated)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_automatic-chat__cap_5","uri":"capability://safety.moderation.response.quality.filtering.and.confidence.scoring","name":"response quality filtering and confidence scoring","description":"Before returning an LLM-generated response to the user, the system applies multiple quality filters: checks if the response is grounded in retrieved knowledge base documents (if RAG is enabled), scores confidence based on retrieval similarity and LLM uncertainty signals, and applies content policy filters to block harmful or off-topic responses. If confidence is below a threshold, the system may return a fallback response (e.g., 'I'm not sure about that — let me connect you with a human') or offer escalation instead of a potentially incorrect answer.","intents":["I want to avoid the chatbot giving confidently wrong answers that frustrate customers","I need to ensure responses are grounded in my actual product information, not hallucinations","I want to automatically escalate uncertain queries rather than risk customer dissatisfaction"],"best_for":["industries with high accuracy requirements (healthcare, finance, legal)","companies with strict brand guidelines or compliance requirements","support teams wanting to minimize reputational damage from chatbot errors"],"limitations":["Confidence scoring is heuristic-based and imperfect — may block valid responses or allow invalid ones","No ground truth labels — system cannot learn from user feedback whether responses were actually correct","Overly aggressive filtering may cause excessive escalations, defeating automation benefits","Content policy filters are static — require manual updates to handle new edge cases or policy changes","No A/B testing framework — cannot measure impact of different filtering thresholds on user satisfaction"],"requires":["Knowledge base for grounding (if using RAG-based confidence scoring)","LLM with uncertainty quantification capabilities (not all models provide confidence scores)","Content policy definitions (either built-in or custom)"],"input_types":["LLM-generated response (text)","retrieval similarity scores (if RAG enabled)","LLM uncertainty signals (logits, token probabilities)"],"output_types":["confidence score (0-1 or 0-100)","filtered response (original, fallback, or escalation)","quality metadata (grounding sources, policy violations)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_automatic-chat__cap_6","uri":"capability://data.processing.analysis.analytics.and.performance.monitoring.dashboard","name":"analytics and performance monitoring dashboard","description":"Provides a web-based dashboard showing chatbot performance metrics: conversation volume, average response time, user satisfaction ratings (if collected via post-chat surveys), escalation rate, and top unresolved queries. Tracks trends over time and allows filtering by time period, page URL, or visitor segment. Integrates with external analytics platforms (Google Analytics, Mixpanel) to correlate chatbot interactions with business outcomes (conversion rate, support ticket volume, customer satisfaction).","intents":["I want to measure whether the chatbot is actually reducing support workload","I need to identify which types of questions the chatbot struggles with","I want to track chatbot ROI and justify continued investment to stakeholders"],"best_for":["product managers wanting data-driven insights into chatbot performance","support leaders tracking team efficiency and customer satisfaction trends","companies with mature analytics practices wanting to integrate chatbot metrics into existing dashboards"],"limitations":["Metrics are limited to chatbot interactions — cannot measure impact on customers who never use the chatbot","User satisfaction ratings are optional and typically have low response rates (<5%), making trend analysis unreliable","No automatic root cause analysis — high escalation rate is visible but reasons are not automatically identified","Dashboard data has latency (typically 1-24 hours) — cannot monitor real-time issues","No built-in anomaly detection — requires manual review to spot unusual patterns"],"requires":["Access to Automatic Chat dashboard (web browser)","Optional: Google Analytics or Mixpanel account for deeper integration","Basic analytics literacy to interpret metrics and trends"],"input_types":["chatbot conversation logs (timestamps, user messages, responses, escalations)","user satisfaction surveys (if enabled)","visitor metadata (page URL, device type, referrer)"],"output_types":["dashboard visualizations (charts, tables, trend lines)","exportable reports (CSV, PDF)","real-time alerts (optional, if configured)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_automatic-chat__cap_7","uri":"capability://text.generation.language.multi.language.support.and.localization","name":"multi-language support and localization","description":"Automatically detects visitor browser language preference and serves the chatbot interface in that language. Supports translating user messages to a canonical language for LLM processing, then translating responses back to the visitor's language using either built-in translation APIs (Google Translate, DeepL) or fine-tuned multilingual LLMs. Knowledge base documents can be indexed in multiple languages or automatically translated on ingestion.","intents":["I want to support international customers without hiring multilingual support staff","I need the chatbot to work seamlessly for non-English speakers","I want to maintain consistent knowledge base information across multiple languages"],"best_for":["global e-commerce companies with international customer bases","SaaS platforms serving multiple language markets","companies wanting to expand to new markets without hiring local support teams"],"limitations":["Translation quality varies by language pair — less common languages may have poor translation quality","Translation adds ~100-300ms latency per message (round-trip translation)","Idioms, cultural references, and domain-specific terminology may not translate well, leading to confusing responses","Knowledge base translation is not automatic — requires manual translation or external translation service","No built-in language-specific knowledge base variants — same information served to all languages regardless of local relevance"],"requires":["Translation API credentials (Google Translate, DeepL, or equivalent)","Multilingual LLM or separate language-specific models","Browser language detection (standard browser feature)"],"input_types":["visitor browser language preference","user messages in any supported language","knowledge base documents (single or multiple languages)"],"output_types":["chatbot interface in visitor's language","responses translated to visitor's language","conversation history in original language"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Website with HTML/JavaScript support (any CMS or static site)","Active internet connection for both website and visitors","Automatic Chat account with valid API credentials","No specific browser version requirements (ES5+ JavaScript support)","Structured knowledge base in supported format (markdown, PDF, plain text, or web-crawlable URLs)","Regular knowledge base maintenance process to keep documentation current","API credentials for Automatic Chat with knowledge base upload permissions","Browser with localStorage or equivalent session storage support","Backend session management infrastructure (typically Redis or in-memory store)","LLM with sufficient context window to accommodate conversation history + knowledge base context + system prompt"],"failure_modes":["Widget injection adds ~50-100ms to initial page load due to JavaScript parsing and DOM manipulation","No built-in offline mode — requires active internet connection to function","Limited to text-based interactions; no native support for file uploads, image analysis, or voice input","Session state stored in browser localStorage is vulnerable to XSS attacks if not properly sandboxed","Context window limited by backend LLM constraints — cannot maintain coherent conversations beyond ~20 turns without summarization","Retrieval quality depends on knowledge base quality — garbage in, garbage out; poorly written or outdated docs will produce poor responses","Semantic search may fail for domain-specific jargon or acronyms not well-represented in training data","No automatic knowledge base versioning — updating docs can break responses that relied on previous versions","Embedding generation adds ~100-500ms latency per query depending on document count and embedding model size","Maximum knowledge base size likely constrained by vector database capacity and query latency requirements (typically <10GB uncompressed text)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"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":"2026-05-24T12:16:29.133Z","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=automatic-chat","compare_url":"https://unfragile.ai/compare?artifact=automatic-chat"}},"signature":"7r2KuNF08gv5cLkV0wW1NV3DqvYACT1PyRQyeZh91KsHwGx8Fc79HDWj33eh2XwgpexUL27CSMH+4RukbTfWDA==","signedAt":"2026-06-23T02:07:16.994Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/automatic-chat","artifact":"https://unfragile.ai/automatic-chat","verify":"https://unfragile.ai/api/v1/verify?slug=automatic-chat","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"}}