{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_yourgpt","slug":"yourgpt","name":"YourGPT","type":"product","url":"https://yourgpt.ai","page_url":"https://unfragile.ai/yourgpt","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_yourgpt__cap_0","uri":"capability://memory.knowledge.multi.source.knowledge.base.ingestion.with.automatic.reindexing","name":"multi-source knowledge base ingestion with automatic reindexing","description":"Ingests training data from heterogeneous sources (websites via URL/sitemap crawling, PDFs, Word docs, CSVs, Notion links, YouTube videos, raw text) and stores them in a RAG-compatible vector index. The 'Auto ReIndex' feature monitors source content for changes and automatically updates the knowledge base without manual re-upload, enabling dynamic knowledge synchronization. Implementation uses document chunking and embedding generation (model unspecified) to support semantic retrieval during conversation.","intents":["I want to train my chatbot on our help center, product docs, and FAQ without manually copying text","I need my chatbot's knowledge to stay current when we update our website or documentation","I want to support multiple document formats (PDF, Word, CSV) without converting them manually","I need to include video content (YouTube) as training material for my support bot"],"best_for":["E-commerce businesses with existing help centers and product documentation","SaaS companies managing rapidly-evolving knowledge bases","Agencies managing multiple client chatbots with different knowledge sources","Organizations with multilingual documentation across multiple platforms"],"limitations":["Knowledge base size capped by plan tier: Essential (200 webpages + 20 documents), Professional (500 webpages + 100 documents), Advanced (2,000 webpages + 500 documents)","Auto reindex frequency not specified — may not catch real-time changes immediately","YouTube video ingestion mechanism unknown — likely transcription-based but no confirmation of accuracy or latency","No built-in deduplication or conflict resolution when same content exists in multiple sources","CSV/structured data handling mechanism unknown — may not preserve schema relationships"],"requires":["Valid URLs for website/sitemap crawling (must be publicly accessible)","File uploads up to size limit (unspecified)","Notion workspace access token for document linking","YouTube video URLs (public or unlisted)","Professional+ tier for API/webhook access to programmatically manage knowledge base"],"input_types":["website URLs","XML sitemaps","PDF files","DOCX (Word) files","PPTX (PowerPoint) files","CSV files","TXT files","Markdown files","Notion document links","YouTube video URLs","raw text input"],"output_types":["vector embeddings (stored internally)","indexed knowledge base (queryable via chat)","retrieved context chunks (injected into LLM prompts)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_yourgpt__cap_1","uri":"capability://planning.reasoning.no.code.conversational.flow.builder.with.sequential.multi.step.agents","name":"no-code conversational flow builder with sequential multi-step agents","description":"Provides a visual drag-and-drop interface for designing multi-turn conversation flows without writing code. Flows support sequential step execution, intent detection (classifying user queries), conditional branching, form capture, API calls to external services, and custom code execution within steps. Each step can trigger actions (send message, call API, execute code) and route to subsequent steps based on conditions, enabling complex conversation logic without backend development.","intents":["I want to build a multi-step conversation flow (e.g., collect email → verify → send confirmation) without coding","I need to route conversations based on user intent (e.g., 'billing question' → billing flow, 'technical issue' → support flow)","I want to call external APIs from within my chatbot flow (e.g., look up order status, create ticket)","I need to capture structured data (forms, fields) during conversation and store it"],"best_for":["Non-technical business users building simple to moderately complex support flows","Agencies rapidly prototyping chatbots for multiple clients","Teams without dedicated backend engineers who need custom conversation logic","Businesses automating lead qualification or data collection workflows"],"limitations":["Advanced use cases (complex conditional logic, nested loops, state machines) may require custom code execution, reducing no-code advantage","Flow export/import format is proprietary — cannot easily migrate flows to other platforms","No built-in version control or rollback for flow changes","Debugging flow execution requires manual inspection via 'Flow Logger' — no breakpoints or step-through debugging","Custom code execution mechanism and supported languages unknown","No A/B testing or multivariate testing of flow variants","Concurrent flow execution limits unknown — may bottleneck under high load"],"requires":["Professional+ tier (Essential tier does not include Chatbot Studio/flow builder)","Basic understanding of conversation design and user intent classification","API documentation for any external services you want to integrate","For custom code: knowledge of supported language (likely JavaScript/Python, unconfirmed)"],"input_types":["user text messages","user intent classification results","form field inputs","API response data","conditional logic expressions"],"output_types":["text responses","form prompts","API call requests","custom code execution results","routing decisions (next step in flow)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_yourgpt__cap_10","uri":"capability://tool.use.integration.rest.api.and.webhook.support.for.custom.integrations","name":"rest api and webhook support for custom integrations","description":"Exposes REST API endpoints (Professional+ tier) and webhook support for programmatic chatbot management, conversation triggering, and event handling. Developers can create custom integrations beyond the pre-built channel connectors, automate chatbot configuration, or build custom workflows that respond to external events. Webhook payloads include conversation context, allowing external systems to react to chatbot events.","intents":["I want to trigger chatbot conversations programmatically from my application","I need to build a custom integration with a system that's not in the pre-built list","I want to receive webhooks when conversations reach certain milestones (escalation, lead capture)","I need to automate chatbot configuration and management via API"],"best_for":["Developers building custom integrations beyond pre-built channels","Teams with proprietary systems requiring chatbot integration","Agencies building white-label solutions on top of YourGPT","Organizations automating chatbot deployment and configuration"],"limitations":["REST API and webhook support available only on Professional+ tier","API documentation not provided in architectural analysis — endpoints and schema unknown","Rate limiting and quota policies not specified","No mention of API versioning or backward compatibility guarantees","Webhook retry logic and delivery guarantees unknown","Custom integration development available only on Enterprise tier","No SDK provided — developers must build HTTP clients manually","Authentication mechanism (API keys, OAuth) not specified"],"requires":["Professional+ tier for REST API and webhook access","Developer knowledge of REST APIs and webhook handling","API documentation (not provided in analysis)","Enterprise tier for custom integration development support"],"input_types":["API requests (POST, GET, PUT, DELETE)","webhook event payloads","conversation context and metadata"],"output_types":["API responses (JSON)","webhook events (conversation escalation, lead capture, etc.)","chatbot configuration updates"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_yourgpt__cap_11","uri":"capability://memory.knowledge.self.learning.capability.with.automatic.knowledge.refinement","name":"self-learning capability with automatic knowledge refinement","description":"Claims a 'Self Learning' feature that automatically refines the chatbot's knowledge base and response quality based on conversation outcomes. Implementation mechanism unknown, but likely involves tracking which responses were marked as helpful/unhelpful by users or agents, and using that feedback to adjust response generation or knowledge base weighting. May also involve automatic intent detection improvement based on conversation patterns.","intents":["I want my chatbot to improve over time without manually updating the knowledge base","I need feedback from conversations to automatically refine response quality","I want the chatbot to learn new intents from user interactions","I need to track which responses are most helpful and prioritize them"],"best_for":["Teams with high conversation volume that can provide training signal","Businesses wanting continuous improvement without manual intervention","Organizations with evolving knowledge bases that change frequently"],"limitations":["Self-learning mechanism completely unspecified — unclear how it works or what triggers learning","No mention of feedback mechanisms (user ratings, agent corrections, etc.)","No control over learning parameters or ability to disable learning","Risk of learning from incorrect feedback or biased data","No visibility into what the chatbot has learned or how knowledge has changed","Potential for learning to degrade response quality if feedback is poor","No mention of human review or approval before applying learned changes","Learning may be limited to Professional+ tier (feature tier not specified)"],"requires":["Active conversations with feedback signals (mechanism unknown)","Sufficient conversation volume to generate meaningful learning signal","Trust in automated learning without manual review"],"input_types":["conversation outcomes","user feedback (mechanism unknown)","agent corrections or approvals"],"output_types":["refined knowledge base","improved response generation","new intent detection"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_yourgpt__cap_12","uri":"capability://text.generation.language.message.rewriting.and.response.rephrasing","name":"message rewriting and response rephrasing","description":"Provides tools to rewrite or rephrase chatbot responses before sending, allowing agents or administrators to adjust tone, clarity, or content. Likely includes templates or suggestion mechanisms to help craft better responses. May also support automatic rephrasing to match brand voice or tone guidelines.","intents":["I want to adjust a chatbot response before it's sent to match our brand voice","I need to rephrase responses to be more friendly or professional","I want to correct factual errors in responses before they reach customers","I need to ensure responses comply with company policies or legal requirements"],"best_for":["Support teams with quality standards for customer-facing responses","Businesses with strict brand voice guidelines","Organizations requiring compliance review before customer communication","Teams training chatbots and needing to correct responses in real-time"],"limitations":["Message rewriting mechanism not detailed — unclear if it's manual editing or AI-assisted","No mention of response templates or suggestion mechanisms","Unclear if rewriting happens before or after message is sent","No version history or audit trail for message changes","No mention of tone detection or automatic tone adjustment","Rewriting capability may be limited to certain user roles","No integration with brand voice guidelines or style guides"],"requires":["Access to conversation interface with message editing capability","User role with permission to edit messages","Understanding of desired tone and messaging guidelines"],"input_types":["original chatbot response","user editing input","tone or style preferences"],"output_types":["rewritten response","edited message sent to customer"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_yourgpt__cap_13","uri":"capability://text.generation.language.canned.replies.and.response.template.management","name":"canned replies and response template management","description":"Allows creation and management of pre-written response templates ('canned replies') that agents can quickly insert into conversations. Templates can include variables (e.g., {{customer_name}}, {{order_id}}) that are automatically populated from conversation context. Reduces response time for common questions and ensures consistency across support team.","intents":["I want my support team to quickly respond to common questions using templates","I need to ensure consistent messaging across all support conversations","I want to reduce response time by pre-writing common responses","I need to personalize templates with customer data (name, order ID, etc.)"],"best_for":["Support teams handling high volume of repetitive inquiries","Organizations with strict messaging standards","Teams wanting to reduce response time and improve consistency","Businesses with complex responses that benefit from pre-written templates"],"limitations":["Template management interface not detailed","No mention of template categories or search functionality","Variable substitution mechanism not specified — unclear what variables are available","No version control or template change history","No A/B testing of template variants","Template sharing across team members not mentioned","No analytics on template usage or effectiveness","Limited to text responses — no rich formatting or media templates"],"requires":["Access to template management interface","User role with permission to create/edit templates","Understanding of common customer questions and responses"],"input_types":["template text with variables","conversation context for variable substitution"],"output_types":["personalized response with variables filled in","sent message to customer"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_yourgpt__cap_14","uri":"capability://automation.workflow.internal.team.notes.and.conversation.metadata","name":"internal team notes and conversation metadata","description":"Allows support agents and team members to add internal notes to conversations that are visible only to the team, not to customers. Notes are preserved in conversation history and visible during human handoff, providing context for agents taking over from the chatbot. Metadata (tags, priority, department) can be attached to conversations for organization and routing.","intents":["I want to leave notes for other team members about a conversation without the customer seeing them","I need to track conversation metadata (priority, department, tags) for organization","I want agents taking over from the chatbot to see internal notes about what was already tried","I need to document decisions or context for future reference"],"best_for":["Support teams with multiple agents handling same conversations","Organizations with complex conversation workflows requiring context sharing","Teams needing to track conversation metadata for analytics or routing","Businesses with compliance requirements for conversation documentation"],"limitations":["Internal notes interface not detailed","No mention of note permissions or role-based visibility","No mention of note search or filtering","No version history for note edits","Metadata schema not specified — unclear what tags or fields are available","No integration with external documentation systems","Notes may not be exported or archived separately from conversations"],"requires":["User role with permission to add internal notes","Access to conversation interface","Team coordination on note format and content"],"input_types":["free-form text notes","metadata tags and fields"],"output_types":["internal notes visible to team","metadata attached to conversation","notes visible during human handoff"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_yourgpt__cap_15","uri":"capability://data.processing.analysis.conversation.transcript.export.and.email.delivery","name":"conversation transcript export and email delivery","description":"Allows export of conversation transcripts in email-friendly format and automatic delivery via email to specified recipients. Transcripts include full conversation history, internal notes, and metadata. Useful for compliance, record-keeping, or sharing conversation context with external parties.","intents":["I want to email a conversation transcript to a customer for their records","I need to archive conversation transcripts for compliance or legal purposes","I want to share conversation context with team members who weren't involved","I need to export conversations for external analysis or reporting"],"best_for":["Organizations with compliance or legal requirements for conversation records","Support teams needing to share conversation context with customers or external parties","Businesses archiving conversations for historical reference","Teams exporting conversations for external analysis or reporting"],"limitations":["Export format not specified — unclear if PDF, HTML, plain text, or other","No mention of selective export (e.g., exclude internal notes from customer-facing exports)","Email delivery mechanism not detailed — unclear if automatic or manual","No mention of export scheduling or batch export","No integration with external archival systems","Export may not preserve formatting or media attachments","No encryption or security controls for exported transcripts mentioned"],"requires":["Access to conversation interface with export capability","Email address for transcript delivery","User role with permission to export conversations"],"input_types":["conversation history","internal notes and metadata","export format preferences"],"output_types":["transcript file (format unspecified)","email with transcript attachment"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_yourgpt__cap_16","uri":"capability://text.generation.language.interactive.rich.messages.with.cards.buttons.and.carousels","name":"interactive rich messages with cards, buttons, and carousels","description":"Supports creation of rich message formats beyond plain text, including interactive buttons, card layouts, carousels (scrollable lists), and form elements. These rich messages render natively on supported channels (Slack, Discord, WhatsApp, etc.) with platform-specific formatting. Users can click buttons to trigger actions (navigate flow, call API, submit form) without typing.","intents":["I want to present multiple options as clickable buttons instead of asking users to type","I need to display product cards or information in a visually appealing format","I want to create a carousel of items for users to browse and select","I need to collect structured form data without typing long responses"],"best_for":["E-commerce chatbots presenting product options","Support chatbots offering multiple resolution paths","Lead generation chatbots collecting structured data","Businesses wanting to improve user experience with visual interfaces"],"limitations":["Rich message support varies by channel — not all platforms support all formats","Fallback behavior for unsupported channels not specified","Rich message builder interface not detailed","No mention of custom styling or advanced layout options","Button action types not fully specified (likely limited to flow navigation, API calls, form submission)","Carousel size limits not mentioned","No analytics on button click rates or user interaction patterns","Media attachments (images, videos) in rich messages not mentioned"],"requires":["Integration with channel that supports rich messages (Slack, Discord, WhatsApp, etc.)","Flow builder access to design rich message layouts","Understanding of channel-specific formatting constraints"],"input_types":["button labels and actions","card content and layout","carousel items","form field definitions"],"output_types":["rendered rich messages on channel","button click events","form submission data"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_yourgpt__cap_2","uri":"capability://automation.workflow.real.time.human.handoff.with.conversation.context.preservation","name":"real-time human handoff with conversation context preservation","description":"Detects when a conversation requires human intervention (via explicit user request, unresolved intent, or escalation trigger) and seamlessly transfers the chat to a live agent while preserving full conversation history, internal notes, and extracted user data. The handoff mechanism maintains context across the AI-to-human boundary, allowing agents to see what the bot attempted and why it escalated, reducing repetition and improving resolution time.","intents":["I want my chatbot to escalate complex issues to human agents without losing conversation history","I need agents to see what the bot already tried so they don't repeat questions","I want to add internal notes during a conversation that only agents can see","I need to route conversations to specific departments or agents based on issue type"],"best_for":["Support teams using hybrid AI+human workflows","Businesses with variable support load (AI handles routine, humans handle exceptions)","Organizations with department-specific expertise (billing, technical, sales)","High-volume support operations where reducing agent context-switching saves time"],"limitations":["Escalation triggers and routing logic not fully specified — unclear how 'unresolved intent' is detected","No mention of queue management, wait times, or agent availability checks before handoff","Department-based routing available only on Advanced+ tier","No SLA or guaranteed response time for human agents","Conversation history retention period unknown — may be limited by data retention policy","No indication of sentiment-based escalation (e.g., escalate if customer frustration detected)","Multi-agent assignment or round-robin distribution not mentioned"],"requires":["At least one human agent account configured in YourGPT workspace","Integration with communication channel (Slack, Discord, Intercom, etc.) for agent notification","Advanced+ tier for department-based routing","Clear escalation rules defined in flow (explicit handoff trigger or intent-based routing)"],"input_types":["user escalation request (explicit)","unresolved conversation state","intent classification result","conversation history","extracted user data (email, name, issue type)"],"output_types":["handoff notification to agent","conversation transcript with context","internal notes and metadata","routed conversation in agent interface"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_yourgpt__cap_3","uri":"capability://tool.use.integration.omni.channel.message.delivery.with.platform.native.integrations","name":"omni-channel message delivery with platform-native integrations","description":"Routes chatbot responses to multiple communication channels (Slack, Discord, WhatsApp, Facebook Messenger, Instagram, Telegram, Line, Intercom, Crisp) using native API integrations. Each channel integration handles platform-specific formatting (rich cards, buttons, carousels, file attachments) and maintains separate conversation threads. Users can interact with the same chatbot across channels while maintaining independent conversation state per channel.","intents":["I want my chatbot to respond to customers on WhatsApp, Facebook, and Slack simultaneously","I need to send rich messages (cards, buttons, carousels) that render correctly on each platform","I want to collect customer inquiries from multiple channels into a single support queue","I need to support file attachments and media across different messaging platforms"],"best_for":["Businesses with customers across multiple messaging platforms (e.g., B2C e-commerce using WhatsApp + Facebook)","Agencies managing chatbots for clients with diverse channel preferences","Global organizations supporting customers on region-specific platforms (e.g., Line in Japan, WeChat in China)","Teams consolidating support across Slack (internal) and external customer channels"],"limitations":["Limited to 8 pre-integrated channels — no custom channel adapters mentioned","Platform-specific features may not be fully supported (e.g., WhatsApp template messages, Instagram Stories)","Conversation state is per-channel — user history on WhatsApp doesn't carry to Slack","Rich message formatting (cards, carousels) support varies by platform; fallback behavior unknown","No mention of channel-specific rate limiting or quota management","Webhook/API access for custom channel integration available only on Enterprise tier","Message delivery guarantees and retry logic not specified"],"requires":["Active account/workspace on each channel platform (WhatsApp Business Account, Facebook Page, Slack workspace, etc.)","API credentials or OAuth tokens for each channel","Professional+ tier for REST API and webhook access","Enterprise tier for custom channel development"],"input_types":["text messages from any integrated channel","media files (images, documents) from channels that support them","button clicks and interactive elements","voice messages (if channel supports)"],"output_types":["text responses formatted for each channel","rich cards and carousels (platform-native format)","buttons and interactive elements","file attachments","voice responses (if channel supports)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_yourgpt__cap_4","uri":"capability://text.generation.language.real.time.translation.across.100.languages","name":"real-time translation across 100+ languages","description":"Automatically detects user message language and translates both incoming messages and outgoing responses in real-time, supporting 100+ languages. Translation occurs transparently within the conversation flow — the chatbot responds in the user's language without explicit language selection. Implementation likely uses a third-party translation API (Google Translate, Azure Translator, or similar) integrated into the message processing pipeline.","intents":["I want my chatbot to serve customers in their native language without building separate language-specific bots","I need automatic language detection so users don't have to select a language","I want to support multilingual customer bases without translating training data for each language","I need real-time translation for conversations with international customers"],"best_for":["Global e-commerce businesses serving customers across multiple countries","SaaS platforms with international user bases","Agencies managing chatbots for multinational clients","Organizations expanding into new geographic markets without localization overhead"],"limitations":["Translation quality depends on underlying API (likely Google Translate or similar) — may struggle with domain-specific terminology, slang, or context-dependent meaning","No mention of custom translation models or glossaries for industry-specific terms","Bidirectional translation (user message → bot response) adds latency — exact overhead unknown","Language detection may fail for code-mixed messages (e.g., 'Hola, can you help?')","No option to disable translation for specific languages or use human-translated content","Translation adds per-message cost (if using paid translation API) — pricing impact unknown","RTL (right-to-left) language support and rendering not explicitly mentioned"],"requires":["No explicit configuration required — automatic language detection","Underlying translation API (Google Translate, Azure, etc.) must be accessible","Training data in at least one language (bot will translate responses to user's language)"],"input_types":["text messages in any of 100+ supported languages","language-mixed messages (may have reduced accuracy)"],"output_types":["translated responses in user's detected language","language-detected metadata (for logging/analytics)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_yourgpt__cap_5","uri":"capability://text.generation.language.multi.model.llm.selection.with.credit.based.consumption.pricing","name":"multi-model llm selection with credit-based consumption pricing","description":"Allows users to select from multiple LLM providers and models (GPT-3.5, GPT-4o-mini, GPT-4o, GPT-4 Turbo, GPT-4, o1) on a per-chatbot or per-conversation basis. Each model has a relative cost multiplier (GPT-3.5: 1x, GPT-4o-mini: 0.5x, GPT-4o: 5x, GPT-4 Turbo: 10x, GPT-4: 20x) applied to a monthly AI credit budget. Users can optimize cost vs. quality by selecting cheaper models for simple queries and expensive models for complex reasoning.","intents":["I want to use GPT-4 for complex reasoning but GPT-3.5 for simple FAQ responses to save costs","I need to understand how much each conversation costs in terms of AI credits","I want to switch models based on conversation complexity or user tier","I need to stay within a monthly AI budget while maximizing response quality"],"best_for":["Cost-conscious teams optimizing AI spend across multiple chatbots","Businesses with variable query complexity (simple FAQ + complex troubleshooting)","Organizations experimenting with different models to find quality/cost tradeoffs","Teams managing multiple chatbots with different quality requirements"],"limitations":["Monthly AI credit limits by tier: Essential (10M), Professional (30M), Advanced (100M) — overage pricing unknown","No real-time cost tracking or credit usage alerts mentioned","Model selection is static per chatbot — no dynamic routing based on query complexity","Credit-to-USD conversion rate not published — unclear actual cost per model","No cost estimation before sending a message — users can't preview credit cost","Credit rollover policy unknown — unused credits may expire monthly","No cost optimization recommendations or analytics","Limited to OpenAI models — no support for Anthropic Claude, Llama, or open-source models"],"requires":["Professional+ tier for GPT-4 access (Essential tier limited to GPT-3.5)","Advanced+ tier for o1 model access","Understanding of model capabilities and cost tradeoffs","Monthly budget planning based on estimated conversation volume"],"input_types":["model selection (per chatbot configuration)","conversation context and user message"],"output_types":["LLM response from selected model","AI credit consumption (not visible to users)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_yourgpt__cap_6","uri":"capability://data.processing.analysis.lead.generation.and.qualified.lead.extraction.from.conversations","name":"lead generation and qualified lead extraction from conversations","description":"Automatically extracts structured lead data (email, name, company, phone, issue type, etc.) from conversation context and routes qualified leads to sales teams. The extraction mechanism likely uses intent detection and entity recognition to identify when a conversation contains lead-qualifying signals (e.g., 'I'm interested in pricing' or 'Can you demo the product?'). Extracted leads can be exported or integrated with CRM systems via Zapier/Pabbly.","intents":["I want to automatically capture lead information from support conversations without asking explicit forms","I need to identify sales-qualified leads from chatbot conversations and route them to sales","I want to export leads to my CRM (HubSpot, Salesforce, etc.) automatically","I need to track which conversations converted to leads"],"best_for":["SaaS companies using chatbots for both support and lead generation","E-commerce businesses identifying upsell/cross-sell opportunities","Agencies managing chatbots that need to generate leads for clients","Sales teams wanting to capture intent signals from support conversations"],"limitations":["Lead qualification criteria not specified — unclear what signals trigger lead extraction","Entity extraction accuracy depends on conversation clarity — may miss implicit leads","No mention of lead scoring or qualification confidence levels","Requires Professional+ tier — not available on Essential plan","Lead export format and CRM field mapping not detailed","No built-in lead deduplication — may create duplicate records if same user contacts multiple times","No lead follow-up automation or nurture workflows mentioned","Integration limited to Zapier/Pabbly — no native CRM connectors"],"requires":["Professional+ tier for lead generation feature","CRM or lead database to export leads to (via Zapier/Pabbly integration)","Clear conversation design that naturally captures lead information","Zapier or Pabbly account for CRM integration"],"input_types":["conversation messages","user intent classification","extracted entities (email, name, company, phone)"],"output_types":["structured lead data (JSON or CSV)","lead export to CRM via Zapier/Pabbly","lead analytics and conversion tracking"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_yourgpt__cap_7","uri":"capability://data.processing.analysis.conversation.analytics.and.user.engagement.tracking","name":"conversation analytics and user engagement tracking","description":"Provides dashboards tracking conversation metrics (volume, resolution rate, user satisfaction, response time), user activity patterns (frequency, channels, topics), and engagement metrics. Analytics can be filtered by location, time period, and conversation type. Implementation likely aggregates conversation logs and metadata into a time-series database for visualization and trend analysis.","intents":["I want to see how many conversations my chatbot handled and what percentage were resolved","I need to identify which topics or questions are most common","I want to track user engagement and identify power users or at-risk customers","I need to measure chatbot performance over time and identify improvement areas"],"best_for":["Support teams measuring chatbot ROI and performance","Product managers identifying customer pain points from conversation data","Businesses optimizing chatbot training based on conversation patterns","Teams tracking customer engagement and churn signals"],"limitations":["Analytics dashboard features not detailed — unclear what metrics are available","No mention of custom metrics or event tracking","Location-based filtering available only on Advanced+ tier","No real-time analytics — likely batch-processed with delay","No sentiment analysis mentioned despite 'user activity tracking' claim","No conversation export for external analysis or compliance audits","Data retention period unknown — may be limited by plan tier","No A/B testing or multivariate analysis of conversation variants"],"requires":["Active conversations to generate analytics data","Advanced+ tier for location-based filtering","Access to YourGPT analytics dashboard"],"input_types":["conversation logs and metadata","user interaction events","channel and timestamp data"],"output_types":["conversation volume metrics","resolution rate and satisfaction scores","user engagement trends","topic/intent distribution","location-based analytics (Advanced+ tier)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_yourgpt__cap_8","uri":"capability://planning.reasoning.flow.debugging.and.conversation.path.logging","name":"flow debugging and conversation path logging","description":"Provides a 'Flow Logger' tool that records the execution path of each conversation through the chatbot's workflow, showing which steps were executed, what conditions were evaluated, and what data was passed between steps. Developers can inspect failed or unexpected conversations to identify logic errors, missing intents, or data extraction issues without accessing backend logs.","intents":["I want to debug why a conversation took an unexpected path through my flow","I need to see what data was extracted at each step of the conversation","I want to identify which intent detection failed and why","I need to troubleshoot API calls or custom code execution within flows"],"best_for":["Developers building complex multi-step conversation flows","Teams troubleshooting chatbot behavior without backend access","QA teams testing conversation logic before production","Support teams diagnosing customer issues with specific conversation paths"],"limitations":["Flow Logger interface and capabilities not detailed","No mention of breakpoints, step-through debugging, or conditional breakpoints","Logs likely retained only for recent conversations — historical debugging may not be possible","No integration with external debugging tools or log aggregation platforms","Custom code execution errors may not be fully visible in logs","No performance profiling — can't identify slow steps or bottlenecks","Logs may not capture all state transitions or intermediate values"],"requires":["Professional+ tier (flow builder access)","Access to YourGPT dashboard and Flow Logger tool","Understanding of conversation flow design and intent detection"],"input_types":["conversation execution trace","step-by-step execution log","condition evaluation results","data passed between steps"],"output_types":["visual flow execution path","step-by-step log entries","extracted data and variables","error messages and exceptions"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_yourgpt__cap_9","uri":"capability://automation.workflow.white.label.customization.with.custom.domain.support","name":"white-label customization with custom domain support","description":"Removes YourGPT branding ('Powered by YourGPT' footer) and allows custom domain configuration (e.g., chat.mycompany.com instead of yourgpt.ai/chat). Widget appearance can be customized (colors, fonts, logo) to match brand identity. 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