{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"rev-ai","slug":"rev-ai","name":"Rev AI","type":"api","url":"https://www.rev.ai","page_url":"https://unfragile.ai/rev-ai","categories":["voice-audio"],"tags":[],"pricing":{"model":"usage-based","free":true,"starting_price":"$0.02/min"},"status":"active","verified":false},"capabilities":[{"id":"rev-ai__cap_0","uri":"capability://data.processing.analysis.asynchronous.audio.to.text.transcription.with.speaker.diarization","name":"asynchronous audio-to-text transcription with speaker diarization","description":"Converts pre-recorded audio files (submitted via URL) to text through a job-based asynchronous API that returns speaker-segmented monologues with word-level timestamps. The system processes audio through proprietary models trained on 7M+ hours of human-verified speech data, returning structured JSON with speaker IDs and per-word timing information (ts/end_ts fields). Processing typically completes within ~1 minute for standard files, with results retrievable via polling or webhook callbacks.","intents":["I need to transcribe recorded phone calls, meetings, or interviews with automatic speaker identification","I want to extract dialogue with precise timing for video synchronization or editing workflows","I need to process large batches of audio files asynchronously without blocking my application","I need transcripts that preserve speaker turns and conversation structure for analysis"],"best_for":["teams building call center analytics platforms","developers creating meeting transcription tools (Zoom, Teams integrations)","media companies automating subtitle generation and speaker attribution","enterprises requiring HIPAA/SOC II compliant transcription for healthcare/financial audio"],"limitations":["Maximum file size unknown — documentation does not specify upload constraints","Maximum audio duration unknown — no documented limits on processing duration","Supported audio formats unknown — only .mp3 shown in examples, other formats undocumented","Polling-based status checks discouraged in production — requires webhook implementation for scalable workflows","Speaker diarization returns only integer speaker IDs, not speaker names or identification","No confidence scores or alternative hypotheses returned in transcript response"],"requires":["Valid Rev AI access token (Bearer token authentication)","Audio file accessible via publicly resolvable URL (direct file upload not documented)","For production use: webhook endpoint to receive job completion notifications","Language parameter (ISO 639-1 code, defaults to 'en')"],"input_types":["audio file (URL-based source via source_config.url parameter)","metadata string for job tagging/tracking"],"output_types":["JSON job object with id, status, created_on, language fields","JSON transcript with monologues array containing speaker ID and elements array with type, value, ts, end_ts"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rev-ai__cap_1","uri":"capability://data.processing.analysis.real.time.streaming.speech.to.text.transcription","name":"real-time streaming speech-to-text transcription","description":"Processes live audio streams with low-latency transcription output, enabling real-time caption generation and live meeting transcription. Implementation details (streaming protocol, latency guarantees, output format) are mentioned in documentation but not technically specified. Supports continuous audio input with incremental transcript updates.","intents":["I need live captions for video broadcasts or webinars as audio is being streamed","I want real-time transcription for live meetings with minimal latency","I need to build a live transcription feature into a video conferencing application","I want to capture and transcribe phone calls in real-time as they happen"],"best_for":["live streaming platforms (Twitch, YouTube Live, etc.)","video conferencing integrations requiring real-time captions","accessibility teams building live caption systems","contact centers needing real-time agent guidance based on call transcription"],"limitations":["Streaming latency unknown — no documented latency SLA or performance guarantees","Streaming protocol unspecified — WebSocket, gRPC, or other transport mechanism not documented","Output format for streaming results unknown — incremental vs. full transcript delivery not specified","Streaming endpoint details not provided in available documentation","No documented support for streaming-specific features like partial results or confidence scores"],"requires":["Valid Rev AI access token","Live audio stream source (protocol/format requirements unknown)","Network connectivity for continuous streaming","Language parameter (ISO 639-1 code)"],"input_types":["audio stream (protocol and format unspecified)"],"output_types":["real-time transcript updates (format unspecified)"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rev-ai__cap_10","uri":"capability://safety.moderation.compliance.certified.transcription.with.encryption.and.data.residency","name":"compliance-certified transcription with encryption and data residency","description":"Provides transcription service with compliance certifications (HIPAA, SOC II, GDPR, PCI DSS) and security features including encryption at rest and in transit. Supports on-premises and cloud deployment options enabling data residency requirements. 99.99% uptime SLA ensures service reliability for regulated industries. Enables secure handling of sensitive audio content (healthcare, financial, legal).","intents":["I need to transcribe healthcare audio while maintaining HIPAA compliance","I want to process financial or payment card data in transcripts securely","I need to ensure GDPR compliance for EU customer data","I want to deploy transcription on-premises for data sovereignty requirements"],"best_for":["healthcare organizations transcribing patient interactions","financial services firms processing regulated audio content","EU-based organizations requiring GDPR compliance","enterprises with strict data residency requirements","organizations requiring on-premises deployment for security"],"limitations":["Specific compliance details unknown — no documented HIPAA BAA terms or SOC II audit scope","Data residency options unknown — no documented geographic regions or data center locations","On-premises deployment details unknown — no documented deployment architecture or requirements","Encryption key management unknown — no documented key rotation or management procedures","Audit logging unknown — no documented audit trail or compliance reporting capabilities","Data retention policies unknown — no documented data deletion or retention schedules"],"requires":["Valid Rev AI access token","For HIPAA: Business Associate Agreement (BAA) execution","For on-premises: deployment infrastructure and configuration","Compliance certification verification"],"input_types":["audio file (URL-based or on-premises)"],"output_types":["encrypted transcript with compliance audit trail"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rev-ai__cap_11","uri":"capability://tool.use.integration.mcp.integration.for.ai.assistant.context.access","name":"mcp integration for ai assistant context access","description":"Integrates with Model Context Protocol (MCP) enabling AI assistants (Cursor, VS Code) to access Rev AI transcription capabilities through standardized protocol. Installable on Cursor and VS Code enabling developers to invoke transcription from within IDE. Specific MCP capabilities and integration details not documented.","intents":["I want to transcribe audio directly from my IDE without leaving development environment","I need to access transcription results in my AI assistant context","I want to integrate transcription into my Cursor or VS Code workflow","I need to reference transcribed content in my development tasks"],"best_for":["developers using Cursor IDE with AI assistance","VS Code users integrating transcription into development workflows","teams building AI-assisted development environments","developers needing transcription context in coding tasks"],"limitations":["MCP capabilities unknown — no documented specific capabilities exposed via MCP","Installation process unknown — no documented installation or configuration steps","Integration scope unknown — unclear which Rev AI features are accessible via MCP","Authentication mechanism unknown — no documented token management for MCP","Performance characteristics unknown — no documented latency or throughput for MCP calls"],"requires":["Cursor IDE or VS Code installation","Valid Rev AI access token","MCP server installation (process undocumented)"],"input_types":["audio file reference or URL (via MCP protocol)"],"output_types":["transcript or transcription job status (via MCP protocol)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rev-ai__cap_12","uri":"capability://tool.use.integration.llm.integration.with.transcript.export.for.ai.processing","name":"llm integration with transcript export for ai processing","description":"Enables direct integration with LLM platforms (ChatGPT, Claude) through 'Copy for LLM' and 'Open in ChatGPT/Claude' options. Allows transcripts to be exported in LLM-compatible format for downstream AI processing, summarization, or analysis. Integration mechanism and export format not documented.","intents":["I want to send transcripts to ChatGPT for summarization or analysis","I need to process transcripts with Claude for content extraction","I want to use LLMs to analyze sentiment or extract insights from transcripts","I need to generate meeting summaries or action items from transcribed content"],"best_for":["teams using ChatGPT or Claude for transcript analysis","developers building AI-powered transcript processing pipelines","organizations automating meeting summary generation","users wanting quick LLM-based transcript analysis"],"limitations":["Export format unknown — no documented format for LLM-compatible transcript export","Integration mechanism unknown — unclear if 'Copy for LLM' is manual copy-paste or API-driven","Token limit handling unknown — no documented approach to LLM context window limits","Authentication flow unknown — no documented OAuth or token management for LLM integration","Supported LLMs unknown — only ChatGPT and Claude mentioned, other LLMs undocumented"],"requires":["Valid Rev AI access token","ChatGPT or Claude account (for respective integrations)","Completed transcription job"],"input_types":["transcript from completed transcription job"],"output_types":["LLM-compatible transcript format (format unknown)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rev-ai__cap_13","uri":"capability://automation.workflow.pay.as.you.go.usage.based.pricing.with.free.tier","name":"pay-as-you-go usage-based pricing with free tier","description":"Implements usage-based pricing model where customers pay for transcription based on consumption (billing unit unknown — likely per-minute or per-request). Free tier available for account signup with limits unknown. Enterprise pricing available via custom negotiation. Pricing details not publicly documented in available materials.","intents":["I want to try Rev AI transcription without upfront commitment","I need predictable per-unit pricing for transcription costs","I want volume discounts for large-scale transcription","I need custom pricing for enterprise deployment"],"best_for":["startups and small teams evaluating transcription services","organizations with variable transcription volume","enterprises requiring custom pricing and SLAs","developers building transcription features into products"],"limitations":["Pricing rates unknown — no documented per-minute or per-request rates","Free tier limits unknown — no documented free tier usage limits or duration","Billing unit unknown — unclear if billing is per-minute, per-request, or per-hour","Volume discount tiers unknown — no documented discount structure for high-volume usage","Enterprise pricing unknown — custom pricing requires sales contact","Billing cycle unknown — no documented monthly, annual, or usage-based billing cycle"],"requires":["Valid Rev AI account","Payment method for paid tier","For enterprise: sales contact and negotiation"],"input_types":["transcription job submissions"],"output_types":["usage-based billing charges (rate structure unknown)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rev-ai__cap_2","uri":"capability://data.processing.analysis.custom.vocabulary.injection.for.domain.specific.terminology","name":"custom vocabulary injection for domain-specific terminology","description":"Allows users to inject domain-specific vocabulary, acronyms, and terminology into the transcription model to improve accuracy for specialized language (medical, legal, technical jargon). Implementation mechanism (vocabulary file format, injection method, model adaptation approach) not documented. Improves WER for domain-specific terms by providing context to the underlying ASR model.","intents":["I need accurate transcription of medical terminology in doctor-patient conversations","I want legal documents transcribed with correct legal terminology and case names","I need technical product names and acronyms transcribed correctly in engineering meetings","I want to improve transcription accuracy for industry-specific jargon in my domain"],"best_for":["healthcare organizations transcribing clinical notes and patient interactions","legal firms automating deposition and court proceeding transcription","technical companies transcribing engineering discussions with product/technology names","enterprises with proprietary terminology or brand names requiring consistent transcription"],"limitations":["Vocabulary file format unknown — no specification for how to structure custom vocabulary","Vocabulary size limits unknown — no documented maximum vocabulary entries or token limits","Model adaptation mechanism unknown — unclear if vocabulary is applied per-job or globally","Vocabulary management interface unknown — no documented API for CRUD operations on vocabulary lists","No documented performance impact — unclear if custom vocabulary increases processing latency"],"requires":["Valid Rev AI access token","Custom vocabulary list (format unspecified)","Domain-specific terminology or acronym definitions"],"input_types":["vocabulary list (format unknown)"],"output_types":["improved transcript with custom terminology applied"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rev-ai__cap_3","uri":"capability://data.processing.analysis.forced.alignment.with.word.level.precision.timestamps","name":"forced alignment with word-level precision timestamps","description":"Generates precise word-level timing information by aligning transcribed text back to the original audio waveform, enabling frame-accurate subtitle generation and video synchronization. Uses forced alignment algorithms to map each word to its exact start/end timestamps in the audio. Output includes ts (start time in seconds) and end_ts (end time in seconds) for every transcribed word element.","intents":["I need to generate subtitles with frame-accurate timing for video content","I want to synchronize transcript text with video playback for editing or accessibility","I need to extract specific audio segments based on word-level timing for clip generation","I want to build interactive transcripts where clicking a word jumps to that point in the audio"],"best_for":["video production and post-production teams creating subtitled content","accessibility teams building synchronized captions for video platforms","media companies automating subtitle generation workflows","developers building interactive transcript players or video editors"],"limitations":["Timestamp precision unknown — no documented accuracy or granularity (millisecond vs. frame-level)","Alignment accuracy unknown — no documented error rates or edge cases (overlapping speech, silence)","Forced alignment mechanism unknown — unclear if applied automatically or requires separate API call","No documented support for multi-speaker alignment — unclear how overlapping speech is handled","Timestamp format precision unknown — floating-point seconds may lack sufficient granularity for video frames"],"requires":["Valid Rev AI access token","Audio file for transcription (via URL)","Transcription job completion (forced alignment applied to transcript output)"],"input_types":["audio file (URL-based)"],"output_types":["transcript with ts and end_ts fields for each word element (float seconds)"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rev-ai__cap_4","uri":"capability://data.processing.analysis.topic.extraction.from.transcribed.content","name":"topic extraction from transcribed content","description":"Analyzes transcribed text to automatically extract key topics, themes, and subject matter discussed in the audio. Implementation approach (NLP model type, topic taxonomy, extraction algorithm) not documented. Enables automatic categorization and content discovery without manual review.","intents":["I want to automatically categorize customer support calls by topic for routing and analysis","I need to extract discussion topics from meeting transcripts for knowledge management","I want to identify key subjects in interviews or focus groups for content analysis","I need to automatically tag transcribed content for search and discovery"],"best_for":["customer service teams analyzing call center conversations","market research firms processing interview and focus group transcripts","knowledge management teams organizing meeting notes and discussions","content platforms automating metadata generation for transcribed audio"],"limitations":["Topic taxonomy unknown — no documented list of supported topics or categorization scheme","Extraction mechanism unknown — unclear if rule-based, ML-based, or hybrid approach","Confidence scores unknown — no documented confidence or relevance scoring for extracted topics","Language support unknown — unclear if topic extraction available for all 57+ supported languages","API integration unknown — no documented endpoint or request/response format for topic extraction"],"requires":["Valid Rev AI access token","Completed transcription job (topic extraction applied to transcript)"],"input_types":["transcribed text from completed transcription job"],"output_types":["topic list (format unknown)"],"categories":["data-processing-analysis","natural-language-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rev-ai__cap_5","uri":"capability://data.processing.analysis.sentiment.analysis.on.transcribed.speech","name":"sentiment analysis on transcribed speech","description":"Analyzes emotional tone and sentiment expressed in transcribed audio content, enabling automatic detection of customer satisfaction, agent performance, or conversation sentiment. Implementation (sentiment model type, granularity level, scoring approach) not documented. Provides sentiment classification at conversation or segment level.","intents":["I want to measure customer satisfaction from call center conversations automatically","I need to identify negative sentiment in customer interactions for quality assurance","I want to analyze agent performance based on customer sentiment in calls","I need to detect emotional tone in interviews or user research sessions"],"best_for":["contact centers measuring customer satisfaction and agent performance","customer success teams identifying at-risk customer relationships","market research firms analyzing emotional responses in interviews","HR teams evaluating communication quality in recorded interactions"],"limitations":["Sentiment granularity unknown — unclear if sentiment is per-conversation, per-speaker, or per-segment","Sentiment scale unknown — binary (positive/negative), ternary (positive/neutral/negative), or continuous score","Confidence scores unknown — no documented confidence or reliability metrics for sentiment predictions","Language support unknown — unclear if sentiment analysis available for all 57+ supported languages","Sarcasm/irony handling unknown — no documented approach to detecting non-literal sentiment","API integration unknown — no documented endpoint or request/response format"],"requires":["Valid Rev AI access token","Completed transcription job (sentiment analysis applied to transcript)"],"input_types":["transcribed text from completed transcription job"],"output_types":["sentiment classification (format and scale unknown)"],"categories":["data-processing-analysis","natural-language-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rev-ai__cap_6","uri":"capability://data.processing.analysis.automatic.language.identification.from.audio","name":"automatic language identification from audio","description":"Detects the language spoken in audio content and returns ISO 639-1 language code, enabling automatic routing to language-specific transcription models. Operates on audio stream without requiring pre-specification of language. Supports 57+ languages with automatic detection enabling multi-language batch processing.","intents":["I need to automatically detect the language of incoming calls for routing to correct transcription model","I want to process multilingual audio files without manually specifying the language","I need to identify language switches or code-switching in multilingual conversations","I want to automatically categorize audio content by language for batch processing"],"best_for":["global contact centers handling calls in multiple languages","international organizations processing multilingual audio content","platforms accepting user-generated audio without language metadata","developers building language-agnostic transcription pipelines"],"limitations":["Language list unknown — only '57+ languages' documented, specific language codes not provided","Detection accuracy unknown — no documented accuracy metrics or confidence scores","Code-switching handling unknown — unclear how language switches within single audio are handled","Minimum audio duration unknown — unclear how much audio required for reliable detection","Confidence scores unknown — no documented confidence metric for language identification","API integration unknown — unclear if language detection is automatic or requires separate call"],"requires":["Valid Rev AI access token","Audio file for transcription (language detection applied automatically)"],"input_types":["audio file (URL-based)"],"output_types":["ISO 639-1 language code (e.g., 'en', 'es', 'fr')"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rev-ai__cap_7","uri":"capability://automation.workflow.job.based.asynchronous.api.with.webhook.notifications","name":"job-based asynchronous api with webhook notifications","description":"Implements job-based asynchronous processing pattern where audio transcription jobs are submitted via POST endpoint, tracked via job ID, and results retrieved when complete. Supports two notification modes: polling via GET endpoint (discouraged in production) or webhook callbacks to user-specified endpoint. Job object includes id, status (in_progress/transcribed), created_on timestamp, metadata field for tagging, and language specification.","intents":["I need to submit multiple audio files for transcription without blocking my application","I want to receive notifications when transcription jobs complete without polling","I need to track transcription job status and retrieve results asynchronously","I want to tag and organize transcription jobs with metadata for tracking"],"best_for":["backend services processing large volumes of audio files","batch processing pipelines transcribing thousands of files","applications requiring non-blocking transcription workflows","teams building production systems requiring webhook-based event handling"],"limitations":["Polling discouraged in production — documentation explicitly warns against polling-based status checks, requiring webhook implementation","Webhook payload schema unknown — no documented structure for webhook callback payloads","Webhook configuration mechanism unknown — unclear how webhooks are registered or managed","Webhook retry logic unknown — no documented retry behavior or failure handling","Job retention unknown — no documented how long job records are retained after completion","Concurrent job limits unknown — no documented limits on simultaneous job submissions","Rate limiting unknown — no documented rate limits or quota information"],"requires":["Valid Rev AI access token (Bearer token authentication)","Audio file accessible via publicly resolvable URL","For production: webhook endpoint to receive job completion notifications","Metadata string for job tagging (optional)"],"input_types":["JSON request with source_config.url, optional metadata, language parameter"],"output_types":["JSON job object with id, status, created_on, name, metadata, type, language fields"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rev-ai__cap_8","uri":"capability://data.processing.analysis.transcript.retrieval.with.structured.monologue.output","name":"transcript retrieval with structured monologue output","description":"Retrieves completed transcription results in structured JSON format with monologues array containing speaker-segmented dialogue. Each monologue includes speaker integer ID and elements array with word-level details (type, value, ts, end_ts). Uses custom Accept header (application/vnd.rev.transcript.v1.0+json) for versioned API response format. Enables direct integration with downstream systems without parsing unstructured text.","intents":["I need to retrieve transcripts in structured format for programmatic processing","I want to extract speaker-segmented dialogue for conversation analysis","I need word-level timestamps for each transcribed word for synchronization","I want to integrate transcription results directly into my application database"],"best_for":["developers building transcript processing pipelines","applications requiring structured transcript data for analysis","systems integrating transcription with downstream NLP or analytics","platforms building interactive transcript players or editors"],"limitations":["Speaker identification limited to integer IDs — no speaker names or identification metadata","No confidence scores — transcript elements lack confidence metrics for accuracy assessment","No alternative hypotheses — single best hypothesis returned, no N-best alternatives","Monologue structure unclear — no documented rules for monologue segmentation (speaker change, silence duration)","Element type limited to 'text' — no documented support for other element types (silence, noise, etc.)","Punctuation handling undocumented — claimed but no specification of punctuation rules or accuracy"],"requires":["Valid Rev AI access token","Completed transcription job ID","Accept header: application/vnd.rev.transcript.v1.0+json"],"input_types":["job ID (path parameter)"],"output_types":["JSON with monologues array containing speaker ID and elements array with type, value, ts, end_ts"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rev-ai__cap_9","uri":"capability://data.processing.analysis.multi.language.transcription.across.57.languages","name":"multi-language transcription across 57+ languages","description":"Supports transcription in 57+ languages with language specification via ISO 639-1 code parameter. Default language is English ('en'). Models trained on diverse speech data from 7M+ hour human-verified corpus enabling accurate transcription across languages with claimed bias mitigation across ethnic backgrounds, nationalities, genders, and accents. Language parameter specified in job submission and returned in job metadata.","intents":["I need to transcribe audio in languages other than English","I want to process multilingual content with language-specific models","I need to support global customers in their native languages","I want to transcribe content with diverse accents and dialects accurately"],"best_for":["global organizations supporting customers in multiple languages","international media companies transcribing multilingual content","research teams studying speech patterns across languages","platforms serving non-English speaking users"],"limitations":["Language list unknown — only '57+ languages' documented, specific supported languages not provided","Language-specific accuracy unknown — no documented WER metrics per language","Accent handling unknown — claimed bias mitigation but no documented approach or metrics","Language mixing unknown — no documented support for code-switching or multilingual utterances","Language detection accuracy unknown — automatic detection available but accuracy not documented","Regional dialect support unknown — unclear if regional variants (e.g., Spanish variants) are supported"],"requires":["Valid Rev AI access token","ISO 639-1 language code (e.g., 'en', 'es', 'fr', 'de', 'ja')","Audio content in specified language"],"input_types":["audio file in target language (URL-based)"],"output_types":["transcript in target language with language code in job metadata"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"rev-ai__headline","uri":"capability://voice.audio.speech.to.text.api.for.real.time.and.asynchronous.transcription","name":"speech-to-text api for real-time and asynchronous transcription","description":"Rev AI offers a powerful speech-to-text API that leverages extensive human transcription data to provide real-time and asynchronous audio transcription, along with advanced features like topic extraction and sentiment analysis, tailored for conversational and telephony audio.","intents":["best speech-to-text API","speech-to-text for real-time transcription","top audio transcription services","best API for audio analysis","speech recognition API for developers"],"best_for":["real-time transcription","telephony audio","custom vocabulary needs"],"limitations":[],"requires":[],"input_types":["audio files","live audio streams"],"output_types":["transcripts","topic data","sentiment analysis"],"categories":["voice-audio"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Valid Rev AI access token (Bearer token authentication)","Audio file accessible via publicly resolvable URL (direct file upload not documented)","For production use: webhook endpoint to receive job completion notifications","Language parameter (ISO 639-1 code, defaults to 'en')","Valid Rev AI access token","Live audio stream source (protocol/format requirements unknown)","Network connectivity for continuous streaming","Language parameter (ISO 639-1 code)","For HIPAA: Business Associate Agreement (BAA) execution","For on-premises: deployment infrastructure and configuration"],"failure_modes":["Maximum file size unknown — documentation does not specify upload constraints","Maximum audio duration unknown — no documented limits on processing duration","Supported audio formats unknown — only .mp3 shown in examples, other formats undocumented","Polling-based status checks discouraged in production — requires webhook implementation for scalable workflows","Speaker diarization returns only integer speaker IDs, not speaker names or identification","No confidence scores or alternative hypotheses returned in transcript response","Streaming latency unknown — no documented latency SLA or performance guarantees","Streaming protocol unspecified — WebSocket, gRPC, or other transport mechanism not documented","Output format for streaming results unknown — incremental vs. full transcript delivery not specified","Streaming endpoint details not provided in available documentation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.061Z","last_scraped_at":null,"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=rev-ai","compare_url":"https://unfragile.ai/compare?artifact=rev-ai"}},"signature":"sO2xsFYfb+0K8ekl4ML/sltqu4BUzAnahn8zt1siN0WWJXyC20+AzTEbBaBbD+V+JDq4BYQe1t354yeGfxHiDg==","signedAt":"2026-06-22T00:14:15.710Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rev-ai","artifact":"https://unfragile.ai/rev-ai","verify":"https://unfragile.ai/api/v1/verify?slug=rev-ai","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"}}