{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_gnbly","slug":"gnbly","name":"Gnbly","type":"product","url":"https://gnbly.com","page_url":"https://unfragile.ai/gnbly","categories":["automation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_gnbly__cap_0","uri":"capability://automation.workflow.ai.driven.inbound.call.automation.with.natural.language.understanding","name":"ai-driven inbound call automation with natural language understanding","description":"Gnbly processes incoming calls through an AI system that understands natural language intent, extracts key information from caller speech, and executes predefined workflows without human intervention. The system likely uses speech-to-text conversion, NLU models for intent classification, and conditional logic trees to route or resolve calls automatically. This reduces manual handling of repetitive inquiries like account lookups, billing questions, or appointment scheduling.","intents":["Automatically handle high-volume repetitive inbound calls without agent involvement","Extract caller intent and relevant information from natural speech in real-time","Route calls to appropriate departments or agents based on detected intent","Reduce average handle time for customer service operations"],"best_for":["Medium to large enterprises with 100+ inbound calls daily","Customer service teams handling repetitive inquiry patterns","Contact centers seeking to reduce labor costs for routine interactions"],"limitations":["NLU accuracy degrades with heavy accents, background noise, or non-standard speech patterns","Cannot handle complex multi-turn conversations requiring contextual reasoning beyond predefined workflows","Limited visibility into model architecture and training data — unclear if using proprietary or third-party NLU engines","No documented support for non-English languages or regional dialects"],"requires":["Active phone line or VoIP integration (SIP/WebRTC compatible)","Predefined call workflows and intent mappings configured in platform","Integration with backend systems for data lookup (CRM, billing systems, etc.)"],"input_types":["audio/voice (inbound calls)","structured workflow definitions (JSON or visual builder)"],"output_types":["call resolution (automated or routed to agent)","structured call data (intent, extracted entities, duration)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gnbly__cap_1","uri":"capability://automation.workflow.intelligent.call.routing.with.department.and.agent.assignment","name":"intelligent call routing with department and agent assignment","description":"Gnbly implements a routing engine that classifies incoming calls by intent, priority, and caller attributes, then distributes them to the most appropriate agent or department based on skill matching, availability, and queue depth. The system likely uses rule-based routing (if-then logic), skill-based assignment algorithms, and real-time queue monitoring to minimize wait times and improve first-contact resolution rates.","intents":["Route calls to specialized agents based on detected caller need or account type","Distribute calls evenly across available agents to prevent queue bottlenecks","Prioritize urgent or high-value calls for faster handling","Reduce transfers and repeat calls by matching caller intent to agent expertise"],"best_for":["Multi-department contact centers with specialized teams","Operations with variable call volume requiring dynamic load balancing","Enterprises tracking agent skill sets and availability in real-time"],"limitations":["Routing rules must be manually configured — no self-learning from historical call outcomes","No documented support for complex multi-criteria optimization (e.g., minimizing total wait time across all queues)","Skill-based routing requires accurate agent skill inventory maintenance","Cannot dynamically adjust routing weights based on agent performance metrics"],"requires":["Agent availability and skill data synchronized with platform","Predefined routing rules or decision trees","Integration with phone system or VoIP provider for call control"],"input_types":["call metadata (caller ID, IVR selections, detected intent)","agent status and skill inventory","queue depth and wait time thresholds"],"output_types":["routing decision (target agent/department/queue)","call transfer command to phone system"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gnbly__cap_2","uri":"capability://data.processing.analysis.comprehensive.call.analytics.and.performance.reporting","name":"comprehensive call analytics and performance reporting","description":"Gnbly collects detailed metadata from every call (duration, intent, resolution status, agent handling time, transfers, etc.) and aggregates this data into dashboards and reports showing trends, KPIs, and performance by agent, department, or time period. The system likely uses time-series databases for call event storage, statistical aggregation for KPI calculation, and visualization layers for reporting. This enables data-driven optimization of call center operations.","intents":["Track call volume, average handle time, and resolution rates by department or agent","Identify peak call times and staffing gaps to optimize scheduling","Measure customer satisfaction proxies (call duration, transfer rates, repeat calls)","Benchmark performance against historical trends or industry standards"],"best_for":["Call center managers requiring visibility into team performance","Operations teams optimizing staffing and resource allocation","Compliance-heavy industries needing detailed call audit trails"],"limitations":["Analytics are descriptive (what happened) rather than predictive (what will happen)","No documented machine learning for anomaly detection or forecasting","Report customization capabilities unknown — may be limited to predefined templates","Data retention policies and historical data availability not specified"],"requires":["Active call logging and metadata capture from phone system","Integration with CRM or backend systems for caller/account context","User access to reporting dashboard (web or API)"],"input_types":["call event logs (start time, duration, agent ID, intent, resolution)","agent and department hierarchies","custom KPI definitions"],"output_types":["dashboard visualizations (charts, tables, KPI cards)","scheduled reports (PDF, CSV, email)","API endpoints for programmatic access to metrics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gnbly__cap_3","uri":"capability://automation.workflow.outbound.call.automation.with.predictive.dialing","name":"outbound call automation with predictive dialing","description":"Gnbly enables automated outbound calling campaigns where the system dials contacts from a list, detects when a human answers, and connects them to an available agent or plays a pre-recorded message. The system likely uses predictive dialing algorithms to optimize agent utilization by dialing multiple numbers in parallel while accounting for no-answers and voicemails, reducing idle time between calls. This is commonly used for sales, collections, or appointment reminders.","intents":["Execute large-scale outbound calling campaigns without manual dialing","Maximize agent productivity by automatically connecting them to live calls","Reduce idle time between calls through predictive dialing","Deliver pre-recorded messages or surveys to large contact lists"],"best_for":["Sales teams running outbound prospecting campaigns","Collections departments managing high-volume debt recovery","Appointment reminder and confirmation operations","Survey and feedback collection at scale"],"limitations":["Predictive dialing can result in abandoned calls if agent availability is miscalculated","Regulatory compliance required (TCPA, GDPR, DNC lists) — unclear if platform enforces these automatically","No documented support for dynamic list management or real-time campaign adjustments","Call quality and connection reliability depend on underlying VoIP provider"],"requires":["Contact list with phone numbers (CSV, database, or API integration)","Compliant consent and DNC list management","Available agents or pre-recorded message content","VoIP provider with outbound calling capacity"],"input_types":["contact lists (phone numbers, names, metadata)","campaign configuration (dialing rate, message, agent assignment)","pre-recorded audio files or agent scripts"],"output_types":["call completion logs (connected, voicemail, no-answer, busy)","agent call recordings and transcripts","campaign performance metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gnbly__cap_4","uri":"capability://data.processing.analysis.call.recording.transcription.and.searchable.archive","name":"call recording, transcription, and searchable archive","description":"Gnbly automatically records all inbound and outbound calls, converts audio to text using speech-to-text technology, and stores transcripts in a searchable archive indexed by caller, agent, date, and extracted keywords. This enables compliance, quality assurance, training, and dispute resolution. The system likely uses cloud storage for audio files, ASR APIs for transcription, and full-text search indexing for transcript retrieval.","intents":["Maintain compliance audit trails for regulated industries (finance, healthcare)","Search call history by caller name, phone number, or topic discussed","Review agent performance and identify training opportunities","Resolve disputes by retrieving exact call transcripts and recordings"],"best_for":["Regulated industries requiring call recording (finance, insurance, healthcare)","Quality assurance teams conducting agent coaching","Legal and compliance teams managing audit requirements","Customer service teams handling disputes or escalations"],"limitations":["Transcription accuracy depends on audio quality and speaker clarity — no documented accuracy rates","Storage costs scale with call volume — pricing model unclear","Search functionality limited to metadata and transcript keywords — no semantic search or intent-based retrieval documented","Retention policies and deletion procedures not specified"],"requires":["Consent from all call participants (varies by jurisdiction)","Sufficient storage capacity for audio and transcript files","Compliance with data protection regulations (GDPR, HIPAA, etc.)"],"input_types":["live call audio streams (inbound and outbound)","call metadata (caller ID, timestamp, agent ID)"],"output_types":["audio files (WAV, MP3, or proprietary format)","text transcripts (searchable, timestamped)","archive metadata and search results"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gnbly__cap_5","uri":"capability://automation.workflow.real.time.call.monitoring.and.supervisor.intervention","name":"real-time call monitoring and supervisor intervention","description":"Gnbly allows supervisors to listen to live calls in progress, view call details (caller info, intent, agent notes), and optionally intervene by whispering to the agent or taking over the call. This is implemented through real-time audio streaming to supervisor dashboards, call state synchronization, and audio mixing for whisper/takeover functionality. Supervisors can also flag calls for quality review or coaching.","intents":["Monitor agent performance and call quality in real-time","Intervene in difficult calls to prevent escalation or poor outcomes","Provide real-time coaching or guidance to agents during calls","Flag calls for post-call review and training"],"best_for":["Quality assurance teams conducting live call monitoring","Supervisors managing high-stakes customer interactions","Training programs requiring real-time coaching opportunities","Operations teams responding to urgent escalations"],"limitations":["Real-time streaming adds latency and bandwidth overhead — no documented latency specifications","Whisper and takeover features may not work reliably with all VoIP codecs or network conditions","Supervisor dashboard scalability unclear — maximum concurrent monitored calls not specified","Agent awareness of monitoring may impact call quality (psychological effect not addressed)"],"requires":["Supervisor user accounts with monitoring permissions","Sufficient network bandwidth for real-time audio streaming","Integration with phone system for call state and audio access"],"input_types":["live call audio and metadata","supervisor dashboard interactions (listen, whisper, takeover commands)"],"output_types":["real-time supervisor dashboard with call details","audio stream to supervisor headset","call flags and coaching notes"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gnbly__cap_6","uri":"capability://tool.use.integration.integration.with.crm.and.backend.systems.for.caller.context","name":"integration with crm and backend systems for caller context","description":"Gnbly integrates with CRM platforms (Salesforce, HubSpot, etc.) and backend systems to retrieve caller information, account history, and relevant context before or during calls. When a call arrives, the system looks up the caller by phone number or account ID, retrieves their profile and recent interactions, and displays this context to the agent or uses it for routing decisions. This is implemented through API integrations, webhook-based data sync, and screen-pop functionality.","intents":["Display caller account information and history to agents automatically","Route calls based on account type, value, or history","Reduce call handling time by providing context upfront","Enable personalized interactions based on caller profile"],"best_for":["Enterprises with existing CRM investments (Salesforce, HubSpot, Pipedrive)","Customer service teams requiring account context for every call","Operations seeking to reduce call handling time through context availability","Sales teams managing account-based outreach"],"limitations":["Integration complexity depends on CRM API capabilities and data schema","Real-time data sync latency may cause stale information display","No documented support for complex data transformations or field mapping","CRM availability issues could impact call handling if context lookup fails"],"requires":["Active CRM account with API access enabled","API credentials and authentication tokens","Data mapping configuration (phone number to CRM record lookup)","Network connectivity and API rate limit management"],"input_types":["incoming call metadata (caller phone number, account ID)","CRM API endpoints and authentication"],"output_types":["caller profile and account history displayed to agent","routing decisions based on CRM data","call context logged back to CRM record"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_gnbly__cap_7","uri":"capability://automation.workflow.ivr.interactive.voice.response.with.custom.menu.trees.and.branching.logic","name":"ivr (interactive voice response) with custom menu trees and branching logic","description":"Gnbly enables creation of custom IVR menus where callers navigate through voice prompts and keypad selections to reach the right department, provide information, or self-serve for simple tasks. The system uses a visual builder or configuration interface to define menu trees with branching logic, conditional routing based on caller input, and integration with backend systems for data collection. This reduces agent workload for routine inquiries.","intents":["Route callers to appropriate departments without agent involvement","Collect caller information (account number, reason for call) via keypad or voice","Provide self-service options (balance inquiry, appointment scheduling, payment)","Reduce call volume to agents by handling routine inquiries in IVR"],"best_for":["Contact centers with high call volume and clear call classification patterns","Operations offering self-service options (account lookup, payments, scheduling)","Enterprises seeking to reduce agent workload for routine inquiries"],"limitations":["IVR usability degrades with complex menu trees — callers may abandon after multiple levels","Voice recognition for open-ended input is less reliable than keypad selection","No documented support for dynamic menu generation based on caller profile","Integration with backend systems for real-time data lookup may add latency"],"requires":["Visual IVR builder or configuration interface","Phone system integration for menu playback and DTMF/voice input capture","Backend system integration for data lookup and self-service actions"],"input_types":["IVR menu configuration (prompts, options, branching logic)","caller input (DTMF keypad selections or voice commands)","backend system APIs for data lookup"],"output_types":["audio prompts played to caller","routing decision based on caller input","collected caller information"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Active phone line or VoIP integration (SIP/WebRTC compatible)","Predefined call workflows and intent mappings configured in platform","Integration with backend systems for data lookup (CRM, billing systems, etc.)","Agent availability and skill data synchronized with platform","Predefined routing rules or decision trees","Integration with phone system or VoIP provider for call control","Active call logging and metadata capture from phone system","Integration with CRM or backend systems for caller/account context","User access to reporting dashboard (web or API)","Contact list with phone numbers (CSV, database, or API integration)"],"failure_modes":["NLU accuracy degrades with heavy accents, background noise, or non-standard speech patterns","Cannot handle complex multi-turn conversations requiring contextual reasoning beyond predefined workflows","Limited visibility into model architecture and training data — unclear if using proprietary or third-party NLU engines","No documented support for non-English languages or regional dialects","Routing rules must be manually configured — no self-learning from historical call outcomes","No documented support for complex multi-criteria optimization (e.g., minimizing total wait time across all queues)","Skill-based routing requires accurate agent skill inventory maintenance","Cannot dynamically adjust routing weights based on agent performance metrics","Analytics are descriptive (what happened) rather than predictive (what will happen)","No documented machine learning for anomaly detection or forecasting","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.892Z","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=gnbly","compare_url":"https://unfragile.ai/compare?artifact=gnbly"}},"signature":"uRhHhmIWUZArqwmyUTyKyj/nL8+gQ9mZ228S1g9qzJg6GtZ8ZCmArWSolfnXO2PKFFkt4uL6nT/2t+Zx0VkzDg==","signedAt":"2026-06-23T05:52:20.316Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gnbly","artifact":"https://unfragile.ai/gnbly","verify":"https://unfragile.ai/api/v1/verify?slug=gnbly","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"}}