{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_meetraai","slug":"meetraai","name":"MeetraAI","type":"product","url":"https://meetra.ai","page_url":"https://unfragile.ai/meetraai","categories":["data-analysis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_meetraai__cap_0","uri":"capability://data.processing.analysis.real.time.conversation.transcription.with.speaker.diarization","name":"real-time conversation transcription with speaker diarization","description":"Automatically converts audio from sales calls, customer success interactions, and support conversations into timestamped transcripts while identifying and labeling individual speakers. Uses speech-to-text processing with speaker separation algorithms to distinguish between multiple participants, enabling downstream analysis to attribute statements to specific roles (e.g., sales rep vs. prospect). Integrates with common communication platforms and recording systems to capture audio streams in real-time or batch mode.","intents":["I need to automatically capture what was said in calls without manual note-taking","I want to know who said what in multi-party conversations for coaching and QA","I need transcripts searchable by speaker role to find specific rep behaviors or customer objections"],"best_for":["Sales teams managing 50+ calls per week who need audit trails","Customer success managers coaching reps on call quality","Compliance-focused organizations requiring call documentation"],"limitations":["Accuracy degrades with heavy accents, background noise, or overlapping speakers — typically 85-92% word error rate in real-world conditions","Diarization fails when more than 5-6 speakers are present simultaneously","Real-time processing adds 2-5 second latency before transcript availability","Requires explicit platform integrations — does not work with arbitrary audio files without pre-configuration"],"requires":["Audio input from supported platforms (Zoom, Microsoft Teams, Salesforce, etc.) or direct API access","Minimum 10 seconds of audio per speaker for accurate diarization","Network connectivity for real-time processing or batch upload capability"],"input_types":["audio stream (MP3, WAV, M4A)","video with embedded audio (MP4, MOV)","live call feeds from integrated platforms"],"output_types":["timestamped transcript (JSON, plain text)","speaker-labeled segments with confidence scores","searchable transcript index"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_meetraai__cap_1","uri":"capability://data.processing.analysis.sentiment.and.emotion.detection.across.conversation.segments","name":"sentiment and emotion detection across conversation segments","description":"Analyzes transcript segments and audio tone to classify emotional states and sentiment polarity (positive, negative, neutral) at the speaker level and conversation-phase level. Uses a combination of NLP-based text sentiment analysis and acoustic feature extraction (pitch, pace, energy) to detect emotional shifts. Produces segment-level sentiment scores with temporal visualization, enabling identification of conversation turning points and emotional escalations or de-escalations.","intents":["I need to identify when a customer became frustrated or disengaged during a call","I want to spot moments where my sales rep lost control of the conversation emotionally","I need to measure overall call sentiment to correlate with deal outcomes"],"best_for":["Sales coaches analyzing rep performance and emotional intelligence","Customer success teams detecting churn risk signals in support calls","Quality assurance teams building coaching playbooks around emotional moments"],"limitations":["Sentiment detection is language-specific — primarily trained on English; other languages have 15-25% lower accuracy","Sarcasm, industry jargon, and context-dependent language frequently misclassified as opposite sentiment","Acoustic analysis requires clear audio — heavily compressed or low-bitrate calls produce unreliable tone detection","No distinction between authentic emotion and performative tone (e.g., a rep faking enthusiasm)"],"requires":["Transcript or audio input with speaker labels","Minimum 30-second segments for reliable sentiment scoring","English language content for highest accuracy (other languages supported with degraded performance)"],"input_types":["transcript text with speaker attribution","audio waveform with timestamp markers","pre-segmented conversation phases"],"output_types":["sentiment scores per segment (0-1 scale)","emotion labels (frustration, enthusiasm, confusion, etc.)","temporal sentiment trend visualization","speaker-level sentiment summary"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_meetraai__cap_10","uri":"capability://code.generation.editing.custom.model.training.and.fine.tuning.for.domain.specific.analysis","name":"custom model training and fine-tuning for domain-specific analysis","description":"Enables customers to fine-tune sentiment, intent, and objection classification models on their own conversation data to improve accuracy for domain-specific language and sales methodologies. Provides a training interface where customers can label conversation segments and trigger model retraining. Supports transfer learning to leverage pre-trained models while adapting to customer-specific patterns. Produces model performance metrics (precision, recall, F1) to validate improvements before deployment.","intents":["I want to improve sentiment analysis accuracy for my industry-specific jargon and customer communication style","I need to train custom objection classifiers for my specific product and sales methodology","I want to measure whether custom models improve coaching accuracy compared to generic models"],"best_for":["Enterprises with specialized verticals (e.g., enterprise software, medical devices) where generic models underperform","Organizations with proprietary sales methodologies that don't map to standard frameworks","Data science teams with ML expertise seeking to optimize model performance"],"limitations":["Requires manual labeling of training data — typically 500-1000 labeled examples needed for meaningful improvement","Model retraining takes 2-4 hours — not suitable for real-time model updates","Transfer learning improves accuracy by 5-15% on average — diminishing returns beyond 1000 labeled examples","No A/B testing framework — difficult to measure actual improvement in downstream business metrics (coaching effectiveness, deal outcomes)"],"requires":["Minimum 500 labeled conversation segments for model training","Data science or ML engineering expertise to interpret model metrics","Access to historical conversation data with outcomes"],"input_types":["labeled conversation segments (text + label pairs)","conversation metadata (rep, customer, outcome)"],"output_types":["fine-tuned model (deployed to production)","model performance metrics (precision, recall, F1, confusion matrix)","feature importance analysis (which words/phrases drive predictions)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_meetraai__cap_11","uri":"capability://automation.workflow.real.time.conversation.alerts.and.intervention.prompts","name":"real-time conversation alerts and intervention prompts","description":"Monitors ongoing calls in real-time and surfaces alerts or coaching prompts to reps or managers when specific conversation patterns are detected (e.g., 'customer expressed budget concern — suggest trial offer', 'rep has talked for 3+ minutes without customer response — prompt to ask question'). Uses low-latency intent and sentiment detection to identify intervention opportunities within 5-10 seconds of occurrence. Supports configurable alert rules and delivery channels (in-app notification, SMS, Slack).","intents":["I want to coach my reps in real-time during calls when they miss objection handling opportunities","I need to alert managers when a call is going poorly so they can intervene","I want to prompt reps with suggested responses when specific customer objections are detected"],"best_for":["Sales teams with high-velocity call environments (5-10+ calls per rep per day) where post-call coaching is too late","Organizations with strong call coaching culture and manager availability for real-time intervention","Inside sales teams where reps are co-located or remote but available for real-time guidance"],"limitations":["Real-time processing adds 5-10 second latency — alerts arrive after the conversation moment has passed, reducing intervention effectiveness","Alert fatigue is common — too many alerts reduce rep engagement and trust in system","Intervention prompts can disrupt call flow and distract reps — effectiveness depends on rep discipline and manager judgment","Requires live call feed integration — not all phone systems support real-time audio streaming"],"requires":["Live call feed from phone system or meeting platform (Zoom, Teams, etc.)","Real-time processing infrastructure (low-latency inference)","Configurable alert rules and delivery channels"],"input_types":["live audio stream from ongoing call","alert rule configuration (trigger conditions, alert type, delivery channel)"],"output_types":["real-time alerts (in-app, SMS, Slack)","coaching prompts with suggested responses","alert history and engagement metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_meetraai__cap_12","uri":"capability://data.processing.analysis.conversation.analytics.dashboards.and.reporting.with.trend.analysis","name":"conversation analytics dashboards and reporting with trend analysis","description":"Provides customizable dashboards and reports aggregating conversation metrics across teams, time periods, and customer segments. Includes pre-built reports (team sentiment trends, objection frequency, rep performance rankings, customer health) and custom report builder for ad-hoc analysis. Supports drill-down from aggregate metrics to individual calls and segments. Produces trend analysis showing metric changes over time and correlation analysis (e.g., 'calls with high discovery quality have 40% higher close rates').","intents":["I want to see team-level trends in call quality, sentiment, and objection handling over the past quarter","I need to identify which rep behaviors correlate with successful deals","I want to create custom reports for executive leadership showing conversation intelligence ROI"],"best_for":["Sales and CS leaders seeking data-driven insights into team performance and customer health","Organizations building business cases for conversation intelligence ROI","Teams with analytics expertise seeking to build custom reports and dashboards"],"limitations":["Correlation analysis is not causation — high discovery quality may correlate with close rates but may not cause them","Trend analysis requires minimum 30-50 calls per period for statistical significance — small teams see noisy results","Custom report builder requires SQL or analytics expertise — non-technical users limited to pre-built reports","Dashboard refresh latency is 1-4 hours — not suitable for real-time decision making"],"requires":["Minimum 100 analyzed calls for meaningful aggregate metrics","Conversation metadata (rep, customer, deal stage, outcome) for segmentation","Analytics database or data warehouse for efficient querying"],"input_types":["conversation analysis results (sentiment, intent, coaching flags, etc.)","call metadata (rep, customer, date, outcome)","custom metric definitions"],"output_types":["pre-built dashboards (team performance, customer health, objection trends)","custom reports (ad-hoc analysis)","trend visualizations (time series, heatmaps)","correlation analysis results"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_meetraai__cap_2","uri":"capability://data.processing.analysis.intent.and.topic.extraction.with.conversation.flow.mapping","name":"intent and topic extraction with conversation flow mapping","description":"Automatically identifies customer intents (e.g., 'pricing inquiry', 'technical support', 'renewal discussion') and sales rep intents (e.g., 'discovery', 'objection handling', 'closing attempt') throughout the conversation. Uses intent classification models trained on sales conversation patterns to tag conversation phases and extract key topics discussed. Produces a conversation flow diagram showing intent transitions and topic sequences, enabling analysis of conversation structure and effectiveness.","intents":["I want to know if my reps are following the discovery-to-close conversation flow","I need to identify which customer objections are being raised and how reps respond","I want to extract key topics discussed to correlate with deal progression"],"best_for":["Sales managers analyzing rep adherence to sales methodology","Training teams identifying common objection patterns for coaching","Deal intelligence teams correlating conversation topics with win/loss outcomes"],"limitations":["Intent classification is domain-specific — models trained on B2B SaaS sales; B2C, services, or highly specialized verticals show 20-30% lower accuracy","Requires sufficient context within conversation segments — short utterances (< 5 words) frequently misclassified","Cannot distinguish between stated intent and actual intent (e.g., 'I'm just browsing' vs. genuine buying signal)","Topic extraction produces generic categories — does not capture product-specific or company-specific topics without custom training"],"requires":["Transcript with speaker attribution and timestamps","Minimum 5-minute conversation length for reliable intent patterns","English language content (other languages supported with reduced accuracy)"],"input_types":["transcript text with speaker labels","conversation segments with phase markers","raw conversation audio (processed to transcript first)"],"output_types":["intent labels per speaker turn (JSON array)","topic tags with confidence scores","conversation flow diagram (JSON graph structure)","intent transition heatmap"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_meetraai__cap_3","uri":"capability://data.processing.analysis.competitive.intelligence.and.objection.pattern.recognition","name":"competitive intelligence and objection pattern recognition","description":"Identifies mentions of competitors, pricing discussions, and customer objections within conversations, then aggregates patterns across calls to surface recurring themes. Uses named entity recognition (NER) to detect competitor names and product mentions, combined with intent classification to identify objection contexts. Produces reports showing which competitors are mentioned most, what objections are most common, and how reps handle them, enabling sales leadership to identify coaching gaps and competitive positioning weaknesses.","intents":["I need to know which competitors are being mentioned in lost deals","I want to identify the top 5 objections my reps face and how they respond","I need to track whether our competitive positioning messaging is consistent across reps"],"best_for":["Sales leaders building competitive battle cards and objection handling playbooks","Product marketing teams understanding customer perception of competitors","Sales enablement teams identifying coaching opportunities around objection handling"],"limitations":["Competitor detection relies on explicit mentions — indirect references ('that other tool', 'the market leader') are missed","Objection classification is generic — does not distinguish between price objections, technical concerns, and organizational fit issues without custom training","Requires sufficient call volume (50+ calls minimum) for pattern reliability — small teams see noisy results","Does not capture non-verbal objection signals (hesitation, silence, tone shifts) without acoustic analysis integration"],"requires":["Minimum 50 transcribed calls for meaningful pattern detection","Competitor database or custom entity list for accurate NER","Sales methodology framework to classify objection types"],"input_types":["transcript corpus with timestamps","competitor name list (optional custom entity dictionary)","objection taxonomy or sales methodology framework"],"output_types":["competitor mention frequency report (JSON)","objection pattern heatmap (competitor vs. objection type)","rep-level objection handling effectiveness scores","competitive positioning consistency audit"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_meetraai__cap_4","uri":"capability://planning.reasoning.coaching.moment.identification.and.rep.performance.scoring","name":"coaching moment identification and rep performance scoring","description":"Automatically flags conversation segments where coaching opportunities exist (e.g., rep missed discovery question, failed to handle objection, talked too much without listening). Uses behavioral pattern matching against sales methodology frameworks to identify deviations from best practices. Scores individual reps on dimensions like discovery quality, objection handling, talk-to-listen ratio, and closing effectiveness. Produces rep performance dashboards with trend analysis and peer benchmarking.","intents":["I want to automatically flag calls where my reps need coaching without manually reviewing every call","I need to score rep performance consistently across dimensions to identify top performers and struggling reps","I want to benchmark individual reps against team averages to set coaching priorities"],"best_for":["Sales managers with 5-50 direct reports seeking scalable coaching workflows","Sales coaches needing data-driven coaching priorities and evidence for conversations","Sales leaders building performance management systems with objective metrics"],"limitations":["Coaching recommendations are methodology-specific — requires explicit sales framework configuration; generic recommendations are often irrelevant","Talk-to-listen ratio and pacing metrics are crude proxies for actual listening quality — high-performing reps may naturally talk more in certain scenarios","Performance scoring is relative to team averages — does not account for call difficulty, customer type, or deal stage variations","Requires 20+ calls per rep minimum for reliable trend analysis; new reps show high variance"],"requires":["Sales methodology framework (e.g., MEDDIC, Sandler, Challenger) configured in system","Minimum 20 calls per rep for reliable performance scoring","Transcript data with speaker attribution and timestamps"],"input_types":["transcript corpus with speaker labels","call metadata (rep ID, customer, deal stage, outcome)","sales methodology configuration"],"output_types":["coaching moment flags with timestamp and reason (JSON)","rep performance scorecard (multi-dimensional)","peer benchmarking comparison","trend analysis (performance over time)","coaching priority queue"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_meetraai__cap_5","uri":"capability://data.processing.analysis.customer.health.and.churn.risk.scoring.from.conversation.signals","name":"customer health and churn risk scoring from conversation signals","description":"Analyzes customer success and support calls to extract health signals (sentiment trends, feature adoption mentions, renewal readiness indicators) and produces a churn risk score. Uses historical conversation data to identify patterns associated with churned customers, then applies those patterns to current customers. Integrates with CRM to surface at-risk accounts and trigger intervention workflows. Produces customer health dashboards with leading indicators derived from conversation content.","intents":["I need to identify at-risk customers before they churn based on conversation patterns","I want to track customer health trends over time using conversation sentiment and engagement signals","I need to prioritize customer success outreach based on churn risk scores"],"best_for":["Customer success teams managing 100+ accounts seeking early churn warning systems","Subscription businesses with high customer lifetime value where churn prevention is critical","Mid-market SaaS companies with 12-24 month sales cycles where renewal conversations are predictable"],"limitations":["Churn prediction requires historical data from churned customers — new products or markets lack training data","Conversation-based signals are lagging indicators — by the time sentiment turns negative, customer may already be evaluating alternatives","Does not capture non-conversation signals (product usage, support ticket volume, payment delays) — requires CRM integration for holistic health scoring","False positive rate is typically 20-30% — many flagged accounts do not actually churn"],"requires":["Minimum 100 historical customer conversations with churn outcomes for model training","CRM integration to correlate conversation data with customer metadata and outcomes","Regular conversation data (monthly or quarterly check-ins) for trending analysis"],"input_types":["customer success call transcripts with customer ID","support conversation transcripts","CRM customer data (contract value, renewal date, product usage)"],"output_types":["churn risk score per customer (0-100)","health trend visualization (sentiment, engagement over time)","at-risk customer list with risk factors","recommended intervention actions"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_meetraai__cap_6","uri":"capability://tool.use.integration.crm.and.workflow.integration.with.conversation.insights","name":"crm and workflow integration with conversation insights","description":"Integrates conversation analysis results (sentiment, intents, coaching moments, objections) directly into CRM systems (Salesforce, HubSpot, Pipedrive) and triggers automated workflows based on conversation outcomes. Pushes key insights as call summaries, next steps, and coaching flags into deal records and contact profiles. Supports webhook-based integrations and native CRM connectors to enable real-time insight delivery and automation trigger conditions (e.g., 'if objection detected, create coaching task').","intents":["I want call insights automatically added to Salesforce deal records without manual data entry","I need to trigger coaching tasks or follow-up actions based on conversation analysis results","I want to enrich customer profiles with sentiment and engagement data from conversations"],"best_for":["Sales and CS teams already using Salesforce, HubSpot, or Pipedrive who want to avoid context switching","Organizations with existing workflow automation needs (e.g., task creation, notification routing)","Teams seeking to embed conversation insights into existing deal review and forecasting processes"],"limitations":["CRM field mapping is manual — requires IT or admin configuration for each custom field or object type","Webhook latency adds 5-30 seconds between conversation completion and CRM update — not suitable for real-time use cases","Limited to CRM-supported field types — complex insights (conversation flow diagrams) cannot be stored natively","Requires CRM API access and OAuth credentials — adds security and compliance complexity"],"requires":["Active CRM account (Salesforce, HubSpot, Pipedrive, or other supported platform)","CRM admin access to configure field mappings and webhook endpoints","API credentials and OAuth setup"],"input_types":["conversation analysis results (JSON)","CRM object identifiers (deal ID, contact ID, account ID)"],"output_types":["CRM field updates (call summary, sentiment, next steps)","task creation (coaching tasks, follow-up reminders)","webhook events for downstream automation"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_meetraai__cap_7","uri":"capability://search.retrieval.conversation.search.and.retrieval.with.semantic.understanding","name":"conversation search and retrieval with semantic understanding","description":"Enables full-text and semantic search across the conversation corpus to find calls matching specific criteria (e.g., 'calls where customer mentioned budget concerns', 'calls where rep used closing technique X', 'calls with high sentiment drop'). Uses vector embeddings of conversation segments to enable semantic similarity search beyond keyword matching. Supports filtered search by rep, customer, deal stage, date range, and conversation outcome. Returns ranked results with highlighted relevant segments and context.","intents":["I need to find examples of how top reps handle a specific objection for coaching purposes","I want to search for all calls where a specific competitor was mentioned","I need to find calls with similar conversation patterns to a lost deal to understand what went wrong"],"best_for":["Sales coaches building playbooks and needing quick access to exemplar calls","Sales leaders analyzing competitive losses and needing to find similar calls","Training teams creating onboarding materials and needing to extract relevant call examples"],"limitations":["Semantic search requires vector embeddings of all conversations — adds storage overhead (~1-2 MB per call) and indexing latency (30-60 seconds per call)","Search quality depends on embedding model quality — domain-specific queries (product feature names, industry jargon) may return irrelevant results","Filtered search across large corpora (10,000+ calls) can be slow without proper indexing — typical query latency is 2-10 seconds","No support for complex boolean queries — cannot combine multiple conditions with AND/OR/NOT logic"],"requires":["Minimum 50 transcribed calls for meaningful search results","Vector database or search index (Elasticsearch, Pinecone, or similar) for semantic search","Conversation metadata (rep, customer, deal stage, outcome) for filtered search"],"input_types":["natural language search query","filter criteria (rep name, date range, deal outcome, etc.)","conversation corpus (transcripts with metadata)"],"output_types":["ranked search results (call ID, relevance score)","highlighted relevant segments with context","metadata summary (rep, customer, date, outcome)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_meetraai__cap_8","uri":"capability://data.processing.analysis.multi.language.conversation.analysis.with.language.detection","name":"multi-language conversation analysis with language detection","description":"Automatically detects the language spoken in conversations and applies language-specific NLP models for transcription, sentiment analysis, and intent extraction. Supports 15+ languages including Spanish, French, German, Mandarin, Japanese, and others. Produces multilingual transcripts and analysis results, enabling global sales and CS teams to analyze conversations in their native languages. Handles code-switching (mixing multiple languages in a single call) with language-aware segmentation.","intents":["I need to analyze sales calls in Spanish, French, and German for my European teams","I want sentiment analysis and coaching insights for calls with customers in Asia-Pacific regions","I need to handle calls where reps and customers switch between languages mid-conversation"],"best_for":["Global sales and CS organizations with multilingual teams and customers","Companies expanding into new geographic markets and needing conversation intelligence in local languages","Distributed teams where reps and customers speak different native languages"],"limitations":["Language-specific model quality varies significantly — English models are 95%+ accurate, but less common languages (e.g., Portuguese, Thai) are 80-85% accurate","Code-switching (mixing languages) reduces accuracy by 10-20% — models trained primarily on single-language conversations","Sentiment and intent models are trained on English sales conversations — translations to other languages may lose cultural nuance","Requires language-specific training data for custom verticals — generic models may not understand industry jargon in non-English languages"],"requires":["Audio or transcript input in supported language","Language detection model (automatic or manual specification)","Language-specific NLP models deployed (adds ~50-100 MB per language)"],"input_types":["audio in any supported language","transcript in any supported language","mixed-language audio (code-switching)"],"output_types":["language-detected transcript with language labels per segment","sentiment and intent analysis in original language","optional English translation"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_meetraai__cap_9","uri":"capability://safety.moderation.call.recording.and.storage.with.compliance.and.privacy.controls","name":"call recording and storage with compliance and privacy controls","description":"Manages secure storage of call recordings with configurable retention policies, encryption, and access controls. Implements compliance features including GDPR consent tracking, CCPA data deletion workflows, and audit logging for who accessed which recordings. Supports on-premise or cloud storage options with SOC 2 Type II certification. Provides granular permission controls (e.g., reps can only access their own calls, managers can access team calls, compliance can access all).","intents":["I need to store call recordings securely with encryption and audit trails for compliance","I want to automatically delete recordings after 90 days to comply with data retention policies","I need to track who accessed which recordings for GDPR and CCPA compliance"],"best_for":["Regulated industries (financial services, healthcare, legal) with strict data retention and privacy requirements","Global organizations operating in GDPR, CCPA, and other privacy-regulated jurisdictions","Enterprises with security and compliance teams requiring audit trails and access controls"],"limitations":["On-premise storage requires significant infrastructure investment and ongoing maintenance","Encryption adds computational overhead — decryption latency is 1-3 seconds per recording access","GDPR 'right to be forgotten' requires deletion of all derived data (transcripts, embeddings, analysis results) — not just recordings","Access control granularity is limited to role-based (RBAC) — attribute-based access control (ABAC) not supported"],"requires":["Compliance framework configuration (GDPR, CCPA, HIPAA, etc.)","Storage infrastructure (cloud or on-premise)","Encryption keys management (customer-managed or provider-managed)"],"input_types":["call recordings (audio files)","compliance policy configuration","user and role definitions"],"output_types":["encrypted recording storage","audit logs (access, deletion, retention events)","compliance reports (data retention, deletion confirmations)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Audio input from supported platforms (Zoom, Microsoft Teams, Salesforce, etc.) or direct API access","Minimum 10 seconds of audio per speaker for accurate diarization","Network connectivity for real-time processing or batch upload capability","Transcript or audio input with speaker labels","Minimum 30-second segments for reliable sentiment scoring","English language content for highest accuracy (other languages supported with degraded performance)","Minimum 500 labeled conversation segments for model training","Data science or ML engineering expertise to interpret model metrics","Access to historical conversation data with outcomes","Live call feed from phone system or meeting platform (Zoom, Teams, etc.)"],"failure_modes":["Accuracy degrades with heavy accents, background noise, or overlapping speakers — typically 85-92% word error rate in real-world conditions","Diarization fails when more than 5-6 speakers are present simultaneously","Real-time processing adds 2-5 second latency before transcript availability","Requires explicit platform integrations — does not work with arbitrary audio files without pre-configuration","Sentiment detection is language-specific — primarily trained on English; other languages have 15-25% lower accuracy","Sarcasm, industry jargon, and context-dependent language frequently misclassified as opposite sentiment","Acoustic analysis requires clear audio — heavily compressed or low-bitrate calls produce unreliable tone detection","No distinction between authentic emotion and performative tone (e.g., a rep faking enthusiasm)","Requires manual labeling of training data — typically 500-1000 labeled examples needed for meaningful improvement","Model retraining takes 2-4 hours — not suitable for real-time model updates","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"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":"2026-05-24T12:16:31.857Z","last_scraped_at":"2026-04-05T13:23:42.560Z","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=meetraai","compare_url":"https://unfragile.ai/compare?artifact=meetraai"}},"signature":"D8/GO6SSf2W1mz0Kh4f3PXoPzc3TaixVb0pf4YvYk3vQf3iglpJlnbuKUpFLJifMNT7V8rz9F1QT3KFJwTL1AQ==","signedAt":"2026-06-21T12:20:52.571Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/meetraai","artifact":"https://unfragile.ai/meetraai","verify":"https://unfragile.ai/api/v1/verify?slug=meetraai","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"}}