{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-limitless","slug":"limitless","name":"Limitless","type":"product","url":"https://www.limitless.ai/","page_url":"https://unfragile.ai/limitless","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-limitless__cap_0","uri":"capability://automation.workflow.multi.source.conversation.recording.and.capture","name":"multi-source conversation recording and capture","description":"Captures audio and conversation data from multiple input sources including native app integrations (Zoom, Teams, Google Meet), optional wearable device streaming, and direct application APIs. Uses background audio processing with automatic source detection to route conversations to appropriate transcription pipelines based on platform-specific metadata and codec support.","intents":["Record meetings across multiple platforms without manual intervention","Capture ambient conversations via wearable without app-specific setup","Automatically detect and route audio from different sources to appropriate processors","Maintain continuous recording across app switches and context changes"],"best_for":["Knowledge workers attending multiple daily meetings across platforms","Sales and customer success teams managing client interactions","Researchers and academics conducting interviews"],"limitations":["Wearable recording quality depends on device microphone and ambient noise conditions","Platform integrations require OAuth/API access — some enterprise environments restrict third-party app permissions","Audio capture may fail if application blocks recording or uses DRM-protected streams","Continuous wearable recording drains battery significantly — typically 4-6 hours per charge"],"requires":["iOS or Android device for mobile app","OAuth credentials for integrated platforms (Zoom, Microsoft Teams, Google Workspace)","Optional: compatible wearable device (specific models supported)","Microphone permissions enabled at OS level"],"input_types":["audio stream from meeting platforms","audio stream from wearable device","application metadata (meeting title, participants, timestamps)"],"output_types":["raw audio files (WAV, MP3, or platform-native codec)","structured meeting metadata (participants, duration, platform source)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-limitless__cap_1","uri":"capability://data.processing.analysis.real.time.speech.to.text.transcription.with.speaker.diarization","name":"real-time speech-to-text transcription with speaker diarization","description":"Converts captured audio to text using streaming transcription APIs with automatic speaker identification and turn-taking detection. Processes audio chunks in real-time or near-real-time, maintaining speaker context across conversation segments and handling overlapping speech through diarization models that identify distinct speakers without explicit labeling.","intents":["Generate searchable text from recorded conversations immediately after capture","Identify who said what in multi-participant meetings without manual annotation","Enable real-time transcription display during active meetings","Handle multiple speakers talking simultaneously with accurate attribution"],"best_for":["Teams with 2-10+ participants per meeting","Users needing immediate transcription for accessibility","Organizations requiring speaker attribution for compliance or documentation"],"limitations":["Diarization accuracy degrades with >8 simultaneous speakers or heavy accents outside training data","Real-time processing adds 2-5 second latency before text appears","Overlapping speech may be attributed to wrong speaker or merged into single speaker turn","Requires continuous network connection for cloud-based transcription — offline fallback limited","Specialized terminology (medical, legal, technical jargon) may require custom language models"],"requires":["Active internet connection for transcription API calls","Audio quality minimum 16kHz sample rate, mono or stereo","API credentials for transcription service (Whisper, Google Speech-to-Text, or proprietary)"],"input_types":["audio stream (WAV, MP3, AAC, Opus)","audio metadata (sample rate, channels, codec)"],"output_types":["plain text transcription with timestamps","structured transcript with speaker labels and turn boundaries","confidence scores per segment"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-limitless__cap_2","uri":"capability://text.generation.language.context.aware.meeting.and.conversation.summarization","name":"context-aware meeting and conversation summarization","description":"Generates abstractive summaries of recorded conversations using large language models with access to full transcripts, speaker metadata, and optional meeting context (calendar title, attendees, agenda). Applies prompt engineering and few-shot examples to extract key decisions, action items, and discussion topics while preserving speaker attribution and temporal structure.","intents":["Create executive summaries of long meetings for stakeholders who couldn't attend","Extract action items and owners automatically from meeting transcripts","Generate meeting notes without manual transcription review","Identify key decisions and discussion topics for knowledge retention"],"best_for":["Managers and executives attending 5+ meetings daily","Distributed teams across time zones needing async meeting summaries","Project teams tracking decisions and action items across multiple meetings"],"limitations":["Summarization quality depends on transcript accuracy — errors in transcription compound in summaries","LLM hallucination may invent action items or misattribute decisions not actually discussed","Context window limits prevent summarizing meetings >2-3 hours without chunking","No understanding of implicit context — may miss unstated decisions or sarcasm","Requires LLM API calls per meeting — adds cost and latency (5-30 seconds per summary)"],"requires":["Complete transcript from transcription capability","LLM API access (OpenAI GPT-4, Anthropic Claude, or proprietary model)","Optional: calendar metadata (meeting title, attendees, agenda)"],"input_types":["full transcript with speaker labels and timestamps","meeting metadata (title, attendees, duration)","optional: pre-meeting agenda or context"],"output_types":["plain text summary (1-3 paragraphs)","structured summary with sections (decisions, action items, discussion topics)","bullet-point format with speaker attribution"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-limitless__cap_3","uri":"capability://search.retrieval.semantic.search.across.conversation.history","name":"semantic search across conversation history","description":"Indexes transcribed conversations using vector embeddings (semantic search) and traditional full-text search, enabling users to find past discussions by meaning rather than exact keyword matching. Stores embeddings in a vector database with metadata (speaker, timestamp, meeting context) and supports hybrid search combining semantic similarity with keyword filtering for precise retrieval.","intents":["Find past discussions about a topic without remembering exact wording or meeting date","Retrieve what a specific person said about a subject across multiple meetings","Locate decisions or commitments made in past conversations","Build context for current discussions by finding related past interactions"],"best_for":["Knowledge workers with 50+ hours of recorded conversations","Sales teams finding past customer discussions and commitments","Product teams tracking feature requests and feedback across meetings"],"limitations":["Semantic search quality depends on embedding model — domain-specific terminology may not embed well","Vector database requires indexing time — new transcripts may not be searchable for 1-5 minutes","Search results ranked by semantic similarity may miss exact matches users expect","Privacy concern: embeddings may leak sensitive information if vector DB is compromised","Storage overhead — embeddings add 10-20% to transcript storage size"],"requires":["Completed transcripts with speaker metadata","Vector database (Pinecone, Weaviate, Milvus, or proprietary)","Embedding model API (OpenAI text-embedding-3, Cohere, or local model)"],"input_types":["natural language search query","optional: filters (speaker name, date range, meeting type)"],"output_types":["ranked list of transcript segments with relevance scores","context snippets (surrounding sentences for each result)","metadata (speaker, timestamp, meeting title)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-limitless__cap_4","uri":"capability://memory.knowledge.cross.app.conversation.aggregation.and.unified.timeline","name":"cross-app conversation aggregation and unified timeline","description":"Aggregates recorded conversations from multiple sources (Zoom, Teams, Slack, email, wearable) into a unified timeline indexed by timestamp and participant. Deduplicates overlapping recordings (e.g., same meeting captured from multiple devices) and correlates related conversations across platforms using participant matching and temporal proximity heuristics.","intents":["View all interactions with a specific person or team across platforms in chronological order","Find related conversations that happened across different apps (e.g., Slack discussion followed by Zoom call)","Avoid duplicate summaries when same meeting recorded from multiple sources","Build complete interaction history for a project or customer across fragmented communication channels"],"best_for":["Users with conversations spread across 3+ platforms (Zoom, Teams, Slack, email, etc.)","Sales and customer success teams managing multi-channel customer relationships","Project teams coordinating across synchronous and asynchronous communication"],"limitations":["Deduplication heuristics may fail if same meeting has different participant lists across platforms","Correlation of related conversations relies on participant overlap — may miss related discussions with different attendees","Slack and email integration requires additional OAuth permissions and API access","Temporal proximity heuristics may incorrectly link unrelated conversations happening close in time","Privacy risk: aggregating conversations across platforms creates comprehensive interaction profile"],"requires":["OAuth credentials for each integrated platform","Participant data (email addresses, display names) to match across platforms","Unified database or index supporting cross-platform queries"],"input_types":["transcripts from multiple sources with timestamps and participants","platform-specific metadata (meeting ID, channel name, thread ID)"],"output_types":["unified timeline view with mixed conversation types","deduplicated conversation records with source attribution","participant-centric conversation history"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-limitless__cap_5","uri":"capability://data.processing.analysis.automatic.action.item.extraction.and.task.assignment","name":"automatic action item extraction and task assignment","description":"Parses transcripts and summaries to identify action items, commitments, and decisions using NLP pattern matching and LLM-based extraction. Extracts task description, implied owner (speaker who committed), deadline (if mentioned), and priority, then optionally integrates with task management systems (Notion, Asana, Linear) to create actionable items without manual entry.","intents":["Automatically create tasks from meeting commitments without manual note-taking","Assign action items to the person who committed rather than meeting organizer","Track who committed to what across multiple meetings","Reduce friction between meeting and task management systems"],"best_for":["Teams using task management tools (Asana, Linear, Notion) alongside meetings","Project managers coordinating work across multiple meetings","Organizations with high meeting volume and many distributed action items"],"limitations":["Extraction accuracy depends on explicit language — implicit commitments ('we should look into X') may be missed","Owner assignment fails if speaker doesn't explicitly commit ('John should do this' vs 'I'll do this')","No understanding of task dependencies or priority — all extracted items treated equally","Requires task management system integration — not all tools supported","False positives: discussion points may be extracted as action items if phrased as commitments","Deadline extraction limited to explicit mentions — relative dates ('next week') require context"],"requires":["Complete transcript with speaker attribution","Optional: task management system API (Asana, Linear, Notion, etc.)","LLM API for extraction (or local NLP model)"],"input_types":["full transcript with speaker labels","optional: meeting summary with action items section"],"output_types":["structured action items with fields (description, owner, deadline, priority)","task creation payloads for target system (Asana, Linear, etc.)","confidence scores for each extraction"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-limitless__cap_6","uri":"capability://safety.moderation.privacy.preserving.local.and.hybrid.recording.modes","name":"privacy-preserving local and hybrid recording modes","description":"Offers on-device recording and transcription options that keep sensitive audio and transcripts local rather than sending to cloud APIs. Uses local speech-to-text models (Whisper, etc.) and optional end-to-end encryption for cloud storage, with user control over which conversations are processed locally vs. cloud-based for performance tradeoffs.","intents":["Record confidential conversations (legal, medical, financial) without sending audio to third-party APIs","Comply with data residency requirements or regulations (HIPAA, GDPR, SOC 2)","Maintain full control over sensitive conversation data","Balance privacy with performance — choose local processing for sensitive calls, cloud for speed"],"best_for":["Healthcare, legal, and financial services professionals handling regulated data","Organizations with strict data residency or privacy requirements","Users in jurisdictions with data protection regulations (GDPR, CCPA)"],"limitations":["Local transcription models (Whisper) are slower than cloud APIs — 2-5x latency increase","Local models have lower accuracy than cloud-based models, especially for accents and technical terms","On-device processing requires significant device storage and compute — impacts battery and performance","Hybrid mode requires user decision-making per conversation — adds friction","End-to-end encryption prevents server-side features (search, summarization) unless decrypted on device","Local model updates require manual download — may lag behind cloud model improvements"],"requires":["Device with sufficient storage (2-5GB for local models) and compute (modern CPU/GPU)","Optional: local speech-to-text model (Whisper, etc.)","Optional: encryption library for E2E encryption (TweetNaCl, libsodium)"],"input_types":["audio stream from recording","user preference (local vs. cloud processing)"],"output_types":["encrypted transcript stored locally or on device","plaintext transcript for local processing","encryption keys for user management"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-limitless__cap_7","uri":"capability://automation.workflow.wearable.device.integration.and.ambient.conversation.capture","name":"wearable device integration and ambient conversation capture","description":"Integrates with compatible wearable devices (smartwatches, AI pins, glasses) to capture ambient conversations and background audio without explicit app activation. Handles battery optimization through intelligent recording scheduling, audio compression, and periodic syncing to phone/cloud, with user controls for when recording is active (e.g., during work hours only).","intents":["Capture informal conversations and discussions that happen outside scheduled meetings","Record conversations without holding a phone or opening an app","Build complete conversation history including hallway discussions and informal chats","Enable hands-free recording for mobile professionals"],"best_for":["Sales professionals and consultants having frequent informal client conversations","Researchers conducting interviews in field settings","Executives and managers having ad-hoc discussions throughout the day"],"limitations":["Wearable microphone quality is poor — background noise and wind significantly degrade audio","Battery drain is severe — continuous recording drains wearable battery in 4-6 hours","Ambient recording captures unintended conversations — privacy and consent issues","Syncing to phone/cloud requires proximity and network — may miss conversations in offline areas","Limited wearable device support — only specific models compatible","Audio compression for battery efficiency reduces transcription accuracy","Legal/ethical concerns: recording ambient conversations without explicit participant consent"],"requires":["Compatible wearable device (specific models supported)","Bluetooth or WiFi connection to phone for syncing","Phone with Limitless app installed","User consent and legal compliance for recording"],"input_types":["ambient audio stream from wearable microphone","wearable device metadata (battery level, location, time)"],"output_types":["compressed audio files synced to phone","raw audio for transcription","metadata (recording duration, quality, timestamp)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-limitless__cap_8","uri":"capability://memory.knowledge.meeting.context.enrichment.with.calendar.and.crm.data","name":"meeting context enrichment with calendar and crm data","description":"Automatically enriches recorded meetings with metadata from calendar systems (meeting title, attendees, agenda) and optional CRM integration (customer name, account, deal stage). Uses this context to improve summarization, action item extraction, and search by understanding meeting purpose and participant roles without manual annotation.","intents":["Automatically tag meetings with customer or project context from CRM","Improve summarization by understanding meeting purpose from calendar agenda","Track customer interactions and commitments across multiple meetings","Build customer interaction history without manual data entry"],"best_for":["Sales and customer success teams using CRM systems (Salesforce, HubSpot, Pipedrive)","Organizations with structured meeting practices (calendar invites with agendas)","Teams managing customer relationships across multiple touchpoints"],"limitations":["Requires OAuth access to calendar and CRM systems — may not be available in restricted environments","Matching meetings to CRM records relies on email/name matching — fails with generic attendees or aliases","Calendar agenda quality varies — many meetings lack detailed agendas, limiting context","CRM integration adds latency — enrichment may not complete until 1-5 minutes after meeting","Privacy concern: linking conversations to customer records creates comprehensive customer profile","Requires manual CRM configuration — different systems have different field structures"],"requires":["OAuth credentials for calendar system (Google Calendar, Outlook, etc.)","Optional: OAuth credentials for CRM system (Salesforce, HubSpot, etc.)","Matching logic for correlating meetings to CRM records"],"input_types":["meeting transcript with attendees and timestamp","calendar event data (title, attendees, agenda, duration)","optional: CRM record data (customer name, account, deal stage)"],"output_types":["enriched meeting record with context fields","customer interaction history linked to CRM","context-aware summaries and action items"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-limitless__cap_9","uri":"capability://text.generation.language.conversation.based.knowledge.base.and.faq.generation","name":"conversation-based knowledge base and faq generation","description":"Analyzes aggregated conversation history to identify frequently discussed topics, common questions, and recurring explanations, then automatically generates knowledge base articles and FAQ entries. Uses clustering and topic modeling to group related conversations, extracts representative answers from transcripts, and creates searchable documentation without manual authoring.","intents":["Build internal knowledge base from accumulated meeting discussions and decisions","Create FAQ documentation from frequently asked questions in customer calls","Identify knowledge gaps and training needs based on repeated questions","Reduce time spent answering repetitive questions by documenting common answers"],"best_for":["Customer success and support teams handling repetitive questions","Product teams documenting feature decisions and rationale","Organizations with high meeting volume and distributed knowledge"],"limitations":["Topic clustering may group unrelated conversations if they share keywords","Extracted answers may lack clarity or completeness — require editorial review","No understanding of answer quality — may document incorrect or outdated information","Requires significant conversation volume (50+ hours) to identify meaningful patterns","Generated documentation may have inconsistent tone and structure","Privacy concern: extracting and publishing conversation content may expose confidential information"],"requires":["Aggregated conversation history with transcripts and metadata","Topic modeling or clustering algorithm (LDA, K-means, or LLM-based)","Optional: knowledge base system for publishing (Notion, Confluence, etc.)"],"input_types":["collection of transcripts with timestamps and participants","optional: topic hints or categories to focus on"],"output_types":["FAQ entries with questions and answers","knowledge base articles with topic and related conversations","topic clusters with frequency and participant information"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["iOS or Android device for mobile app","OAuth credentials for integrated platforms (Zoom, Microsoft Teams, Google Workspace)","Optional: compatible wearable device (specific models supported)","Microphone permissions enabled at OS level","Active internet connection for transcription API calls","Audio quality minimum 16kHz sample rate, mono or stereo","API credentials for transcription service (Whisper, Google Speech-to-Text, or proprietary)","Complete transcript from transcription capability","LLM API access (OpenAI GPT-4, Anthropic Claude, or proprietary model)","Optional: calendar metadata (meeting title, attendees, agenda)"],"failure_modes":["Wearable recording quality depends on device microphone and ambient noise conditions","Platform integrations require OAuth/API access — some enterprise environments restrict third-party app permissions","Audio capture may fail if application blocks recording or uses DRM-protected streams","Continuous wearable recording drains battery significantly — typically 4-6 hours per charge","Diarization accuracy degrades with >8 simultaneous speakers or heavy accents outside training data","Real-time processing adds 2-5 second latency before text appears","Overlapping speech may be attributed to wrong speaker or merged into single speaker turn","Requires continuous network connection for cloud-based transcription — offline fallback limited","Specialized terminology (medical, legal, technical jargon) may require custom language models","Summarization quality depends on transcript accuracy — errors in transcription compound in summaries","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.577Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=limitless","compare_url":"https://unfragile.ai/compare?artifact=limitless"}},"signature":"lru0VLiwjEGrKRydxbg/2QiM47ZULgzXLdjeQJ8Bt099oWMuysjS/wbPrA0gZsCa850JYr6I1Wn7o2O/q4ULCg==","signedAt":"2026-06-22T03:50:07.938Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/limitless","artifact":"https://unfragile.ai/limitless","verify":"https://unfragile.ai/api/v1/verify?slug=limitless","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"}}