{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"assemblyai","slug":"assemblyai","name":"AssemblyAI","type":"api","url":"https://www.assemblyai.com","page_url":"https://unfragile.ai/assemblyai","categories":["voice-audio"],"tags":[],"pricing":{"model":"usage-based","free":true,"starting_price":"$0.12/hr"},"status":"active","verified":false},"capabilities":[{"id":"assemblyai__cap_0","uri":"capability://data.processing.analysis.pre.recorded.audio.speech.to.text.transcription.with.multi.language.support","name":"pre-recorded audio speech-to-text transcription with multi-language support","description":"Converts pre-recorded audio files to text using Universal-3 Pro or Universal-2 models via asynchronous REST API processing. Universal-3 Pro achieves market-leading accuracy across 6 languages (English, Spanish, German, French, Italian, Portuguese) with context-aware prompting; Universal-2 supports 99 languages at lower cost. Processing returns word-level timestamps, speaker segmentation, and confidence scores via polling or webhook callbacks.","intents":["I need to transcribe recorded meetings, interviews, or podcasts into searchable text","I want to support multiple languages in my transcription pipeline without building language-specific models","I need word-level timing data to sync transcripts with video playback","I want to reduce transcription costs while maintaining accuracy for non-English content"],"best_for":["teams building meeting intelligence or podcast platforms","enterprises processing multilingual audio archives","developers needing accurate transcription without ML infrastructure","cost-sensitive applications serving non-English markets"],"limitations":["Universal-3 Pro limited to 6 languages; Universal-2 trades accuracy for breadth across 99 languages","Asynchronous processing adds latency (specific SLA unknown); not suitable for real-time transcription","Maximum audio duration and file size constraints not documented","Keyterms prompting limited to 1000 words/phrases with 6-word maximum per phrase","No built-in batch processing API documented; requires sequential requests or custom orchestration"],"requires":["AssemblyAI API key (obtained via account signup)","Audio file in supported format (specific formats not documented)","HTTP client library or AssemblyAI SDK (Python or JavaScript)","Polling mechanism or webhook endpoint for result retrieval"],"input_types":["audio file (format unspecified in documentation)","audio URL (remote file reference)","audio stream (for pre-recorded async processing)"],"output_types":["JSON transcript with word-level timestamps","speaker labels and diarization data","confidence scores per word","detected entities (names, companies, emails, dates, locations)"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai__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 via WebSocket or streaming protocol, delivering near-real-time transcription with word-level timestamps and speaker diarization. Uses Universal-3 Pro Streaming model with same context-aware prompting and entity detection as pre-recorded variant. Designed for live call transcription, voice conference capture, and real-time voice agent interactions.","intents":["I need to transcribe live phone calls or video conferences as they happen","I want to build a real-time meeting assistant that captures and processes audio simultaneously","I need to detect entities and extract insights from live conversations in-stream","I'm building a voice agent that needs to understand user speech in real-time"],"best_for":["contact center and customer service platforms requiring live call transcription","video conferencing integrations (Zoom, Teams, Google Meet)","voice agent platforms and IVR systems","live event captioning and accessibility applications"],"limitations":["Streaming pricing not documented; cost model unclear vs pre-recorded ($0.21/hr baseline)","Requires persistent WebSocket connection; network interruptions may cause transcript loss","Latency profile not specified; actual real-time performance unknown","Speaker diarization in streaming mode may have accuracy degradation vs pre-recorded","No documented maximum stream duration or concurrent stream limits"],"requires":["AssemblyAI API key","WebSocket client library (native browser WebSocket or Node.js ws module)","Audio stream source (microphone, phone line, or media server)","Network connection with stable bandwidth for continuous streaming"],"input_types":["audio stream (WebSocket binary frames)","PCM audio data (sample rate and bit depth unspecified)","live microphone input","phone call audio (via SIP or media server integration)"],"output_types":["partial transcripts (interim results during streaming)","final transcripts (confirmed text after phrase completion)","word-level timestamps","speaker labels and diarization","detected entities in real-time"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai__cap_10","uri":"capability://text.generation.language.transcript.summarization.and.key.insight.extraction","name":"transcript summarization and key insight extraction","description":"Automatically generates summaries of transcribed conversations and extracts key insights including action items, decisions, topics discussed, and sentiment trends. Summarization works on full transcripts or conversation segments. Returns structured summaries with configurable detail levels (brief, detailed, executive summary). Claimed in artifact description but detailed implementation unknown.","intents":["I need to generate meeting summaries automatically for busy executives","I want to extract action items and decisions from customer calls","I'm building a meeting intelligence tool that identifies key topics and outcomes","I need to create searchable summaries of large audio archives"],"best_for":["meeting intelligence and productivity platforms","customer success and account management tools","legal and compliance documentation","knowledge management and enterprise search"],"limitations":["Summarization implementation details not documented (abstractive vs extractive, model used, etc.)","Summary quality and accuracy metrics not provided","Configurable summary length or detail levels not documented","No documented handling of multi-topic or long conversations","Pricing for summarization feature not documented","Integration with external LLMs for custom summarization unknown"],"requires":["AssemblyAI API key","Summarization feature enabled (may require separate enablement)","Transcription with summarization enabled"],"input_types":["transcript text","optional: summary configuration (length, detail level, focus areas)"],"output_types":["transcript summary (text)","extracted action items (structured list)","key topics and decisions (structured data)","sentiment summary (optional)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai__cap_11","uri":"capability://data.processing.analysis.sentiment.analysis.and.emotion.detection","name":"sentiment analysis and emotion detection","description":"Analyzes emotional tone and sentiment in transcribed conversations, detecting speaker sentiment (positive, negative, neutral) and emotional states (anger, frustration, satisfaction, etc.). Returns sentiment scores per speaker, conversation segment, or overall. Enables customer satisfaction measurement, agent performance evaluation, and conversation quality assessment.","intents":["I need to measure customer satisfaction from call recordings","I want to identify frustrated or angry customers for escalation","I'm building a quality assurance system that scores agent empathy and tone","I need to analyze sentiment trends across customer interactions"],"best_for":["contact center quality assurance and performance management","customer satisfaction and NPS measurement","customer success and retention analytics","employee training and coaching platforms"],"limitations":["Sentiment detection accuracy not documented; performance on sarcasm, mixed sentiment unknown","Emotion categories and detection methodology not specified","No documented handling of multilingual sentiment (primarily English)","Sentiment scores may not correlate with actual customer satisfaction (no validation data)","No contextual sentiment analysis (e.g., distinguishing 'angry at situation' vs 'angry at agent')","Pricing and availability of sentiment analysis feature not documented"],"requires":["AssemblyAI API key","Sentiment analysis feature enabled (may require separate enablement)","Transcription with sentiment detection enabled"],"input_types":["transcript text","optional: speaker labels (for per-speaker sentiment)"],"output_types":["sentiment scores (0-1 per sentiment type)","sentiment classification (positive, negative, neutral)","emotion detection (anger, frustration, satisfaction, etc.)","per-speaker sentiment (if diarization enabled)","sentiment timeline (sentiment changes over conversation)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai__cap_12","uri":"capability://data.processing.analysis.word.level.timestamp.and.temporal.alignment","name":"word-level timestamp and temporal alignment","description":"Provides precise word-level timestamps for every word in the transcript, enabling exact audio segment retrieval and temporal alignment with video or other media. Timestamps are returned in milliseconds with confidence scores. Enables video subtitle generation, audio clip extraction, and precise quote verification.","intents":["I need to generate video subtitles that sync with audio timing","I want to extract specific audio clips based on transcript keywords","I'm building a video editor that needs frame-accurate subtitle placement","I need to verify quotes by retrieving the exact audio segment"],"best_for":["video production and subtitle generation","media editing and post-production tools","video search and indexing platforms","podcast and media player applications"],"limitations":["Timestamp accuracy not documented; drift or sync issues with long audio unknown","Timestamps in milliseconds; no sub-millisecond precision for frame-accurate video sync","No documented handling of variable playback speeds or time-stretched audio","Confidence scores for timestamps not documented","No automatic subtitle file generation (SRT, VTT); requires custom formatting"],"requires":["AssemblyAI API key","Transcription with word-level timestamps enabled (default)","Audio file with consistent playback speed"],"input_types":["audio file or stream"],"output_types":["JSON array of words with start/end timestamps (milliseconds)","confidence scores per word (optional)","speaker labels with timestamps (if diarization enabled)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai__cap_13","uri":"capability://data.processing.analysis.medical.domain.transcription.with.specialized.vocabulary","name":"medical-domain transcription with specialized vocabulary","description":"Specialized transcription mode optimized for medical conversations including clinical terminology, drug names, medical procedures, and patient information. Uses domain-specific language model tuning and medical vocabulary injection. Adds $0.15/hour to transcription cost. Supports both Universal-3 Pro and Universal-2 models.","intents":["I need accurate transcription of doctor-patient conversations with medical terminology","I'm building a medical documentation system that captures clinical notes from voice","I want to transcribe telemedicine calls with proper drug names and procedure terminology","I need HIPAA-compliant transcription with automatic PII redaction for healthcare"],"best_for":["healthcare providers and telemedicine platforms","medical transcription services","clinical documentation and EHR integration","healthcare compliance and quality assurance"],"limitations":["Medical mode accuracy not documented; no comparison with general-purpose models","Supported medical specialties not documented (cardiology, oncology, psychiatry, etc.)","Medical vocabulary limited to standard terminology; rare diseases or experimental treatments may not be recognized","No integration with medical knowledge bases or drug databases","Adds $0.15/hour cost (significant premium over base $0.21/hr); cumulative with other add-ons","HIPAA compliance not explicitly documented; PII redaction requires separate enablement"],"requires":["AssemblyAI API key","Medical mode explicitly enabled in API request","Medical-domain audio (doctor-patient conversations, clinical notes, etc.)"],"input_types":["medical conversation audio","optional: medical context or specialty (format unknown)"],"output_types":["transcript with medical terminology","detected medical entities (drugs, procedures, conditions)","optional: PII redaction for HIPAA compliance"],"categories":["data-processing-analysis","domain-specific-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai__cap_14","uri":"capability://tool.use.integration.sdk.and.integration.support.with.python.and.javascript","name":"sdk and integration support with python and javascript","description":"Official SDKs for Python and JavaScript enable developers to integrate AssemblyAI transcription into applications without building raw HTTP clients. SDKs provide type-safe API bindings, automatic retry logic, error handling, and streaming support. Integrations with LiveKit and Pipecat frameworks enable voice agent and real-time communication use cases.","intents":["I want to integrate speech-to-text into my Python or JavaScript application quickly","I need type-safe API bindings to avoid HTTP request errors","I'm building a voice agent with LiveKit or Pipecat and need transcription","I want automatic retry logic and error handling without custom implementation"],"best_for":["Python developers building backend transcription services","JavaScript/Node.js developers building web or Electron applications","teams using LiveKit for real-time communication","developers building voice agents with Pipecat framework"],"limitations":["SDKs limited to Python and JavaScript; no official Go, Rust, or Java SDKs","SDK version numbers and maturity levels not documented","Feature parity between SDKs not documented; Python may have features JavaScript lacks","No async/await support documented for JavaScript SDK (may use callbacks)","LiveKit and Pipecat integrations may require specific versions or configurations","No documented SDK rate limiting or quota management"],"requires":["Python 3.7+ (for Python SDK) or Node.js 14+ (for JavaScript SDK)","AssemblyAI API key","SDK installation via pip (Python) or npm (JavaScript)"],"input_types":["audio file path or URL","audio stream (for streaming transcription)","configuration parameters (language, model, features)"],"output_types":["Transcript object with word-level timestamps","speaker diarization data (if enabled)","entity detection results (if enabled)","sentiment and moderation scores (if enabled)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai__cap_15","uri":"capability://tool.use.integration.mcp.model.context.protocol.integration.for.ai.agents","name":"mcp (model context protocol) integration for ai agents","description":"Provides Model Context Protocol (MCP) integration enabling AI agents and LLMs to access AssemblyAI transcription capabilities through a standardized interface. Documentation available at `/llms.txt` and `/llms-full.txt` endpoints. Enables agents to transcribe audio, extract insights, and perform speech understanding tasks as part of multi-step reasoning workflows.","intents":["I want my AI agent to transcribe audio files as part of a larger task","I need to integrate speech understanding into an agentic workflow","I'm building an AI assistant that processes voice input alongside text","I want to use AssemblyAI capabilities in Claude, GPT-4, or other LLM-based agents"],"best_for":["AI agent and LLM application developers","teams building multi-modal AI assistants","developers using Claude, GPT-4, or open-source LLMs with tool use","enterprises building AI-powered automation workflows"],"limitations":["MCP specification and available tools not documented in source material","Integration with specific LLM providers (OpenAI, Anthropic, etc.) not documented","No documented support for streaming transcription via MCP","Agent-level error handling and retry logic not documented","No documented rate limiting or quota management for agent-initiated requests","MCP documentation location (`/llms.txt`, `/llms-full.txt`) suggests incomplete or evolving specification"],"requires":["AssemblyAI API key","MCP-compatible AI agent framework (Claude, GPT-4 with tool use, etc.)","Access to AssemblyAI MCP documentation at `/llms.txt` or `/llms-full.txt`"],"input_types":["audio file path or URL (passed by agent)","transcription parameters (language, model, features)"],"output_types":["transcript text","structured insights (entities, sentiment, summary)","metadata (timestamps, speaker labels, confidence scores)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai__cap_2","uri":"capability://data.processing.analysis.speaker.diarization.and.multi.speaker.segmentation","name":"speaker diarization and multi-speaker segmentation","description":"Automatically detects and segments audio by speaker, labeling distinct speakers (Speaker A, Speaker B, etc.) with timestamps for when each speaker begins and ends. Works across both pre-recorded and streaming APIs. Adds $0.02/hour to transcription cost. Enables speaker role assignment via prompting (e.g., 'Speaker 1 is the customer, Speaker 2 is the agent').","intents":["I need to identify who said what in multi-speaker conversations (meetings, interviews, podcasts)","I want to extract customer-agent dialogue separately for quality assurance or training","I need to attribute quotes to specific speakers in meeting transcripts","I'm building a conversation analytics tool that requires speaker-level insights"],"best_for":["contact center quality assurance and training platforms","meeting intelligence and conversation analytics tools","podcast and interview transcription services","legal discovery and deposition transcription"],"limitations":["Accuracy degrades with >4 speakers or overlapping speech; no documented performance metrics","Speaker identification is numeric (Speaker 1, Speaker 2) without automatic name mapping; requires custom prompting or post-processing for role assignment","No speaker embedding or voice fingerprinting for cross-call speaker tracking","Adds $0.02/hour cost per transcription; cumulative with other add-ons (keyterms, medical mode)","No documented handling of background noise or audio quality impact on diarization accuracy"],"requires":["AssemblyAI API key","Audio with distinct speaker voices (overlapping speech may degrade accuracy)","Diarization explicitly enabled in API request (not default)","Optional: speaker role prompting for context (e.g., 'Speaker 1 is customer, Speaker 2 is support agent')"],"input_types":["multi-speaker audio file or stream","speaker role context (optional, via prompting)"],"output_types":["speaker labels per word/phrase (Speaker 1, Speaker 2, etc.)","speaker change timestamps","speaker-segmented transcript (grouped by speaker turns)"],"categories":["data-processing-analysis","audio-intelligence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai__cap_3","uri":"capability://data.processing.analysis.entity.detection.and.named.entity.recognition","name":"entity detection and named entity recognition","description":"Automatically extracts and labels named entities from transcribed speech including person names, company names, email addresses, phone numbers, dates, and locations. Works on both pre-recorded and streaming transcripts. Returns entity type, text, and timestamp for each detected entity. Enables domain-specific entity detection via custom keyterms prompting.","intents":["I need to extract contact information (names, emails, phone numbers) from customer calls","I want to identify companies and organizations mentioned in meetings for CRM integration","I need to flag dates and deadlines mentioned in conversations for task creation","I'm building a compliance tool that needs to detect and log all mentioned entities"],"best_for":["contact center and CRM integration platforms","meeting intelligence and action item extraction tools","compliance and audit logging systems","customer intelligence and market research platforms"],"limitations":["Entity types limited to predefined categories (person, company, email, phone, date, location); no custom entity types without keyterms prompting","Accuracy varies by entity type and audio quality; no documented precision/recall metrics","Keyterms prompting limited to 1000 words/phrases (6 words max per phrase); insufficient for large domain vocabularies","No entity linking (e.g., resolving 'John' to a specific person in a knowledge base)","Entities extracted only from transcribed text; no audio-level confidence scores"],"requires":["AssemblyAI API key","Transcription with entity detection enabled (default for Universal-3 Pro)","Optional: keyterms list for domain-specific entity injection (up to 1000 phrases)"],"input_types":["transcribed text (from speech-to-text output)","keyterms list (optional, for custom entity detection)"],"output_types":["JSON array of detected entities with type, text, and timestamp","entity type classification (person, company, email, phone, date, location)","word-level confidence scores (if available)"],"categories":["data-processing-analysis","information-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai__cap_4","uri":"capability://data.processing.analysis.filler.word.and.disfluency.detection","name":"filler word and disfluency detection","description":"Identifies and labels filler words (um, uh, like, you know) and speech disfluencies (stutters, repetitions, restarts, informal speech patterns) in transcripts. Marks these elements in the transcript output with special tags (e.g., `[um]`, `[uh]`) and provides word-level classification. Useful for speech quality analysis, speaker coaching, and conversation naturalness scoring.","intents":["I want to measure speaker confidence and fluency in customer calls or presentations","I need to identify coaching opportunities for sales teams based on speech patterns","I'm building a speech quality assessment tool for language learning platforms","I want to filter out filler words for cleaner transcript summaries"],"best_for":["sales training and coaching platforms","language learning and pronunciation assessment tools","podcast and media production (for editing and quality control)","speaker performance analytics and presentation coaching"],"limitations":["Detection accuracy not documented; false positive/negative rates unknown","Filler word detection may vary by accent, dialect, or language; primarily tuned for English","No quantitative fluency scoring (e.g., filler word density per minute); requires custom calculation","Disfluency classification limited to predefined categories; no custom disfluency types","No audio-level confidence scores for detected disfluencies"],"requires":["AssemblyAI API key","English-language audio (primary support; multilingual support unknown)","Filler word detection enabled in API request"],"input_types":["audio file or stream with speech content"],"output_types":["transcript with filler words and disfluencies tagged (e.g., `[um]`, `[uh]`)","word-level classification (filler, disfluency, or normal speech)","timestamps for each detected element"],"categories":["data-processing-analysis","speech-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai__cap_5","uri":"capability://data.processing.analysis.audio.event.tagging.and.sound.detection","name":"audio event tagging and sound detection","description":"Detects and tags non-speech audio events in transcripts such as background noise, music, silence, and other acoustic events. Marks these events with special tags (e.g., `[beep]`, `[music]`, `[silence]`) at the appropriate timestamps in the transcript. Useful for audio quality assessment, content moderation, and transcript cleanup.","intents":["I need to identify and flag low-quality audio segments in call recordings","I want to detect music or background noise that interferes with transcription accuracy","I'm building a content moderation tool that needs to flag beeps or censored audio","I need to clean up transcripts by removing or marking non-speech segments"],"best_for":["contact center quality assurance and audio monitoring","podcast and media production (for editing and cleanup)","content moderation and compliance platforms","audio quality assessment and diagnostic tools"],"limitations":["Event types limited to predefined categories (beep, music, silence, etc.); no custom event types","Detection accuracy not documented; sensitivity/specificity unknown","No audio event duration or intensity measurements; only presence/absence detection","May interfere with transcription accuracy if events are misclassified as speech","No configurable sensitivity thresholds for event detection"],"requires":["AssemblyAI API key","Audio event tagging enabled in API request","Audio with detectable non-speech events"],"input_types":["audio file or stream with potential non-speech events"],"output_types":["transcript with audio events tagged (e.g., `[beep]`, `[music]`, `[silence]`)","event timestamps and duration","event type classification"],"categories":["data-processing-analysis","audio-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai__cap_6","uri":"capability://data.processing.analysis.context.aware.prompting.and.keyterms.injection","name":"context-aware prompting and keyterms injection","description":"Enables domain-specific vocabulary injection and context guidance via natural language prompts and keyterms lists. Developers provide up to 1000 custom words/phrases (max 6 words per phrase) and optional context prompts (e.g., 'This is a medical consultation') to improve transcription accuracy for specialized terminology. Works with Universal-3 Pro ($0.05/hr add-on) and pre-recorded transcription.","intents":["I need accurate transcription of medical, legal, or technical terminology in my domain","I want to improve recognition of product names, company names, or brand-specific jargon","I'm transcribing conversations with proper nouns or acronyms that standard models miss","I need to provide context to the model about the conversation topic for better accuracy"],"best_for":["healthcare and medical transcription services","legal and compliance documentation","technical support and engineering call transcription","enterprise platforms with domain-specific vocabulary"],"limitations":["Keyterms limited to 1000 words/phrases with 6-word maximum per phrase; insufficient for large domain vocabularies","Prompting effectiveness not quantified; no documented accuracy improvement metrics","Prompting available only for Universal-3 Pro (not Universal-2); adds $0.05/hr cost","No A/B testing or prompt optimization guidance provided","Prompts are static per request; no dynamic prompt adjustment based on conversation context"],"requires":["AssemblyAI API key","Universal-3 Pro model selected (required for prompting)","Keyterms list (up to 1000 phrases, 6 words max each)","Optional: context prompt describing the conversation topic or domain"],"input_types":["keyterms list (array of strings)","context prompt (natural language string)","audio file or stream"],"output_types":["improved transcription with domain-specific terminology","word-level confidence scores (potentially higher for prompted terms)"],"categories":["data-processing-analysis","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai__cap_7","uri":"capability://automation.workflow.voice.agent.api.with.streaming.interaction","name":"voice agent api with streaming interaction","description":"Provides a proprietary end-to-end voice agent stack built on streaming speech-to-text, enabling developers to build conversational voice agents without managing separate STT, NLU, and TTS components. Agents handle real-time audio input/output, speaker identification, and conversation state management. Priced at $4.50/hour of audio. Described as 'fastest path to a working voice agent' with production-ready reliability.","intents":["I want to build a voice agent or IVR system without integrating multiple APIs","I need a production-ready voice agent that handles real-time conversation","I'm building a customer service bot that needs to understand and respond to voice input","I want to deploy a voice agent quickly without managing speech recognition infrastructure"],"best_for":["contact center and customer service automation","IVR and voice bot platforms","healthcare appointment scheduling and patient engagement","enterprise voice assistant applications"],"limitations":["Proprietary model with no version information or training data transparency","Latency profile not documented; actual real-time performance unknown","No documented maximum concurrent agents or stream duration limits","Integration with external LLMs or NLU systems not documented","Pricing significantly higher than STT alone ($4.50/hr vs $0.21/hr); cost model for short interactions unclear","No documented customization options for agent behavior or personality"],"requires":["AssemblyAI API key","Voice Agent API access (may require separate enablement)","Audio input source (microphone, phone line, or media server)","Network connection with stable bandwidth for streaming"],"input_types":["audio stream (live voice input)","agent configuration (prompt, behavior parameters - format unknown)"],"output_types":["audio stream (agent voice response)","transcript of conversation","speaker identification and turn-taking"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai__cap_8","uri":"capability://safety.moderation.pii.redaction.and.sensitive.data.masking","name":"pii redaction and sensitive data masking","description":"Automatically detects and redacts personally identifiable information (PII) from transcripts including names, email addresses, phone numbers, social security numbers, credit card numbers, and other sensitive data. Redaction can be applied to transcript text (replacing with `[PII]` or similar) or audio (via beep/silence masking). Enables compliance with data privacy regulations (GDPR, HIPAA, CCPA).","intents":["I need to comply with GDPR/HIPAA by removing PII from stored transcripts","I want to share call recordings with team members without exposing customer personal data","I'm building a compliance system that automatically redacts sensitive information","I need to mask credit card numbers and SSNs from customer service recordings"],"best_for":["healthcare and HIPAA-compliant platforms","financial services and PCI-DSS compliance","contact centers with privacy requirements","GDPR-regulated enterprises in EU"],"limitations":["PII detection accuracy not documented; false negatives may leave sensitive data exposed","Redaction types limited to predefined PII categories; no custom sensitive data patterns","No documented handling of context-dependent PII (e.g., 'John' as a name vs 'john' as part of email)","Audio redaction (beeping) may impact transcription quality or speaker diarization","No audit trail or logging of redacted data for compliance verification","Pricing and availability of PII redaction feature not documented in source material"],"requires":["AssemblyAI API key","PII redaction feature enabled (may require separate enablement or tier)","Transcription with PII detection enabled"],"input_types":["audio file or stream","transcript text (for text-based redaction)"],"output_types":["redacted transcript with PII replaced (e.g., `[PII]`, `[NAME]`, `[EMAIL]`)","redacted audio (with beeps or silence replacing PII segments)","PII detection metadata (detected PII types and locations)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai__cap_9","uri":"capability://safety.moderation.content.moderation.and.policy.violation.detection","name":"content moderation and policy violation detection","description":"Automatically detects and flags content policy violations in transcripts including profanity, hate speech, harassment, and other prohibited content. Returns moderation scores and violation categories for each detected segment. Enables content filtering for compliance, brand safety, and user experience management.","intents":["I need to flag customer service calls with abusive language for escalation or training","I want to ensure user-generated voice content complies with platform policies","I'm building a safety system that detects hate speech or harassment in conversations","I need to monitor content for brand safety and compliance violations"],"best_for":["content moderation platforms and marketplaces","social media and user-generated content platforms","contact center quality assurance and safety monitoring","streaming and media platforms with content policies"],"limitations":["Moderation categories and sensitivity thresholds not documented","Accuracy metrics (precision, recall, false positive rates) not provided","No documented handling of context-dependent violations (e.g., quoting vs endorsing hate speech)","Moderation tuned for English; multilingual support unknown","No appeal or override mechanism for false positives","Pricing and availability of content moderation feature not documented"],"requires":["AssemblyAI API key","Content moderation feature enabled (may require separate enablement)","Transcription with moderation enabled"],"input_types":["audio file or stream","transcript text (for text-based moderation)"],"output_types":["moderation scores (0-1 confidence per violation type)","violation categories (profanity, hate speech, harassment, etc.)","flagged segments with timestamps","moderation metadata for logging and review"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"assemblyai__headline","uri":"capability://voice.audio.ai.powered.speech.understanding.and.transcription.api","name":"ai-powered speech understanding and transcription api","description":"AssemblyAI is an AI-driven platform that provides accurate speech-to-text transcription and advanced audio intelligence features like summarization, sentiment analysis, and entity detection through a simple REST API.","intents":["best speech-to-text API","speech understanding API for audio analysis","top audio intelligence tools","real-time transcription services","API for sentiment analysis in audio"],"best_for":["developers needing transcription services","businesses requiring audio insights"],"limitations":[],"requires":["audio input"],"input_types":["audio files","streaming audio"],"output_types":["text transcription","summarized content","sentiment analysis results"],"categories":["voice-audio"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["AssemblyAI API key (obtained via account signup)","Audio file in supported format (specific formats not documented)","HTTP client library or AssemblyAI SDK (Python or JavaScript)","Polling mechanism or webhook endpoint for result retrieval","AssemblyAI API key","WebSocket client library (native browser WebSocket or Node.js ws module)","Audio stream source (microphone, phone line, or media server)","Network connection with stable bandwidth for continuous streaming","Summarization feature enabled (may require separate enablement)","Transcription with summarization enabled"],"failure_modes":["Universal-3 Pro limited to 6 languages; 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