{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_feta","slug":"feta","name":"Feta","type":"product","url":"https://feta.io","page_url":"https://unfragile.ai/feta","categories":["automation"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_feta__cap_0","uri":"capability://data.processing.analysis.multi.platform.meeting.audio.capture.and.transcription","name":"multi-platform meeting audio capture and transcription","description":"Automatically captures audio streams from Zoom, Microsoft Teams, and Google Meet via native platform integrations or browser-based recording, then applies speech-to-text processing (likely using cloud-based ASR engines like Google Speech-to-Text or Whisper) to generate full meeting transcripts. The system handles variable audio quality and multi-speaker scenarios by normalizing input before transcription, enabling downstream processing of meeting content without manual recording setup.","intents":["I want to automatically record and transcribe meetings across all my video platforms without manually hitting record","I need accurate transcripts of meetings to search and reference later without relying on attendee notes","I want to capture meetings from platforms my team uses (Zoom, Teams, Google Meet) without switching tools"],"best_for":["remote-first teams using multiple video conferencing platforms","organizations with compliance requirements for meeting records","distributed teams where attendees join from different time zones and need async access to meeting content"],"limitations":["Transcription accuracy degrades significantly with poor audio quality, background noise, or heavy accents — no post-processing correction mechanism mentioned","Requires explicit permission/bot access to meeting platforms, which may conflict with enterprise security policies blocking third-party bots","No support for phone dial-in audio or external audio feeds, limiting coverage for hybrid meetings with non-video participants"],"requires":["Active Zoom, Microsoft Teams, or Google Meet account with meeting host or admin permissions","Feta browser extension or bot installed and authenticated to the meeting platform","Stable internet connection during meeting for real-time capture"],"input_types":["audio stream (Zoom, Teams, Google Meet native formats)","meeting metadata (participant list, timestamps, platform)"],"output_types":["full meeting transcript (text)","speaker-attributed transcript (structured JSON or plain text with speaker labels)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_feta__cap_1","uri":"capability://text.generation.language.contextual.ai.meeting.summarization.with.decision.extraction","name":"contextual ai meeting summarization with decision extraction","description":"Processes full meeting transcripts through a large language model (likely GPT-4 or similar) with a specialized prompt engineering pipeline that extracts summaries, key decisions, and action items in a single inference pass. The system likely uses few-shot prompting or fine-tuning to understand meeting context (project names, participant roles, business domain) and avoid generic verbose summaries, producing structured outputs that distinguish between decisions, action items, and discussion points.","intents":["I want a concise summary of what happened in a meeting without reading the full transcript","I need to automatically extract decisions made and action items assigned so I can track them in my workflow","I want summaries that understand my business context and avoid generic fluff"],"best_for":["teams holding 5+ meetings per week who want to reduce recap time","organizations with structured meeting formats (standups, planning sessions, client calls) where decision extraction is predictable","non-technical stakeholders who need quick meeting recaps without reading transcripts"],"limitations":["Summarization quality depends heavily on meeting structure and clarity — rambling or off-topic discussions produce poor summaries","Action item extraction accuracy varies; system may miss implicit assignments or misattribute owners if speaker names are unclear","No feedback loop or correction mechanism — users cannot easily retrain the model on their specific meeting patterns or business terminology","Latency for summary generation typically 30-120 seconds post-meeting, delaying availability of summaries for immediate distribution"],"requires":["Completed meeting transcript from the audio capture capability","Meeting metadata (participant names, meeting title/type) for context injection","API access to LLM provider (OpenAI, Anthropic, or internal model)"],"input_types":["full meeting transcript (text with speaker attribution)","meeting metadata (title, participants, duration, platform)"],"output_types":["summary (plain text, 100-300 words)","structured action items (JSON with owner, task, deadline if mentioned)","key decisions (JSON with decision, rationale, stakeholders)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_feta__cap_2","uri":"capability://tool.use.integration.cross.platform.meeting.data.export.and.integration","name":"cross-platform meeting data export and integration","description":"Provides APIs and webhook endpoints to export meeting summaries, transcripts, and action items to external tools (Slack, email, project management platforms) via standardized formats (JSON, CSV, or platform-specific APIs). The system likely implements a webhook-based push model for real-time distribution and a pull API for on-demand retrieval, with support for custom field mapping to adapt Feta's output schema to downstream tool requirements.","intents":["I want meeting summaries automatically posted to Slack channels so my team sees them without leaving the platform","I need to push action items to my project management tool (Jira, Asana, Linear) automatically","I want to export meeting data in a format my CRM or knowledge base can ingest"],"best_for":["teams with established tool ecosystems (Slack, Jira, Asana) who want to avoid manual copy-paste workflows","organizations with custom internal tools that need structured meeting data via API","compliance-heavy industries needing audit trails of meeting exports"],"limitations":["Limited integration ecosystem — only supports a small set of pre-built integrations (Slack, email, basic webhooks), requiring custom development for specialized tools","No field mapping UI for non-technical users — integrations require API key configuration and manual schema alignment","Webhook delivery is not guaranteed (no retry logic or dead-letter queue mentioned) — failed deliveries may silently drop data","Export latency depends on downstream tool API performance; slow integrations can delay summary availability"],"requires":["API key or OAuth token for destination platform (Slack, Jira, etc.)","Feta API key for authentication","Webhook endpoint or API endpoint on destination system that accepts POST requests"],"input_types":["meeting summary (structured JSON from summarization capability)","action items (structured JSON with owner, task, deadline)","full transcript (text)"],"output_types":["Slack message (formatted text with thread support)","email (HTML or plain text)","JSON payload (for custom webhooks)","CSV export (for spreadsheet import)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_feta__cap_3","uri":"capability://data.processing.analysis.speaker.identification.and.role.based.attribution","name":"speaker identification and role-based attribution","description":"Automatically identifies and labels speakers in meeting transcripts using a combination of audio fingerprinting (voice biometrics) and meeting metadata (participant list from platform APIs). The system likely maintains a speaker profile database keyed by voice characteristics and meeting context, enabling consistent speaker attribution across multiple meetings and reducing manual speaker labeling overhead. Role inference (e.g., 'client', 'team member', 'manager') may be derived from meeting metadata or historical patterns.","intents":["I want transcripts with speaker names automatically labeled so I don't have to manually edit them","I need to track who said what across multiple meetings to understand participation patterns","I want to filter action items by speaker role (e.g., show only items assigned to external clients)"],"best_for":["teams with recurring participants across multiple meetings","organizations with large meetings (10+ participants) where manual speaker labeling is impractical","compliance-heavy industries needing clear attribution of statements to individuals"],"limitations":["Speaker identification accuracy degrades with similar voices, heavy accents, or participants joining/leaving mid-meeting","Requires participant list from meeting platform API — works poorly for meetings with dial-in participants or external attendees not in the system","No mechanism to correct misidentified speakers in the UI — users must manually edit transcripts or retrain the model","Privacy concern: voice fingerprinting requires storing voice embeddings, which may violate data residency or GDPR requirements"],"requires":["Meeting participant metadata from platform API (Zoom, Teams, Google Meet)","Audio stream with sufficient quality for voice biometric analysis (SNR > 10dB recommended)","Speaker profile database (built incrementally from previous meetings)"],"input_types":["audio stream with multiple speakers","participant list (names, email addresses from meeting platform)"],"output_types":["speaker-attributed transcript (text with [Speaker Name]: prefix)","speaker statistics (JSON with speaker count, talk time per speaker, turn-taking patterns)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_feta__cap_4","uri":"capability://search.retrieval.meeting.search.and.semantic.retrieval.across.transcript.library","name":"meeting search and semantic retrieval across transcript library","description":"Indexes all meeting transcripts and summaries using vector embeddings (likely OpenAI embeddings or similar) to enable semantic search across the meeting library. Users can query with natural language (e.g., 'What did we decide about pricing?') and the system returns relevant meeting segments ranked by semantic similarity, rather than keyword matching. The system likely maintains a vector database (Pinecone, Weaviate, or similar) indexed by meeting date, participant, and topic for efficient retrieval.","intents":["I want to search across all my past meetings to find when we discussed a specific topic","I need to find action items related to a specific project or person across multiple meetings","I want to retrieve context from previous meetings without remembering exact keywords or dates"],"best_for":["teams with 50+ meetings per month who need to reference historical decisions","organizations with long sales cycles or complex projects requiring historical context","knowledge workers who need to avoid duplicating discussions across meetings"],"limitations":["Search latency depends on vector database size — queries may take 2-5 seconds for large meeting libraries (1000+ meetings)","Semantic search can return false positives if meetings discuss similar topics in different contexts (e.g., 'pricing' in sales vs. 'pricing' in product discussions)","No full-text search fallback — users cannot search for exact phrases or specific speaker quotes","Vector embeddings are language-specific; multilingual meetings may produce poor search results"],"requires":["Vector embedding API (OpenAI, Cohere, or local model)","Vector database (Pinecone, Weaviate, Milvus, or similar) with sufficient capacity for meeting library","Meeting transcripts and summaries indexed and embedded (typically done asynchronously post-meeting)"],"input_types":["natural language search query (text)","optional filters (date range, participants, meeting type)"],"output_types":["ranked list of relevant meeting segments (JSON with meeting metadata, transcript excerpt, relevance score)","summary of relevant meetings (text)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_feta__cap_5","uri":"capability://data.processing.analysis.meeting.insights.and.analytics.dashboard","name":"meeting insights and analytics dashboard","description":"Aggregates meeting data (duration, participant count, talk time distribution, action item completion rate) into a dashboard that provides team-level and individual-level insights. The system likely computes metrics asynchronously (daily or weekly aggregation jobs) and caches results in a time-series database for fast dashboard rendering. Insights may include trends (e.g., 'meeting duration increasing over time') and anomalies (e.g., 'participant X rarely speaks in meetings').","intents":["I want to understand how much time my team spends in meetings and whether it's increasing","I need to see which action items are overdue or not being completed","I want to identify meeting participation patterns to ensure all team members are engaged"],"best_for":["managers and team leads tracking team productivity and meeting efficiency","organizations optimizing meeting culture and reducing meeting load","teams with recurring meetings (standups, planning sessions) where trends are meaningful"],"limitations":["Insights are aggregated and anonymized — no drill-down to individual meeting details from dashboard","Action item completion tracking requires manual updates or integration with external project management tools; no automatic completion detection","Metrics are computed on a fixed schedule (daily/weekly) — real-time insights are not available","No predictive analytics or recommendations for reducing meeting load"],"requires":["Minimum 10-20 meetings captured in Feta to produce meaningful trends","Time-series database or analytics backend for metric aggregation","Dashboard UI with charting library (likely Chart.js, D3.js, or similar)"],"input_types":["meeting metadata (duration, participants, date, platform)","transcript and summary data (for topic analysis)","action item data (owner, status, deadline)"],"output_types":["dashboard visualizations (charts, tables, KPI cards)","CSV export of metrics (for external analysis)","email reports (weekly or monthly summaries)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_feta__cap_6","uri":"capability://automation.workflow.freemium.access.with.usage.based.tier.progression","name":"freemium access with usage-based tier progression","description":"Implements a freemium model where users can capture and summarize a limited number of meetings per month (likely 5-10) without payment, with automatic tier upgrades triggered by usage thresholds. The system tracks usage metrics (meetings captured, API calls, storage) and presents upgrade prompts when users approach limits, enabling low-friction onboarding and conversion to paid tiers. Pricing tiers likely correspond to meeting volume (e.g., 'Starter: 10 meetings/month', 'Pro: 50 meetings/month').","intents":["I want to try Feta without providing a credit card to see if it works for my team","I want to understand pricing before committing to a paid plan","I want to start with a free tier and upgrade only when I need more capacity"],"best_for":["small teams and solo users evaluating meeting productivity tools","organizations with variable meeting volume who don't want to commit to fixed pricing","non-technical founders and managers who want to avoid upfront costs"],"limitations":["Free tier is limited to 5-10 meetings/month — not sufficient for teams with daily meetings","Pricing transparency is poor (per editorial summary) — specific tier features and costs require signing up or contacting sales","No annual billing discount mentioned — monthly pricing may be higher than competitors","Free tier may have feature restrictions (e.g., no API access, limited export options) that are not clearly documented"],"requires":["Email address for account creation","No credit card required for free tier"],"input_types":["user signup data (email, company name, team size)"],"output_types":["free tier access (limited meeting captures)","upgrade prompts (when usage limits approached)","pricing page (with tier options)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Active Zoom, Microsoft Teams, or Google Meet account with meeting host or admin permissions","Feta browser extension or bot installed and authenticated to the meeting platform","Stable internet connection during meeting for real-time capture","Completed meeting transcript from the audio capture capability","Meeting metadata (participant names, meeting title/type) for context injection","API access to LLM provider (OpenAI, Anthropic, or internal model)","API key or OAuth token for destination platform (Slack, Jira, etc.)","Feta API key for authentication","Webhook endpoint or API endpoint on destination system that accepts POST requests","Meeting participant metadata from platform API (Zoom, Teams, Google Meet)"],"failure_modes":["Transcription accuracy degrades significantly with poor audio quality, background noise, or heavy accents — no post-processing correction mechanism mentioned","Requires explicit permission/bot access to meeting platforms, which may conflict with enterprise security policies blocking third-party bots","No support for phone dial-in audio or external audio feeds, limiting coverage for hybrid meetings with non-video participants","Summarization quality depends heavily on meeting structure and clarity — rambling or off-topic discussions produce poor summaries","Action item extraction accuracy varies; system may miss implicit assignments or misattribute owners if speaker names are unclear","No feedback loop or correction mechanism — users cannot easily retrain the model on their specific meeting patterns or business terminology","Latency for summary generation typically 30-120 seconds post-meeting, delaying availability of summaries for immediate distribution","Limited integration ecosystem — only supports a small set of pre-built integrations (Slack, email, basic webhooks), requiring custom development for specialized tools","No field mapping UI for non-technical users — integrations require API key configuration and manual schema alignment","Webhook delivery is not guaranteed (no retry logic or dead-letter queue mentioned) — failed deliveries may silently drop data","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.892Z","last_scraped_at":"2026-04-05T13:23:42.552Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=feta","compare_url":"https://unfragile.ai/compare?artifact=feta"}},"signature":"/UpB5cKR2KBo5mB9iDtbWrrGex7tpbYYL76GruZOf2CCJ33i9Ind6pRXpRwHwcwLqAd3cnesznY3Y6+BFcbHAQ==","signedAt":"2026-06-20T23:43:32.160Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/feta","artifact":"https://unfragile.ai/feta","verify":"https://unfragile.ai/api/v1/verify?slug=feta","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"}}