{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-docket-ai","slug":"docket-ai","name":"Docket AI","type":"product","url":"https://docketai.net/","page_url":"https://unfragile.ai/docket-ai","categories":["app-builders"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-docket-ai__cap_0","uri":"capability://planning.reasoning.sales.conversation.analysis.and.coaching","name":"sales-conversation-analysis-and-coaching","description":"Analyzes real-time or recorded B2B sales conversations using speech-to-text transcription and NLP to identify conversation patterns, objection handling, and deal progression signals. The system likely uses turn-taking analysis and semantic understanding of sales methodologies (MEDDIC, SPIN selling, etc.) to provide immediate or post-call coaching feedback on sales technique effectiveness.","intents":["I need to understand what went wrong in a sales call and get specific coaching on how to handle objections better","I want real-time alerts during calls when I'm missing key discovery questions or not addressing buyer pain points","I need to track whether my team is following our sales methodology consistently across all deals"],"best_for":["B2B SaaS sales teams with complex, multi-stakeholder deals","Sales managers coaching reps on conversation quality and methodology adherence","Enterprise sales organizations with standardized sales processes"],"limitations":["Accuracy depends on audio quality and speaker clarity; background noise degrades transcription fidelity","Coaching recommendations are pattern-based and may not account for industry-specific or customer-specific nuances","Real-time analysis adds latency (likely 2-5 seconds) before feedback is available during calls"],"requires":["Audio input from sales calls (via call recording integration, dial-in, or meeting platform API)","Integration with CRM system to correlate conversation data with deal stage and customer context","Sales methodology framework defined (MEDDIC, SPIN, etc.) to calibrate coaching rules"],"input_types":["audio stream (real-time or recorded)","call transcripts (text)","CRM deal metadata (structured)"],"output_types":["coaching recommendations (text)","conversation quality scores (numeric)","objection handling analysis (structured)","methodology adherence report (structured)"],"categories":["planning-reasoning","sales-enablement"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-docket-ai__cap_1","uri":"capability://planning.reasoning.deal.stage.progression.prediction","name":"deal-stage-progression-prediction","description":"Monitors sales conversations and CRM activity to predict deal progression likelihood and identify stalled or at-risk opportunities. Uses conversation signals (buyer engagement level, question types, commitment language) combined with historical deal velocity patterns to forecast deal closure probability and recommend next steps.","intents":["I need to know which deals in my pipeline are actually progressing vs. which ones are stuck and need intervention","I want early warning signals when a deal is at risk of being lost so I can escalate or adjust strategy","I need to forecast quarterly revenue accurately by understanding true deal health, not just stage labels"],"best_for":["Sales managers managing large pipelines (50+ open deals) who need triage and prioritization","Revenue operations teams building forecasting models","Enterprise sales leaders needing predictive pipeline analytics"],"limitations":["Predictions are probabilistic and depend on sufficient historical deal data; early-stage deals have lower confidence","Conversation-based signals may not capture offline relationship building or executive sponsorship dynamics","Requires consistent CRM hygiene; inaccurate stage labels or missing activity logs degrade prediction accuracy"],"requires":["CRM system integration (Salesforce, HubSpot, etc.) with deal stage, close date, and activity history","Minimum 6-12 months of historical deal data for model training","Call recording or meeting transcript data linked to CRM opportunities"],"input_types":["CRM deal metadata (stage, close date, deal size, customer segment)","conversation transcripts or summaries (text)","sales activity history (structured: calls, emails, meetings)"],"output_types":["deal health score (numeric 0-100)","closure probability forecast (percentage)","risk indicators (categorical: at-risk, stalled, progressing)","recommended actions (text)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-docket-ai__cap_2","uri":"capability://planning.reasoning.objection.handling.recommendation.engine","name":"objection-handling-recommendation-engine","description":"Detects objections and concerns raised by buyers during sales conversations and recommends specific handling strategies based on objection type, buyer context, and historical win/loss patterns. Uses semantic classification of buyer statements to map to a taxonomy of common B2B objections (price, timing, competitor comparison, internal alignment, etc.) and retrieves relevant counterarguments or reframing techniques.","intents":["When a buyer raises a price objection, I need immediate guidance on how to reframe value or negotiate without losing margin","I want to know if a buyer's concern is a real blocker or just a standard objection I can overcome with the right response","I need to learn from my team's best objection handlers — what language and techniques work for similar deals"],"best_for":["Sales reps in complex B2B deals facing sophisticated buyer objections","Sales teams with high deal variability where objection handling experience is unevenly distributed","Organizations building institutional knowledge of effective objection responses"],"limitations":["Recommendations are template-based and may not account for unique customer relationships or political dynamics","Requires a well-curated knowledge base of objection types and responses; generic recommendations reduce effectiveness","Timing of objection detection matters — late detection (after buyer has mentally moved on) reduces recommendation utility"],"requires":["Conversation transcripts or real-time speech-to-text feed","Objection taxonomy and response library (built internally or provided by platform)","CRM context (customer industry, deal size, competitive situation) to filter recommendations"],"input_types":["conversation transcript or audio (text or audio stream)","buyer statement (text)","deal context (structured: industry, deal size, competitor)"],"output_types":["objection classification (categorical)","recommended response strategies (text with examples)","confidence score for recommendation (numeric)","historical success rate for similar objections (numeric)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-docket-ai__cap_3","uri":"capability://data.processing.analysis.buyer.engagement.and.sentiment.tracking","name":"buyer-engagement-and-sentiment-tracking","description":"Monitors buyer engagement signals and sentiment throughout sales conversations and across the deal lifecycle. Analyzes conversation tone, question frequency, response latency, and language patterns to assess buyer interest level, confidence in the solution, and emotional state. Aggregates signals over time to track engagement trends and identify disengagement early.","intents":["I need to know if the buyer is genuinely interested or just being polite — what are the real engagement signals?","I want to detect when a buyer's enthusiasm is dropping so I can re-engage before they ghost","I need to understand buyer sentiment toward our solution vs. competitors based on their language and questions"],"best_for":["Sales reps managing multiple concurrent deals who need to prioritize based on true buyer interest","Sales managers identifying which deals need intervention based on engagement decline","Deal review meetings where sentiment data informs strategy adjustments"],"limitations":["Sentiment analysis is probabilistic and can misinterpret sarcasm, cultural communication styles, or formal corporate language","Engagement signals are noisy — low response latency may indicate busy schedules rather than low interest","Requires sufficient conversation history (multiple touchpoints) to establish reliable engagement trends"],"requires":["Conversation transcripts or real-time meeting data","Email and communication metadata (response times, message frequency)","CRM activity log (meeting attendance, document views, etc.)"],"input_types":["conversation transcript (text)","email communication (text with metadata: timestamps, response times)","meeting attendance and engagement data (structured)"],"output_types":["engagement score (numeric 0-100)","sentiment classification (positive, neutral, negative, mixed)","engagement trend (increasing, stable, declining)","disengagement risk alert (boolean with confidence)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-docket-ai__cap_4","uri":"capability://planning.reasoning.next.step.and.action.recommendation","name":"next-step-and-action-recommendation","description":"Recommends specific next actions for sales reps based on deal stage, buyer engagement level, objections raised, and historical patterns of successful deal progression. Generates actionable recommendations (e.g., 'schedule executive sponsor meeting', 'send ROI analysis', 'involve legal for contract review') with timing and owner assignment suggestions.","intents":["After a call, I need to know what I should do next to move this deal forward — not generic advice, but specific actions","I want to know if I should escalate to a sales engineer, involve legal, or loop in an executive sponsor based on where the deal is","I need a prioritized action list for my pipeline so I'm not wasting time on low-impact activities"],"best_for":["Sales reps who need structured guidance on deal progression tactics","Sales managers creating activity plans for their teams","Sales organizations with defined deal progression playbooks"],"limitations":["Recommendations are based on historical patterns and may not account for unique customer politics or timing constraints","Requires well-defined deal progression playbooks; generic recommendations reduce utility","Timing recommendations are probabilistic and may not align with actual buyer readiness or internal resource availability"],"requires":["Deal stage and context (CRM)","Conversation analysis (objections, engagement, buyer signals)","Historical deal progression data (what actions preceded successful closures)"],"input_types":["deal metadata (stage, size, customer segment, structured)","conversation summary (objections, engagement signals, structured)","buyer context (decision-maker roles, timeline, structured)"],"output_types":["recommended action (text with specific details)","action priority (high, medium, low)","suggested owner (role: AE, SE, manager, etc.)","suggested timing (days until action should occur)","success probability if action is taken (numeric)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-docket-ai__cap_5","uri":"capability://memory.knowledge.competitive.intelligence.and.positioning.guidance","name":"competitive-intelligence-and-positioning-guidance","description":"Detects when competitors are mentioned in sales conversations and provides real-time positioning guidance, competitive differentiation talking points, and win/loss strategy recommendations. Analyzes buyer concerns about competitor solutions and recommends messaging to address competitive threats without being defensive.","intents":["When a buyer mentions a competitor, I need to know how to position our solution against theirs without sounding desperate","I want to understand what competitive advantages matter most to this buyer based on their questions and concerns","I need to learn from deals we've won against specific competitors — what messaging and positioning worked?"],"best_for":["Sales reps in competitive markets facing well-informed buyers","Sales teams with multiple competitors and inconsistent competitive messaging","Organizations building competitive win/loss intelligence"],"limitations":["Competitive intelligence is only as good as the underlying knowledge base; outdated competitor information reduces effectiveness","Positioning guidance may not account for customer-specific requirements or political factors","Real-time detection requires accurate competitor name recognition, which can fail with acronyms or informal references"],"requires":["Conversation transcripts with competitor mentions detected","Competitive intelligence database (features, pricing, positioning of known competitors)","Historical win/loss data correlated with competitor and positioning used"],"input_types":["conversation transcript (text)","competitor mention (text extraction)","deal context (customer segment, use case, structured)"],"output_types":["competitor identified (categorical)","positioning recommendation (text with talking points)","differentiation messaging (text)","win strategy for this competitor matchup (text)","historical win rate against this competitor (numeric)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-docket-ai__cap_6","uri":"capability://planning.reasoning.sales.methodology.adherence.monitoring","name":"sales-methodology-adherence-monitoring","description":"Tracks whether sales reps are following defined sales methodologies (MEDDIC, SPIN, Sandler, etc.) during conversations. Analyzes conversation flow to identify whether reps are asking discovery questions, qualifying opportunities, building consensus, and following the prescribed methodology steps. Provides real-time or post-call feedback on methodology adherence.","intents":["I need to ensure my team is consistently following our sales methodology, not just winging it based on intuition","I want to identify which reps are strong at discovery vs. which ones are jumping to solutions too early","I need to coach reps on methodology adherence — show them where they deviated and why it matters"],"best_for":["Sales organizations with a defined, standardized sales methodology","Sales managers coaching reps on process discipline","Enterprise sales teams where methodology consistency drives predictable outcomes"],"limitations":["Methodology adherence is a proxy for deal success, not a guarantee — some deals may close despite poor methodology execution","Requires a well-defined methodology framework with clear, observable steps; ambiguous methodologies are hard to measure","Real-time feedback may feel intrusive or distracting to reps during calls"],"requires":["Defined sales methodology framework (MEDDIC, SPIN, etc.) with clear steps and observable behaviors","Conversation transcripts or real-time speech-to-text","Training data or rules mapping methodology steps to conversation patterns"],"input_types":["conversation transcript (text)","sales methodology definition (structured rules or training data)","deal context (stage, customer type, structured)"],"output_types":["methodology adherence score (numeric 0-100)","step-by-step adherence breakdown (structured: which steps were followed, which were skipped)","coaching feedback (text with specific examples)","methodology gap analysis (text identifying deviations)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-docket-ai__cap_7","uri":"capability://data.processing.analysis.deal.summary.and.context.generation","name":"deal-summary-and-context-generation","description":"Automatically generates structured deal summaries from sales conversations, extracting key information (buyer pain points, requirements, decision criteria, timeline, stakeholders, next steps, open questions). Creates a machine-readable deal context that can be used to brief other team members, populate CRM fields, or inform downstream deal progression decisions.","intents":["After a call, I need a structured summary of what we learned without having to manually transcribe notes","I want to brief my manager or a sales engineer on the deal quickly — what are the key facts and open questions?","I need to populate CRM fields accurately with buyer requirements and decision criteria so the deal context is available to the whole team"],"best_for":["Sales teams with high call volume who need efficient note-taking and CRM hygiene","Sales organizations with distributed teams who need to share deal context asynchronously","Deal review meetings where structured deal summaries enable faster, more informed discussions"],"limitations":["Extraction accuracy depends on conversation clarity and structure; rambling or off-topic conversations produce incomplete summaries","Requires domain knowledge to distinguish signal (key buyer requirements) from noise (casual conversation)","May miss implicit requirements or unstated decision criteria that experienced reps would catch"],"requires":["Conversation transcript or real-time speech-to-text","CRM system integration to populate extracted fields","Extraction schema defining what information to extract (pain points, requirements, timeline, etc.)"],"input_types":["conversation transcript (text)","call metadata (duration, participants, structured)"],"output_types":["deal summary (text)","structured deal fields (JSON: pain points, requirements, decision criteria, timeline, stakeholders, next steps)","open questions (list of text)","confidence scores for extracted fields (numeric per field)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-docket-ai__cap_8","uri":"capability://planning.reasoning.multi.stakeholder.consensus.tracking","name":"multi-stakeholder-consensus-tracking","description":"Monitors conversations across multiple stakeholders (economic buyer, technical buyer, end user, champion, etc.) to track alignment, identify consensus gaps, and recommend strategies to build internal alignment. Analyzes different stakeholders' concerns, priorities, and stated positions to identify potential blockers to deal closure.","intents":["I need to understand which stakeholders are aligned on our solution and which ones have concerns or competing priorities","I want to know if there's a champion inside the account who can help drive consensus, or if I need to build one","I need a strategy to address the technical buyer's concerns without losing the economic buyer's support"],"best_for":["Sales reps managing complex, multi-stakeholder B2B deals","Sales managers identifying consensus gaps that could derail deals","Organizations selling to large enterprises with multiple decision-makers"],"limitations":["Stakeholder identification and role classification is probabilistic and may misclassify roles based on conversation content","Consensus tracking requires multiple conversations with different stakeholders; single-stakeholder deals provide limited signal","Internal politics and unstated priorities may not be visible in recorded conversations"],"requires":["Multiple conversation transcripts from different stakeholders","Stakeholder role classification (economic buyer, technical buyer, end user, champion, etc.)","CRM stakeholder mapping to correlate conversations with roles"],"input_types":["conversation transcripts from multiple stakeholders (text)","stakeholder metadata (role, department, structured)","deal context (use case, requirements, structured)"],"output_types":["stakeholder alignment map (structured: role, stated position, concerns, priorities)","consensus gaps (list of conflicting priorities or concerns)","champion identification (boolean + confidence score)","alignment strategy recommendations (text)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-docket-ai__cap_9","uri":"capability://data.processing.analysis.sales.rep.performance.benchmarking.and.coaching","name":"sales-rep-performance-benchmarking-and-coaching","description":"Analyzes individual sales rep performance across conversations to identify strengths, weaknesses, and coaching opportunities. Compares rep performance against team benchmarks (call duration, discovery question frequency, objection handling effectiveness, deal progression rate) and recommends targeted coaching based on performance gaps.","intents":["I need to understand which reps are strong at discovery vs. which ones need coaching on objection handling","I want to identify top performers and understand what they're doing differently so I can coach the rest of the team","I need to provide data-driven coaching feedback to reps — not just 'you need to improve', but 'here's specifically where you're underperforming vs. the team'"],"best_for":["Sales managers coaching individual reps","Sales leaders identifying training needs and high performers","Organizations building a culture of continuous improvement through data-driven coaching"],"limitations":["Performance metrics are proxies for success; high call frequency doesn't guarantee deal closure","Benchmarking requires sufficient sample size (minimum 10-20 calls per rep) for reliable comparisons","Coaching recommendations are pattern-based and may not account for rep experience level, customer segment, or deal complexity"],"requires":["Conversation transcripts from multiple reps (minimum 10-20 calls per rep for benchmarking)","Deal outcome data (closed/lost, deal size) to correlate performance metrics with results","Performance metrics definition (call duration, discovery questions, objection handling effectiveness, etc.)"],"input_types":["conversation transcripts (text)","rep metadata (tenure, experience level, structured)","deal outcome data (closed/lost, deal size, structured)"],"output_types":["rep performance scorecard (structured: discovery, objection handling, methodology adherence, etc.)","performance vs. team benchmark (numeric: percentile ranking)","coaching recommendations (text with specific examples)","strength and weakness analysis (text)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Audio input from sales calls (via call recording integration, dial-in, or meeting platform API)","Integration with CRM system to correlate conversation data with deal stage and customer context","Sales methodology framework defined (MEDDIC, SPIN, etc.) to calibrate coaching rules","CRM system integration (Salesforce, HubSpot, etc.) with deal stage, close date, and activity history","Minimum 6-12 months of historical deal data for model training","Call recording or meeting transcript data linked to CRM opportunities","Conversation transcripts or real-time speech-to-text feed","Objection taxonomy and response library (built internally or provided by platform)","CRM context (customer industry, deal size, competitive situation) to filter recommendations","Conversation transcripts or real-time meeting data"],"failure_modes":["Accuracy depends on audio quality and speaker clarity; background noise degrades transcription fidelity","Coaching recommendations are pattern-based and may not account for industry-specific or customer-specific nuances","Real-time analysis adds latency (likely 2-5 seconds) before feedback is available during calls","Predictions are probabilistic and depend on sufficient historical deal data; early-stage deals have lower confidence","Conversation-based signals may not capture offline relationship building or executive sponsorship dynamics","Requires consistent CRM hygiene; inaccurate stage labels or missing activity logs degrade prediction accuracy","Recommendations are template-based and may not account for unique customer relationships or political dynamics","Requires a well-curated knowledge base of objection types and responses; generic recommendations reduce effectiveness","Timing of objection detection matters — late detection (after buyer has mentally moved on) reduces recommendation utility","Sentiment analysis is probabilistic and can misinterpret sarcasm, cultural communication styles, or formal corporate language","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"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":"2026-06-17T09:51:03.038Z","last_scraped_at":"2026-05-03T14:00:10.321Z","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=docket-ai","compare_url":"https://unfragile.ai/compare?artifact=docket-ai"}},"signature":"YDeB2mu18EzLnPa/Rd1STvn6UWnE4kKkj0TTj7PP3c6Iq0bigl6Z7XGa/x0Dxr+CtyiUldlwBoizBxbgruJ9Aw==","signedAt":"2026-06-21T04:55:40.529Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/docket-ai","artifact":"https://unfragile.ai/docket-ai","verify":"https://unfragile.ai/api/v1/verify?slug=docket-ai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}