{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_chatnbx","slug":"chatnbx","name":"ChatNBX","type":"product","url":"https://chat.nbox.ai","page_url":"https://unfragile.ai/chatnbx","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_chatnbx__cap_0","uri":"capability://automation.workflow.cross.device.conversation.synchronization.with.context.preservation","name":"cross-device conversation synchronization with context preservation","description":"Maintains real-time conversation state and message history across web, mobile, and desktop clients through a centralized message store with device-agnostic session management. Uses WebSocket connections for live updates and local caching layers to ensure seamless context switching when users move between devices without losing conversation position, unread markers, or draft messages. The architecture appears to employ a conflict-resolution strategy for concurrent edits and a unified notification queue to prevent duplicate alerts across devices.","intents":["I need my support team to switch from desktop to mobile mid-conversation without losing context or having to re-read messages","I want draft responses and typing indicators to sync instantly across all my devices","I need to ensure that when a team member picks up a conversation on their phone, they see the exact same state as on their desktop"],"best_for":["distributed support teams working across multiple devices","mobile-first customer support operations","organizations requiring seamless handoffs between devices without context loss"],"limitations":["Synchronization latency may increase under high message volume (>500 concurrent conversations) due to centralized state management","Offline-first sync requires local storage capacity; mobile clients may struggle with very large conversation histories (>10k messages per thread)","No explicit mention of conflict resolution strategy for simultaneous edits from multiple devices on same message"],"requires":["Active internet connection for real-time sync (WebSocket support required)","Modern browser with localStorage/IndexedDB support for mobile clients","Account authentication with session token management"],"input_types":["text messages","message metadata (timestamps, read status, draft state)","device identifiers"],"output_types":["synchronized conversation state","real-time message updates","device-specific UI state (scroll position, unread count)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chatnbx__cap_1","uri":"capability://text.generation.language.ai.assisted.response.suggestion.generation.for.support.conversations","name":"ai-assisted response suggestion generation for support conversations","description":"Analyzes incoming customer messages and conversation history using a language model to generate contextually-relevant response suggestions that support agents can accept, edit, or reject. The system appears to use conversation embeddings and message classification to determine suggestion relevance, with a feedback loop that allows agents to rate suggestion quality. Suggestions are generated asynchronously to avoid blocking the agent UI, and the model likely fine-tuned or prompted with domain-specific support patterns to reduce generic outputs.","intents":["I want my support agents to respond 30-40% faster by getting AI-suggested replies they can quickly approve or modify","I need to reduce response time variance across my support team by providing consistent, high-quality starting points for replies","I want to capture common support scenarios and let AI generate appropriate responses based on conversation context"],"best_for":["support teams handling high-volume, repetitive inquiries (billing, password resets, FAQ-style questions)","organizations with 5-50 support agents where consistency matters but full automation isn't appropriate","teams seeking to reduce agent cognitive load without replacing human judgment"],"limitations":["AI models generate occasionally generic or off-topic suggestions requiring significant human oversight in complex support scenarios (per editorial summary)","Suggestion quality degrades on novel or highly specialized customer issues outside training distribution","No explicit mention of multi-language support; likely optimized for English-language conversations","Feedback loop for suggestion improvement may require manual annotation; no self-improving mechanism mentioned"],"requires":["Active conversation history (minimum 2-3 messages for context)","API access to underlying LLM (likely proprietary or third-party like OpenAI)","Support agent to review and approve suggestions before sending"],"input_types":["customer message text","conversation history (full thread)","conversation metadata (customer type, issue category if available)"],"output_types":["text suggestion (1-3 candidate responses)","confidence score or relevance ranking","suggestion metadata (category, tone)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chatnbx__cap_10","uri":"capability://automation.workflow.team.collaboration.and.internal.notes.within.conversations","name":"team collaboration and internal notes within conversations","description":"Allows support agents to add internal notes or comments to conversations visible only to team members, enabling collaboration on complex issues without exposing internal discussion to customers. Internal notes are likely stored separately from customer-facing messages, with different access controls and visibility rules. The system may support @mentions to notify specific team members of internal notes, creating a collaboration workflow within the conversation context.","intents":["I want to ask my team lead for advice on a customer issue without the customer seeing our internal discussion","I need to document my troubleshooting steps and findings so the next agent can pick up where I left off","I want to @mention a colleague to get their input on a technical issue without creating a separate Slack thread"],"best_for":["support teams handling complex issues requiring collaboration","organizations wanting to reduce context switching between chat tools and support platform","teams needing to document internal decision-making for knowledge sharing"],"limitations":["Internal notes are not visible to customers; no mention of note summarization for customer-facing resolution explanation","@mention notifications may create alert fatigue if overused; no mention of notification throttling or batching","No mention of note threading or organization; internal notes may become cluttered and hard to follow","Internal notes are not searchable separately from customer messages; may pollute search results"],"requires":["Team member role with permission to view internal notes","Conversation access (agent must be assigned or have team access)","Internal note creation permission"],"input_types":["internal note text","@mention targets (team member IDs)","note metadata (timestamp, author)"],"output_types":["internal note stored in conversation","notification to @mentioned team members","internal note visible only to authorized team members"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chatnbx__cap_2","uri":"capability://automation.workflow.unified.team.and.customer.communication.channel.management","name":"unified team and customer communication channel management","description":"Provides a single interface for managing both internal team conversations and external customer support threads, routing messages to appropriate channels based on conversation type (internal vs. customer-facing) and participant roles. The system likely uses role-based access control (RBAC) to determine visibility and permissions, with separate message queues or channel partitions for team vs. customer conversations. Internal team discussions can reference or escalate to customer conversations without exposing internal context to customers.","intents":["I want my support team to discuss customer issues internally without the customer seeing our notes or disagreements","I need to seamlessly escalate a customer conversation to my team lead while keeping the customer in the loop on resolution","I want one unified inbox for both team collaboration and customer support instead of switching between Slack and a support tool"],"best_for":["small to mid-sized teams (5-50 people) handling both internal collaboration and customer support","organizations wanting to consolidate communication tools and reduce context switching","support teams that need internal discussion capability without exposing backend processes to customers"],"limitations":["Unified interface may create confusion about message visibility; no explicit mention of visual indicators for internal vs. customer-facing messages","Role-based access control complexity increases with team size; likely requires manual permission management rather than auto-inheritance","Limited integrations with existing team chat tools (Slack, Teams) means teams may still need multiple tools for full workflow","No mention of conversation threading or topic-based organization; may become cluttered with mixed internal/external messages"],"requires":["User account with defined role (agent, team lead, admin)","Conversation participant list with role assignments","Active team and customer accounts"],"input_types":["text messages","participant list with roles","conversation type designation (internal vs. customer)"],"output_types":["filtered message feed based on user role","conversation routing decisions","access control decisions"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chatnbx__cap_3","uri":"capability://automation.workflow.real.time.message.delivery.and.notification.routing.across.channels","name":"real-time message delivery and notification routing across channels","description":"Delivers messages to intended recipients with low latency using a pub-sub or message queue architecture (likely Redis or similar), with intelligent notification routing that respects user preferences, device state, and do-not-disturb settings. The system batches notifications to prevent alert fatigue, deduplicates across devices, and likely uses exponential backoff for delivery retries. Notifications are routed to appropriate channels (push, email, in-app) based on user configuration and message priority.","intents":["I want my support agents to get instant notifications when a customer replies without being spammed with duplicate alerts across devices","I need to ensure urgent customer issues trigger immediate notifications while routine messages respect my do-not-disturb schedule","I want notifications delivered to my phone if I'm away from my desk, but not if I'm actively using the desktop app"],"best_for":["support teams requiring sub-second message delivery for time-sensitive customer issues","organizations with distributed teams across time zones needing smart notification routing","teams wanting to reduce notification fatigue while ensuring critical messages get through"],"limitations":["Notification deduplication across devices adds latency; may result in 1-3 second delay before notification appears on all devices","Do-not-disturb logic requires accurate device state tracking; may fail if user switches devices rapidly","Email notification delivery depends on third-party SMTP provider; no SLA mentioned for email delivery times","No mention of notification prioritization levels; all messages may be treated equally in queue"],"requires":["Push notification service integration (APNs for iOS, FCM for Android)","SMTP provider for email notifications","User notification preferences configured","Active device registration with push tokens"],"input_types":["message object with priority level","recipient user ID","notification preferences (channels, do-not-disturb schedule)","device state (active, idle, offline)"],"output_types":["push notification","email notification","in-app notification","delivery confirmation"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chatnbx__cap_4","uri":"capability://search.retrieval.conversation.search.and.retrieval.with.message.indexing","name":"conversation search and retrieval with message indexing","description":"Indexes all messages and conversation metadata using full-text search (likely Elasticsearch or similar) to enable fast retrieval of past conversations by keyword, participant, date range, or conversation status. The search likely supports boolean operators and filters, with results ranked by relevance and recency. Indexing happens asynchronously to avoid blocking message ingestion, and the system maintains separate indices for team vs. customer conversations to respect access control during search.","intents":["I need to find a previous conversation with a customer to understand their issue history without scrolling through months of messages","I want to search for all conversations mentioning a specific product issue to identify patterns or recurring problems","I need to retrieve a conversation from 6 months ago to reference a previous resolution or agreement"],"best_for":["support teams handling hundreds of conversations per month needing historical context","organizations building knowledge bases from past support interactions","teams needing to identify recurring issues or customer patterns"],"limitations":["Full-text search may return irrelevant results for ambiguous queries; no mention of semantic search or NLP-based relevance ranking","Search index lag may be 1-5 seconds behind real-time messages, making very recent conversations unsearchable immediately","No mention of search result pagination or result limits; large result sets may impact UI performance","Search is limited to message text; no mention of searching by conversation metadata (customer sentiment, resolution status, etc.)"],"requires":["Search index built and maintained (requires indexing service running)","Minimum conversation history to search (empty conversations won't appear in results)","User permissions to access conversations being searched"],"input_types":["search query (text, keywords, filters)","date range filters","participant filters","conversation status filters"],"output_types":["ranked list of matching conversations","message snippets with highlighted matches","conversation metadata (participants, date, status)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chatnbx__cap_5","uri":"capability://automation.workflow.agent.availability.and.presence.management.with.status.indicators","name":"agent availability and presence management with status indicators","description":"Tracks agent online/offline status, current availability (available, busy, away, do-not-disturb), and presence indicators visible to team members and potentially customers. The system likely uses heartbeat pings or WebSocket keep-alives to detect disconnections, with automatic status transitions based on inactivity timeouts. Presence data is broadcast to relevant clients in real-time, enabling intelligent conversation routing to available agents and preventing customers from waiting for unavailable support staff.","intents":["I want customers to see which support agents are available before starting a conversation, reducing wait times","I need my team to see who's available to help with a complex customer issue without manually asking in Slack","I want conversations automatically routed to the next available agent instead of piling up in a single agent's queue"],"best_for":["support teams with 5-50 agents needing basic workload distribution","organizations wanting to reduce customer wait times by showing agent availability","teams using ChatNBX as primary communication tool where presence is critical"],"limitations":["Presence detection relies on client heartbeats; may show agents as available for 30-60 seconds after they disconnect","No mention of presence-based conversation routing; availability may be informational only without automatic load balancing","Inactivity timeout logic may be too aggressive or too lenient depending on agent workflow; no customization mentioned","No integration with calendar systems; agents must manually update status rather than auto-detecting meeting times"],"requires":["Active client connection (web, mobile, or desktop)","Periodic heartbeat or keep-alive mechanism","Status update permissions for user's own status"],"input_types":["agent status (available, busy, away, dnd)","inactivity duration","manual status updates"],"output_types":["presence indicator (online/offline/away)","availability status for UI display","presence broadcast to team members"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chatnbx__cap_6","uri":"capability://automation.workflow.conversation.assignment.and.escalation.workflow.management","name":"conversation assignment and escalation workflow management","description":"Manages assignment of conversations to individual agents or teams, with escalation rules that automatically route conversations to higher-tier support or management when specific conditions are met (e.g., unresolved after 24 hours, customer sentiment negative, issue complexity high). The system likely uses a rules engine to evaluate escalation conditions, with audit trails showing assignment history. Escalations may trigger notifications and update conversation priority or SLA timers.","intents":["I want conversations automatically escalated to my team lead if an agent hasn't responded within 2 hours","I need complex technical issues automatically routed to our senior engineers instead of front-line support","I want to track who handled a conversation and when it was escalated for accountability and training purposes"],"best_for":["support teams with multiple tiers (L1, L2, L3) needing automated escalation","organizations with SLA requirements where escalation timing is critical","teams wanting to ensure complex issues reach appropriate expertise without manual intervention"],"limitations":["Escalation rules require manual configuration; no mention of machine learning-based escalation prediction","No explicit mention of skill-based routing; escalations may go to next tier regardless of agent expertise","Escalation audit trails may not be queryable or reportable; limited visibility into escalation patterns","No mention of conversation re-assignment or load balancing; escalated conversations may pile up with senior staff"],"requires":["Escalation rules configured by admin","Agent/team hierarchy defined","Conversation metadata (creation time, last response time, customer sentiment if available)"],"input_types":["conversation object with metadata","escalation rule definitions","agent/team availability"],"output_types":["assignment decision (agent or team ID)","escalation trigger event","updated conversation metadata (assigned agent, escalation timestamp)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chatnbx__cap_7","uri":"capability://data.processing.analysis.conversation.tagging.and.metadata.annotation.for.organization","name":"conversation tagging and metadata annotation for organization","description":"Allows agents to tag conversations with custom labels (e.g., 'billing', 'bug', 'feature-request', 'urgent') and add structured metadata (customer type, product, issue category) to enable filtering, reporting, and knowledge organization. Tags are likely stored as denormalized fields in the conversation record, enabling fast filtering and aggregation. The system may support tag suggestions based on conversation content or previous tags, reducing manual annotation burden.","intents":["I want to tag conversations by issue type so I can identify patterns (e.g., 80% of issues are billing-related)","I need to mark conversations as 'feature-request' to feed product development with customer feedback","I want to filter my inbox to show only 'urgent' conversations to prioritize my work"],"best_for":["support teams wanting to categorize and analyze conversations for insights","organizations building feedback loops from support to product teams","teams needing to organize conversations by business context (customer type, product, region)"],"limitations":["Manual tagging is labor-intensive; no mention of automatic tag suggestions or ML-based categorization","Tag schema is likely uncontrolled; no mention of tag governance or standardization, leading to inconsistent tagging","No mention of tag-based reporting or analytics; tags may be stored but not leveraged for insights","Tag filtering may not support complex queries (e.g., 'billing AND urgent'); likely simple AND/OR logic only"],"requires":["Tag schema defined (custom tags or predefined list)","Agent permission to add/edit tags","Conversation object with tag field"],"input_types":["tag names (text)","metadata key-value pairs","conversation content (for tag suggestions)"],"output_types":["tagged conversation record","tag suggestions (if ML-enabled)","filtered conversation list by tags"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chatnbx__cap_8","uri":"capability://data.processing.analysis.conversation.history.export.and.compliance.reporting","name":"conversation history export and compliance reporting","description":"Enables export of conversation history in standard formats (CSV, JSON, PDF) for compliance, auditing, or knowledge base building purposes. The system likely supports bulk export with filtering by date range, participant, or tags, with options to include or exclude internal team messages. Exports may be asynchronous (queued and emailed) to avoid blocking the UI, and likely include metadata (timestamps, participants, assignment history) alongside message text.","intents":["I need to export all conversations from Q4 for compliance auditing and regulatory reporting","I want to extract customer feedback from support conversations to share with the product team","I need to generate a report showing conversation volume and resolution times by agent for performance reviews"],"best_for":["regulated industries (finance, healthcare) requiring conversation audit trails","organizations building knowledge bases from support interactions","teams needing to analyze support metrics and agent performance"],"limitations":["Export functionality likely limited to basic formats (CSV, JSON); no mention of advanced reporting or visualization","Bulk exports may be slow for large conversation volumes (>10k conversations); no mention of pagination or streaming","No mention of PII redaction or data anonymization; exports may contain sensitive customer information requiring manual scrubbing","Export permissions may not be granular; likely all-or-nothing access rather than role-based export restrictions"],"requires":["Admin or compliance officer role to initiate exports","Date range or filter criteria for export scope","Storage location for exported files (email, cloud storage, etc.)"],"input_types":["export format (CSV, JSON, PDF)","filter criteria (date range, tags, participants)","include/exclude options (internal messages, metadata)"],"output_types":["exported file in requested format","export metadata (creation timestamp, record count, file size)","export confirmation email"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_chatnbx__cap_9","uri":"capability://memory.knowledge.customer.conversation.context.and.history.retrieval.for.agents","name":"customer conversation context and history retrieval for agents","description":"Displays customer conversation history and context when an agent opens a conversation, including previous interactions, customer profile information, and relevant metadata (account status, purchase history if integrated). The system likely queries a customer database or CRM integration to populate context, with caching to reduce latency. Context is displayed in a sidebar or panel adjacent to the current conversation, enabling agents to understand customer history without leaving the chat interface.","intents":["I want to see a customer's previous support tickets and resolution history when they contact us again","I need to know if a customer is a high-value account or at risk of churn so I can prioritize their issue","I want to avoid asking a customer to repeat information they've already provided in previous conversations"],"best_for":["support teams handling repeat customers where context reduces resolution time","organizations with customer databases or CRM systems (Salesforce, HubSpot) to pull context from","teams wanting to improve customer experience by showing agents full customer history"],"limitations":["Context retrieval depends on CRM integration; limited integrations mentioned (no native Salesforce, HubSpot connectors) means context may be unavailable","Customer profile data may be stale if not synced in real-time; agents may see outdated account status or purchase history","No mention of context relevance ranking; agents may be overwhelmed with irrelevant historical information","Privacy concerns with displaying customer history; no mention of PII masking or data minimization"],"requires":["Customer database or CRM integration configured","Customer identifier (email, account ID) to look up history","API access to retrieve customer data"],"input_types":["customer identifier","conversation metadata (customer email, account ID)"],"output_types":["customer profile information","previous conversation summaries","customer metadata (account status, purchase history, churn risk)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Active internet connection for real-time sync (WebSocket support required)","Modern browser with localStorage/IndexedDB support for mobile clients","Account authentication with session token management","Active conversation history (minimum 2-3 messages for context)","API access to underlying LLM (likely proprietary or third-party like OpenAI)","Support agent to review and approve suggestions before sending","Team member role with permission to view internal notes","Conversation access (agent must be assigned or have team access)","Internal note creation permission","User account with defined role (agent, team lead, admin)"],"failure_modes":["Synchronization latency may increase under high message volume (>500 concurrent conversations) due to centralized state management","Offline-first sync requires local storage capacity; mobile clients may struggle with very large conversation histories (>10k messages per thread)","No explicit mention of conflict resolution strategy for simultaneous edits from multiple devices on same message","AI models generate occasionally generic or off-topic suggestions requiring significant human oversight in complex support scenarios (per editorial summary)","Suggestion quality degrades on novel or highly specialized customer issues outside training distribution","No explicit mention of multi-language support; likely optimized for English-language conversations","Feedback loop for suggestion improvement may require manual annotation; no self-improving mechanism mentioned","Internal notes are not visible to customers; no mention of note summarization for customer-facing resolution explanation","@mention notifications may create alert fatigue if overused; no mention of notification throttling or batching","No mention of note threading or organization; internal notes may become cluttered and hard to follow","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"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":"2026-05-24T12:16:29.716Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=chatnbx","compare_url":"https://unfragile.ai/compare?artifact=chatnbx"}},"signature":"iOr4jy83rrJ2MBhp7oEGiDxjz8hyRtaAAKRsHY5fcbwzKiFZJdHOv/0xw82XPW9fp4rV3LteOgnayAHpQ7y+BA==","signedAt":"2026-06-20T05:11:59.557Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/chatnbx","artifact":"https://unfragile.ai/chatnbx","verify":"https://unfragile.ai/api/v1/verify?slug=chatnbx","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"}}