{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_toqan","slug":"toqan","name":"Toqan","type":"product","url":"https://toqan.ai","page_url":"https://unfragile.ai/toqan","categories":["app-builders"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_toqan__cap_0","uri":"capability://data.processing.analysis.meeting.transcription.and.automatic.action.item.extraction","name":"meeting transcription and automatic action item extraction","description":"Toqan ingests meeting audio/video streams or transcripts from integrated communication platforms (Zoom, Teams, Google Meet) and applies NLP-based semantic analysis to identify decisions, action items, owners, and deadlines. The system likely uses intent recognition and entity extraction models to parse conversational context and surface structured outputs without manual note-taking. This operates as a post-meeting or real-time processing pipeline that converts unstructured dialogue into actionable task artifacts.","intents":["I want to automatically capture action items from meetings without manual note-taking","I need to identify who owns which decisions and by when across all team meetings","I want to ensure no follow-up tasks slip through the cracks after meetings end"],"best_for":["Mid-to-large distributed teams with frequent video meetings","Organizations with high meeting volume and poor follow-through on action items","Teams using Zoom, Microsoft Teams, or Google Meet as primary meeting platforms"],"limitations":["Accuracy degrades with poor audio quality, heavy accents, or technical jargon outside training data","Real-time processing may introduce 30-60 second latency before action items appear","Requires explicit meeting recording/transcription permissions — cannot retroactively process unrecorded meetings","No context from pre-meeting agendas or post-meeting documents to disambiguate action item priority"],"requires":["Active integration with Zoom, Microsoft Teams, Google Meet, or Slack","Meeting recording enabled or real-time audio stream access","API credentials for target communication platform"],"input_types":["audio stream (real-time)","video stream (real-time)","transcript text (post-meeting)"],"output_types":["structured JSON (action items with owner, deadline, description)","task cards (integrated into project management tools)","notifications (to assigned owners)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toqan__cap_1","uri":"capability://data.processing.analysis.real.time.collaboration.bottleneck.detection.and.visualization","name":"real-time collaboration bottleneck detection and visualization","description":"Toqan analyzes communication patterns across integrated platforms (Slack, Teams, email, calendar) to identify workflow friction points: response time delays, communication silos between teams, over-reliance on specific individuals, meeting load imbalances, and decision-making delays. The system likely maintains a temporal graph of interactions and applies statistical anomaly detection or clustering algorithms to surface patterns that deviate from team baselines. Visualizations present these insights as dashboards showing communication flow, response latencies, and team connectivity metrics.","intents":["I want to identify which teams or individuals are communication bottlenecks slowing down decisions","I need to see if certain team members are overloaded with cross-functional requests","I want to detect when communication silos are forming between departments"],"best_for":["Engineering and product teams managing complex cross-functional workflows","Distributed teams with asynchronous communication challenges","Organizations with 50+ employees where communication patterns become opaque"],"limitations":["Requires historical data (2-4 weeks minimum) to establish baseline patterns — new teams see limited insights initially","Cannot distinguish between intentional communication patterns (e.g., scheduled focus time) and actual bottlenecks without additional context","Privacy concerns: analyzing all team communication requires broad data access and clear consent policies","Metrics may be gamed or misinterpreted (e.g., fast response time ≠ good decision quality)"],"requires":["Integration with Slack, Microsoft Teams, or email system","Calendar access (Google Calendar, Outlook) for meeting load analysis","Minimum 2-4 weeks of historical communication data","Admin-level permissions to access cross-team communication patterns"],"input_types":["message metadata (timestamps, sender, recipient, channel)","calendar events (meeting duration, attendees, frequency)","email headers (send/receive times, thread participants)"],"output_types":["dashboard visualizations (communication graphs, heatmaps)","metrics (response time percentiles, message volume by team)","alerts (bottleneck detection, anomalies)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toqan__cap_10","uri":"capability://data.processing.analysis.cross.functional.collaboration.pattern.analysis","name":"cross-functional collaboration pattern analysis","description":"Toqan analyzes communication patterns between teams (engineering, product, design, sales) to identify collaboration strength, friction points, and knowledge silos. The system likely builds a collaboration graph showing which teams communicate frequently, which teams rarely interact, and where communication breaks down. It may identify missing connections (teams that should collaborate but don't) or over-reliance on specific individuals as bridges between teams. This enables organizations to optimize team structure and communication flows.","intents":["I want to understand how well different teams collaborate and where friction exists","I need to identify knowledge silos or teams that should collaborate but don't","I want to optimize team structure based on actual communication patterns"],"best_for":["Large organizations (100+ employees) with multiple teams","Organizations undergoing restructuring or scaling","Teams with complex dependencies (engineering, product, design, sales)"],"limitations":["Collaboration patterns may reflect organizational structure rather than actual need — teams may not communicate because they're siloed, not because they shouldn't","Requires cross-team communication data — privacy concerns if analyzing sensitive discussions","Patterns may be misinterpreted (low communication ≠ poor collaboration; may indicate clear ownership)","Recommendations to increase collaboration may conflict with focus time or deep work requirements"],"requires":["Integration with Slack, Teams, email, or calendar system","Cross-team communication data (requires admin-level access)","Team membership information (to map individuals to teams)"],"input_types":["message metadata (sender team, recipient team, frequency)","meeting attendance (which teams attend which meetings)","collaboration graph (who works with whom, frequency)"],"output_types":["collaboration graphs (visualizing team interactions)","friction point analysis (where collaboration breaks down)","missing connection identification (teams that should collaborate but don't)","recommendations (team structure optimizations)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toqan__cap_2","uri":"capability://planning.reasoning.intelligent.meeting.scheduling.and.conflict.resolution","name":"intelligent meeting scheduling and conflict resolution","description":"Toqan integrates with calendar systems (Google Calendar, Outlook) and analyzes team availability, meeting load, timezone constraints, and participant preferences to suggest optimal meeting times or automatically reschedule conflicting meetings. The system likely uses constraint satisfaction algorithms to balance multiple objectives: minimizing timezone burden, respecting focus time blocks, reducing back-to-back meetings, and accommodating participant preferences. It may also predict meeting necessity based on attendee patterns and suggest async alternatives when appropriate.","intents":["I want to find meeting times that work across timezones without exhausting distributed team members","I need to prevent calendar fragmentation where team members have no uninterrupted focus time","I want to automatically reschedule low-priority meetings when conflicts arise with high-priority work"],"best_for":["Distributed teams spanning 3+ timezones","Organizations with high meeting load (10+ meetings/week per person)","Teams with explicit focus time blocks or deep work requirements"],"limitations":["Requires explicit calendar access and permission to view/modify events — privacy-sensitive in some organizations","Cannot account for implicit constraints (e.g., 'I prefer not to meet after 5pm' if not marked on calendar)","Timezone optimization may still result in suboptimal times for some participants — no perfect solution exists","Rescheduling decisions may conflict with organizational norms or unwritten rules about meeting importance"],"requires":["Google Calendar or Microsoft Outlook integration with read/write permissions","Calendar data for all meeting participants (requires admin setup or individual opt-in)","Timezone information for each team member"],"input_types":["calendar events (time, duration, attendees, recurrence)","availability blocks (focus time, PTO, preferred meeting windows)","team preferences (timezone burden tolerance, meeting load limits)"],"output_types":["meeting time suggestions (ranked by optimization score)","rescheduling recommendations (with rationale)","calendar updates (if auto-rescheduling enabled)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toqan__cap_3","uri":"capability://memory.knowledge.conversation.context.summarization.and.knowledge.indexing","name":"conversation context summarization and knowledge indexing","description":"Toqan processes ongoing conversations across Slack channels, Teams threads, and email chains to generate concise summaries of discussions, decisions, and context. The system likely maintains a vector embedding index of conversation content, enabling semantic search across historical discussions. When new team members join or context is needed, users can query the index to retrieve relevant past conversations without manual scrolling. This operates as a knowledge layer that makes implicit team knowledge explicit and searchable.","intents":["I want to quickly understand the context and history of a decision without reading 50 messages","I need to find past discussions about a specific topic or decision to avoid re-litigating it","I want new team members to onboard faster by accessing summarized context of key decisions"],"best_for":["Teams with high-volume asynchronous communication (Slack-heavy workflows)","Organizations with frequent context switching across projects","Teams with high turnover or frequent new hires"],"limitations":["Summarization quality depends on conversation structure — rambling or off-topic threads may produce inaccurate summaries","Vector embeddings may miss nuanced context or sarcasm — summaries can oversimplify complex decisions","Requires indexing all historical conversations — privacy concerns if sensitive information is included","Search results depend on query quality — users must know what to search for to find relevant context"],"requires":["Slack, Microsoft Teams, or email integration with message history access","Sufficient storage for embedding vectors (scales with conversation volume)","Admin permissions to index cross-channel or cross-team conversations"],"input_types":["message text (from Slack, Teams, email)","message metadata (timestamps, participants, channel/thread)","search queries (natural language)"],"output_types":["conversation summaries (bullet-point or narrative format)","search results (ranked by relevance)","context cards (embedded in chat or dashboard)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toqan__cap_4","uri":"capability://data.processing.analysis.team.communication.health.scoring.and.trend.analysis","name":"team communication health scoring and trend analysis","description":"Toqan calculates quantitative metrics on team communication patterns: response time distributions, message sentiment trends, collaboration frequency between teams, decision velocity, and communication diversity (e.g., percentage of decisions made asynchronously vs. in meetings). The system likely applies time-series analysis to detect trends (e.g., increasing response times, declining cross-team collaboration) and generates alerts when metrics deviate from historical baselines. Scores are aggregated at team and organization levels to provide health snapshots.","intents":["I want a quantitative measure of how well my team is collaborating and communicating","I need to detect when team communication health is degrading (e.g., response times increasing, silos forming)","I want to benchmark my team's communication patterns against historical baselines or industry standards"],"best_for":["Engineering and product leaders managing team health metrics","Organizations with explicit communication or collaboration OKRs","Teams undergoing restructuring or process changes (need baseline metrics)"],"limitations":["Metrics are correlative, not causal — high response time may indicate focus time, not poor collaboration","Sentiment analysis on technical discussions (code reviews, debugging) often produces false positives","Baseline comparisons require sufficient historical data (4+ weeks) — new teams cannot be scored initially","Metrics can be gamed or misinterpreted by managers (e.g., penalizing focus time as 'low collaboration')"],"requires":["Integration with Slack, Microsoft Teams, or email system","Historical communication data (minimum 4 weeks for meaningful baselines)","Admin-level access to cross-team communication patterns"],"input_types":["message metadata (timestamps, sender, recipient, sentiment)","response time data (derived from message timestamps)","collaboration graph (who communicates with whom, frequency)"],"output_types":["health scores (0-100 scale, by team or organization)","trend charts (response time, sentiment, collaboration over time)","alerts (when metrics deviate from baseline)","benchmark reports (vs. historical or peer teams)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toqan__cap_5","uri":"capability://memory.knowledge.cross.platform.conversation.threading.and.context.preservation","name":"cross-platform conversation threading and context preservation","description":"Toqan creates unified conversation threads that span multiple platforms (e.g., a decision initiated in Slack, continued in Teams, and documented in email). The system likely maintains a conversation graph that links related messages across platforms using content similarity, participant overlap, and temporal proximity. Users can view a single unified thread rather than jumping between platforms, and context is preserved as conversations migrate. This operates as a conversation continuity layer that abstracts away platform fragmentation.","intents":["I want to follow a conversation that spans Slack, Teams, and email without switching apps","I need to see the full context of a decision even if it was discussed across multiple platforms","I want to ensure important conversations don't get lost when they move between platforms"],"best_for":["Organizations using multiple communication platforms simultaneously (Slack + Teams + email)","Teams with hybrid workflows (some members prefer Slack, others Teams)","Organizations with formal decision documentation requirements (decisions must be captured across platforms)"],"limitations":["Linking conversations across platforms is heuristic-based — false positives (unrelated conversations linked) or false negatives (related conversations missed) are possible","Unified view may obscure platform-specific context (e.g., Slack thread vs. Teams channel distinction)","Requires read access to all platforms — privacy and permission management becomes complex","Platform-specific features (reactions, threading, pinning) may not translate to unified view"],"requires":["Integration with Slack, Microsoft Teams, and email system","Read access to messages across all platforms","Sufficient compute for real-time conversation linking and deduplication"],"input_types":["messages from Slack, Teams, email","message metadata (timestamps, participants, content)","user-provided conversation links (manual linking hints)"],"output_types":["unified conversation threads (merged across platforms)","context cards (showing platform origin and participants)","conversation graphs (visualizing cross-platform discussion flow)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toqan__cap_6","uri":"capability://planning.reasoning.intelligent.async.first.communication.recommendations","name":"intelligent async-first communication recommendations","description":"Toqan analyzes meeting requests, chat messages, and calendar patterns to recommend when communication should be asynchronous (recorded video, written summary, async thread) versus synchronous (real-time meeting). The system likely uses decision tree or heuristic rules based on: urgency (can it wait 24 hours?), complexity (does it need real-time discussion?), timezone burden (how many timezones affected?), and participant availability. When a synchronous meeting is proposed, the system may suggest an async alternative with rationale, helping teams reduce meeting load.","intents":["I want to reduce unnecessary meetings by identifying which discussions can be async","I need to know when a topic requires real-time discussion vs. when async is sufficient","I want to protect team focus time by pushing low-urgency discussions to async channels"],"best_for":["Distributed teams with high meeting load (10+ meetings/week per person)","Organizations with explicit async-first or deep-work cultures","Teams struggling with meeting fatigue or timezone burden"],"limitations":["Recommendations are heuristic-based — may not account for organizational culture or unwritten norms","Cannot assess discussion complexity without understanding domain context — may recommend async for topics requiring real-time debate","Timezone burden optimization may still result in suboptimal times for some participants","Users may ignore recommendations if they conflict with established meeting habits"],"requires":["Integration with calendar system (Google Calendar, Outlook)","Integration with Slack, Teams, or email for message analysis","Timezone information for team members"],"input_types":["meeting requests (title, description, attendees, proposed time)","message content (to assess urgency and complexity)","calendar data (to assess participant availability and timezone burden)"],"output_types":["async/sync recommendations (with rationale)","suggested async format (recorded video, written summary, thread)","alternative meeting times (if sync is necessary)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toqan__cap_7","uri":"capability://data.processing.analysis.decision.capture.and.audit.trail.generation","name":"decision capture and audit trail generation","description":"Toqan automatically identifies and captures key decisions from conversations (meetings, Slack threads, email chains) and creates structured decision records with: decision statement, rationale, alternatives considered, decision maker, date, and affected stakeholders. The system likely uses NLP to detect decision language patterns ('we decided', 'let's go with', 'approved') and creates an audit trail showing how decisions evolved. These records are indexed and searchable, enabling teams to understand why past decisions were made and avoid re-litigating them.","intents":["I want to capture decisions automatically without manual documentation","I need to understand why a past decision was made and what alternatives were considered","I want to create an audit trail showing how decisions evolved over time"],"best_for":["Organizations with formal governance or compliance requirements","Teams making high-stakes decisions (architecture, product direction, hiring)","Organizations with high turnover where decision context is frequently lost"],"limitations":["NLP-based decision detection produces false positives (casual 'let's try this' misidentified as formal decision) and false negatives (implicit decisions missed)","Structured decision records require manual curation — automated capture alone is insufficient for audit purposes","Cannot capture decisions made outside recorded channels (hallway conversations, 1-on-1s)","Decision rationale extraction is lossy — complex reasoning may be oversimplified in structured format"],"requires":["Integration with Slack, Teams, email, or meeting transcription system","NLP models trained on decision language patterns","Structured storage for decision records (database or knowledge base)"],"input_types":["conversation text (from meetings, Slack, email)","decision language patterns (keywords indicating decisions)","metadata (decision maker, date, affected teams)"],"output_types":["decision records (structured JSON with decision, rationale, alternatives, date)","audit trails (showing decision evolution over time)","decision search results (queryable by topic, decision maker, date)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toqan__cap_8","uri":"capability://data.processing.analysis.team.sentiment.and.engagement.trend.monitoring","name":"team sentiment and engagement trend monitoring","description":"Toqan applies sentiment analysis to team communications (Slack messages, email, meeting transcripts) to detect trends in team morale, engagement, and stress levels. The system likely uses NLP sentiment classifiers and tracks changes over time, generating alerts when sentiment degrades significantly. It may correlate sentiment trends with events (product launches, layoffs, deadline crunches) to identify root causes. Sentiment is aggregated at team and individual levels, enabling managers to identify struggling team members or teams.","intents":["I want to detect when team morale is declining before it becomes a retention problem","I need to identify which team members are stressed or disengaged based on communication patterns","I want to correlate sentiment trends with organizational events to understand impact"],"best_for":["People managers and HR teams monitoring team health","Organizations with high-pressure environments (startups, deadline-driven teams)","Teams with remote/distributed members where sentiment is harder to assess"],"limitations":["Sentiment analysis on technical discussions (code reviews, debugging) produces high false positive rates","Sentiment scores are correlative, not causal — declining sentiment may indicate external factors (personal issues) unrelated to work","Privacy concerns: analyzing sentiment of individual messages may feel invasive and damage trust","Sentiment trends may be misinterpreted by managers (e.g., low sentiment = disengagement, when it may indicate focus on difficult problems)"],"requires":["Integration with Slack, Teams, email, or meeting transcription","Sentiment analysis models (pre-trained or custom-trained on company data)","Historical communication data (4+ weeks for meaningful trend analysis)"],"input_types":["message text (from Slack, Teams, email)","message metadata (timestamps, sender, context)","organizational events (product launches, layoffs, deadline changes)"],"output_types":["sentiment scores (0-100 scale, by team or individual)","trend charts (sentiment over time)","alerts (when sentiment deviates from baseline)","correlation analysis (sentiment vs. organizational events)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toqan__cap_9","uri":"capability://text.generation.language.automated.meeting.preparation.and.pre.read.generation","name":"automated meeting preparation and pre-read generation","description":"Toqan generates meeting preparation materials automatically: summaries of relevant past discussions, key decisions that context the meeting, participant backgrounds, and suggested agenda items based on conversation history. The system likely analyzes recent conversations involving meeting participants, extracts relevant context, and synthesizes it into a pre-read document. This reduces meeting prep time and ensures participants arrive with shared context, enabling more productive discussions.","intents":["I want to prepare for meetings faster by automatically generating pre-read materials","I need to ensure all meeting participants have shared context before the meeting starts","I want to reduce time spent on context-setting during meetings"],"best_for":["Teams with frequent recurring meetings (standups, syncs, reviews)","Organizations with high-context discussions (architecture reviews, product strategy)","Teams with distributed members in different timezones (async pre-read reduces meeting time)"],"limitations":["Pre-read generation depends on conversation history — new topics or projects with limited history produce shallow pre-reads","Automatically generated pre-reads may miss important context or include irrelevant information","Participants may not read pre-reads, reducing effectiveness","Requires identifying relevant past conversations — heuristics may miss important context or include noise"],"requires":["Integration with Slack, Teams, email, or meeting system","Calendar access to identify meeting participants and timing","Historical conversation data relevant to meeting topics"],"input_types":["meeting details (title, participants, time, description)","historical conversations (Slack threads, email chains, past meeting notes)","participant information (roles, recent activity)"],"output_types":["pre-read documents (markdown or PDF format)","context summaries (key decisions, recent discussions)","suggested agenda items (derived from conversation history)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Active integration with Zoom, Microsoft Teams, Google Meet, or Slack","Meeting recording enabled or real-time audio stream access","API credentials for target communication platform","Integration with Slack, Microsoft Teams, or email system","Calendar access (Google Calendar, Outlook) for meeting load analysis","Minimum 2-4 weeks of historical communication data","Admin-level permissions to access cross-team communication patterns","Integration with Slack, Teams, email, or calendar system","Cross-team communication data (requires admin-level access)","Team membership information (to map individuals to teams)"],"failure_modes":["Accuracy degrades with poor audio quality, heavy accents, or technical jargon outside training data","Real-time processing may introduce 30-60 second latency before action items appear","Requires explicit meeting recording/transcription permissions — cannot retroactively process unrecorded meetings","No context from pre-meeting agendas or post-meeting documents to disambiguate action item priority","Requires historical data (2-4 weeks minimum) to establish baseline patterns — new teams see limited insights initially","Cannot distinguish between intentional communication patterns (e.g., scheduled focus time) and actual bottlenecks without additional context","Privacy concerns: analyzing all team communication requires broad data access and clear consent policies","Metrics may be gamed or misinterpreted (e.g., fast response time ≠ good decision quality)","Collaboration patterns may reflect organizational structure rather than actual need — teams may not communicate because they're siloed, not because they shouldn't","Requires cross-team communication data — privacy concerns if analyzing sensitive discussions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2833333333333333,"quality":0.6799999999999999,"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":"2026-05-24T12:16:33.648Z","last_scraped_at":"2026-04-05T13:23:42.562Z","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=toqan","compare_url":"https://unfragile.ai/compare?artifact=toqan"}},"signature":"dVRJ355/TfTHwGi4Hs5WHIxiiU1xvPdvxReyf4ngMs/cViKqDzvo4xlJ71xEifcO8vTRpI/GZNP9wb4nVhQrDQ==","signedAt":"2026-06-21T02:31:03.957Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/toqan","artifact":"https://unfragile.ai/toqan","verify":"https://unfragile.ai/api/v1/verify?slug=toqan","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"}}