{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_crono","slug":"crono","name":"Crono","type":"product","url":"https://www.crono.one","page_url":"https://unfragile.ai/crono","categories":["app-builders"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_crono__cap_0","uri":"capability://automation.workflow.crm.integrated.sales.activity.automation.with.ai.driven.task.scheduling","name":"crm-integrated sales activity automation with ai-driven task scheduling","description":"Automatically captures, categorizes, and schedules follow-up tasks from customer interactions by parsing email, call, and meeting data extracted from connected CRM systems (Salesforce, HubSpot, etc.). Uses NLP to identify action items and deal signals, then creates calendar events and CRM tasks without manual rep intervention. Integrates bidirectionally with CRM APIs to read customer context and write back activity logs, reducing manual data entry overhead.","intents":["I want to eliminate manual follow-up scheduling and task creation after every customer call or email","I need the system to automatically log activities back to our CRM so reps don't duplicate work","I want AI to identify which deals need immediate follow-up based on conversation sentiment and intent signals"],"best_for":["Mid-market B2B sales teams with 20-500 reps","Organizations with established CRM hygiene and consistent data entry practices","Sales leaders looking to reduce administrative overhead without replacing reps"],"limitations":["Effectiveness degrades significantly if CRM data is incomplete or inconsistently formatted — garbage in, garbage out","Requires native API connectors for each CRM; unsupported systems fall back to manual integration or webhooks with higher latency","Cannot distinguish between genuine deal signals and false positives without training on org-specific sales playbooks — initial accuracy may be 60-70%"],"requires":["Active CRM subscription (Salesforce, HubSpot, Pipedrive, or similar)","OAuth 2.0 API access credentials for CRM","Email/calendar integration (Gmail, Outlook, or Exchange)","Minimum 3-6 months of historical CRM data for pattern learning"],"input_types":["email body and metadata","call transcripts or summaries","meeting notes","CRM contact and opportunity records"],"output_types":["structured task objects","calendar events","CRM activity logs","deal stage recommendations"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_crono__cap_1","uri":"capability://data.processing.analysis.real.time.conversation.intelligence.with.deal.signal.detection","name":"real-time conversation intelligence with deal signal detection","description":"Analyzes live or recorded customer conversations (calls, emails, meetings) using NLP and intent classification to surface deal signals, objection patterns, and buyer sentiment in real-time or near-real-time. Extracts key phrases, buying signals (e.g., 'budget approved', 'timeline is Q2'), and competitive mentions, then surfaces these via dashboard or Slack notifications. Uses transformer-based models fine-tuned on B2B sales language to identify patterns humans typically miss during fast-paced conversations.","intents":["I want to know immediately when a customer mentions budget, timeline, or decision-maker info during a call","I need to identify which conversations contain objections so I can coach reps on handling them","I want to spot competitive threats mentioned in customer conversations without manually reviewing every call"],"best_for":["Sales teams with 50+ reps where manual conversation review is impractical","Organizations selling complex B2B solutions with long sales cycles (3-12 months)","Sales leaders who want to identify coaching opportunities and rep performance gaps"],"limitations":["Accuracy on deal signal detection varies by industry vertical — generic models may miss domain-specific terminology (e.g., 'procurement cycle' vs 'buying process')","Requires call recording or meeting transcription; email-only teams get limited signal extraction","Real-time analysis adds 2-5 second latency to live call processing; batch analysis of recorded calls is faster but delayed","Cannot distinguish between exploratory conversations and serious deal progression without historical context"],"requires":["Call recording integration (Gong, Chorus, or native Zoom/Teams recording)","Transcription service (Otter, Rev, or built-in speech-to-text)","CRM connection to map conversations to opportunities","Minimum 100 recorded conversations for model calibration"],"input_types":["audio files (MP3, WAV, M4A)","call transcripts (text)","meeting recordings (MP4, WebM)","email body text"],"output_types":["deal signal JSON (budget, timeline, decision-maker, competitor)","sentiment scores (0-1 scale)","objection categories and frequency","coaching recommendations","Slack/email alerts"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_crono__cap_10","uri":"capability://planning.reasoning.sales.playbook.enforcement.with.process.adherence.tracking","name":"sales playbook enforcement with process adherence tracking","description":"Defines and enforces sales process steps (discovery, qualification, proposal, negotiation) by analyzing rep behavior against playbook requirements. Detects when reps skip steps (e.g., moving deal to proposal without discovery call) or deviate from methodology, and surfaces coaching alerts. Tracks adherence metrics per rep and team to identify process gaps. Integrates with call transcripts to verify that required discovery questions were asked before advancing deals.","intents":["I want to ensure reps follow our sales methodology instead of taking shortcuts","I need to identify which reps are skipping steps and coach them on process","I want to track team-wide process adherence to understand if our methodology is being followed"],"best_for":["Sales organizations with defined, documented sales methodologies (Sandler, Challenger, MEDDIC, etc.)","Teams with 10+ reps where process consistency is hard to enforce manually","Sales leaders who believe process discipline drives outcomes"],"limitations":["Requires explicit playbook definition in Crono — generic playbooks may not match org-specific process","Behavioral detection from call transcripts is probabilistic — may flag false positives if reps discuss discovery topics in different order than expected","Cannot enforce process if reps are not recording calls or logging activities — relies on data completeness","May create compliance theater where reps follow process on recorded calls but not in real interactions"],"requires":["Documented sales playbook with defined stages and required activities","Call recording and transcription for behavioral verification","CRM with activity logging (calls, emails, meetings, deal stage progression)","Minimum 3-6 months of data to establish baseline adherence"],"input_types":["sales playbook definition (stages, required activities, discovery questions)","call transcripts (to verify discovery questions were asked)","CRM activity records (to verify required activities were logged)","deal progression data (stage changes and timing)"],"output_types":["process adherence scorecard (% of deals following playbook)","deviation alerts (deal moved to stage X without completing required step Y)","coaching recommendations (which reps need process training)","playbook effectiveness analysis (do deals following playbook have higher win rates?)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_crono__cap_11","uri":"capability://planning.reasoning.deal.risk.assessment.with.intervention.recommendations","name":"deal risk assessment with intervention recommendations","description":"Analyzes deals for risk factors (no recent activity, competitor mentioned, budget not confirmed, decision-maker not engaged) and assigns risk scores (low/medium/high) to flag deals at risk of slipping or closing. Correlates risk factors with historical deal outcomes to identify which combinations are most predictive of loss. Generates intervention recommendations (e.g., 'schedule executive sponsor call', 'send competitive positioning email') based on risk factors and similar historical deals.","intents":["I want to identify deals at risk of slipping before they become problems","I need to know what actions to take to save at-risk deals","I want to understand which risk factors are most predictive of deal loss so I can focus on those"],"best_for":["Sales managers managing large pipelines where deal health is hard to track manually","Organizations with long sales cycles (6+ months) where early intervention matters","Sales leaders who want to reduce deal slippage and improve forecast accuracy"],"limitations":["Risk assessment is probabilistic and cannot predict with certainty — some high-risk deals will close, some low-risk deals will slip","Intervention recommendations are generic unless trained on org-specific playbook — may not align with actual deal recovery tactics","Requires rich deal data (activity history, conversation signals, competitor mentions) — deals with sparse data will get generic risk scores","May create false sense of security if reps rely on risk scores instead of actively managing deals"],"requires":["CRM with opportunity and activity data","Call recording/transcription for conversation signals","Historical win/loss data with reasons for loss","Minimum 100 closed deals for model training"],"input_types":["opportunity records (stage, amount, close date, age in stage)","activity history (last activity date, activity frequency)","conversation signals (competitor mentions, budget confirmation, decision-maker engagement)","historical deal outcomes (won/lost, reason for loss)"],"output_types":["risk score (0-100 or low/medium/high)","risk factors breakdown (which factors contributing to risk)","intervention recommendations (specific actions to reduce risk)","similar historical deals (examples of deals with similar risk profile and outcomes)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_crono__cap_2","uri":"capability://planning.reasoning.predictive.lead.scoring.with.engagement.and.firmographic.data.fusion","name":"predictive lead scoring with engagement and firmographic data fusion","description":"Combines CRM data (company size, industry, deal stage), engagement metrics (email opens, website visits, content downloads), and conversation signals to assign probabilistic deal-close scores to opportunities. Uses gradient boosting or logistic regression models trained on historical win/loss data to rank leads by likelihood-to-close. Scores update in real-time as new engagement or conversation data arrives, enabling dynamic pipeline prioritization without manual re-ranking.","intents":["I want to know which deals in my pipeline are most likely to close this quarter so I can focus my time","I need to identify stalled deals that need intervention before they slip to next quarter","I want to surface high-potential leads that my team might be overlooking"],"best_for":["Sales teams with 6+ month sales cycles where deal velocity is hard to predict","Organizations with 500+ opportunities in pipeline where manual prioritization is impractical","Sales leaders who want data-driven pipeline forecasting instead of rep gut feel"],"limitations":["Model accuracy depends on historical data quality — if past win/loss records are incomplete or biased, predictions will be skewed","Requires 12+ months of historical data and 100+ closed deals to train reliable models; new orgs will see generic scores until sufficient data accumulates","Cannot account for external market events (recession, competitor launch, regulatory change) that shift deal probability","Scores may create self-fulfilling prophecies if reps ignore low-scoring deals that could have closed"],"requires":["CRM with 12+ months of historical opportunity and activity data","Engagement tracking (email opens, website analytics, or intent data provider)","Closed-won and closed-lost deal records with accurate close dates","Minimum 100 closed deals for model training"],"input_types":["opportunity records (stage, amount, close date, industry, company size)","activity history (calls, emails, meetings)","engagement metrics (email opens, clicks, page views)","conversation signals (deal signals from intelligence capability)"],"output_types":["probability score (0-100)","score components breakdown (e.g., 40% from engagement, 30% from firmographics, 30% from conversation)","confidence interval","risk factors (e.g., 'no recent activity', 'competitor mentioned')"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_crono__cap_3","uri":"capability://text.generation.language.automated.email.and.outreach.sequence.generation.with.personalization","name":"automated email and outreach sequence generation with personalization","description":"Generates personalized email sequences and follow-up messaging based on prospect company data, industry, deal stage, and previous conversation context. Uses prompt engineering or fine-tuned language models to create subject lines, body copy, and call-to-action text that adapts to prospect profile without requiring manual template creation. Integrates with email platforms (Gmail, Outlook) and CRM to schedule sends and track opens/clicks, feeding engagement data back into lead scoring.","intents":["I want to generate personalized outreach emails without spending 30 minutes per prospect writing custom messages","I need to create follow-up sequences that adapt based on prospect response and engagement","I want to A/B test subject lines and messaging to improve open and reply rates"],"best_for":["Sales development teams (SDRs) handling 50+ prospects per week","Account executives managing large pipelines who need quick follow-up messaging","Sales leaders who want to standardize outreach quality across teams"],"limitations":["Generated copy may lack authentic voice or industry-specific terminology if training data is generic — requires human review for high-value deals","Personalization depth limited by available prospect data — generic emails if CRM lacks company research or prior interaction history","Cannot guarantee compliance with anti-spam regulations (CAN-SPAM, GDPR) without manual review; generated sequences may violate frequency caps","Model may generate clichéd or overly salesy language if not fine-tuned on org-specific brand voice"],"requires":["CRM with prospect company and contact data","Email platform integration (Gmail, Outlook, or native email service)","Access to company research data (industry, size, recent news) for personalization context","Optional: historical email performance data (open rates, reply rates) for model fine-tuning"],"input_types":["prospect contact record (name, title, company, industry)","company firmographic data (size, revenue, recent funding, news)","deal context (stage, amount, previous interactions)","conversation history (prior emails, call notes)"],"output_types":["email subject line (text)","email body copy (HTML or plain text)","call-to-action text","send time recommendation","follow-up sequence schedule (JSON)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_crono__cap_4","uri":"capability://planning.reasoning.sales.pipeline.forecasting.with.anomaly.detection","name":"sales pipeline forecasting with anomaly detection","description":"Analyzes historical deal velocity, win rates by stage, and current pipeline composition to forecast quarterly revenue with confidence intervals. Detects anomalies (e.g., unusual number of deals stuck in negotiation, higher-than-normal churn from specific stage) that signal pipeline health issues. Uses time-series analysis and statistical methods to identify trends and flag when pipeline trajectory deviates from historical patterns, enabling proactive intervention.","intents":["I want to forecast Q4 revenue with confidence intervals instead of just adding up deal amounts","I need to know if my pipeline is on track to hit quota or if I should adjust targets","I want early warning when deals are moving slower than normal so I can unblock them"],"best_for":["Sales leaders and finance teams responsible for revenue forecasting","Organizations with 12+ months of historical deal data and consistent sales processes","Teams with variable deal velocity where historical averages are unreliable"],"limitations":["Forecasts degrade during market disruptions (recession, product launch, competitor entry) that break historical patterns","Requires consistent deal stage definitions and accurate stage progression dates — inconsistent CRM hygiene will produce unreliable forecasts","Cannot account for rep-specific performance variance; aggregate forecasts may mask individual underperformance","Anomaly detection may flag legitimate business changes (new product launch, market expansion) as problems"],"requires":["CRM with 12+ months of deal history including stage progression dates","Accurate deal close dates and won/lost status","Consistent deal stage definitions across team","Minimum 50 closed deals per quarter for statistical reliability"],"input_types":["opportunity records (stage, amount, close date, stage entry date)","historical win/loss data by stage","current pipeline snapshot (deals by stage, age in stage)","optional: external factors (market conditions, product releases)"],"output_types":["revenue forecast (point estimate + confidence interval)","deal count forecast by stage","anomaly alerts (JSON with severity and description)","trend analysis (velocity increasing/decreasing)","stage-specific win rate and average cycle time"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_crono__cap_5","uri":"capability://data.processing.analysis.multi.channel.engagement.tracking.and.attribution","name":"multi-channel engagement tracking and attribution","description":"Consolidates engagement data from email, calls, meetings, website visits, and content interactions into a unified activity timeline per prospect. Maps each engagement to CRM records and attributes deal progression to specific touchpoints, enabling analysis of which channels and messages drive advancement. Integrates with email platforms, calendar systems, web analytics, and intent data providers to create a complete engagement picture without manual data entry.","intents":["I want to see all interactions with a prospect across email, calls, and meetings in one place","I need to understand which touchpoints actually moved deals forward vs. which were noise","I want to identify which channels (email vs. call vs. meeting) are most effective for our sales process"],"best_for":["Sales teams using multiple communication tools (email, Slack, Zoom, phone) without unified logging","Organizations trying to optimize sales motion by understanding which channels drive deals","Sales ops teams building data infrastructure for analytics and reporting"],"limitations":["Requires integrations with each communication platform — missing integrations create blind spots in engagement history","Attribution is probabilistic and cannot definitively prove causation (e.g., deal closed after email, but was it the email or the prior call?)","Privacy regulations (GDPR, CCPA) may restrict tracking of certain engagement types (web visits, email opens) without explicit consent","Latency in data consolidation — real-time view may lag 5-15 minutes behind actual interactions"],"requires":["CRM with contact and opportunity records","Email platform integration (Gmail, Outlook, or native email service)","Calendar integration (Google Calendar, Outlook Calendar)","Optional: call recording/transcription service, web analytics, intent data provider","API access to each communication platform"],"input_types":["email metadata (sender, recipient, timestamp, subject, open/click events)","calendar events (attendees, duration, meeting notes)","call logs (duration, timestamp, recording/transcript)","website analytics (page views, time on page, form submissions)","intent signals (content downloads, pricing page visits)"],"output_types":["unified activity timeline (JSON with timestamp, channel, action, outcome)","engagement summary (total touches, channels used, last interaction date)","channel effectiveness metrics (conversion rate by channel)","attribution model output (which touchpoints contributed to deal close)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_crono__cap_6","uri":"capability://data.processing.analysis.competitive.intelligence.extraction.from.conversations","name":"competitive intelligence extraction from conversations","description":"Automatically identifies and extracts mentions of competitors, competitive positioning, and customer objections related to competing solutions from call transcripts, emails, and meeting notes. Categorizes competitive mentions by competitor name, feature comparison, and pricing discussion, then aggregates insights across deals to surface market trends. Uses NER (named entity recognition) and intent classification to distinguish between casual mentions and serious competitive threats.","intents":["I want to know which competitors are being discussed in customer conversations so I can track competitive threats","I need to understand what features or pricing customers are comparing us against","I want to identify common objections related to competitor solutions so I can coach reps on handling them"],"best_for":["Product marketing teams tracking competitive positioning and market perception","Sales leaders identifying which competitors are winning deals and why","Sales enablement teams building competitive battle cards based on real customer conversations"],"limitations":["NER accuracy varies by competitor name uniqueness — generic names (e.g., 'Salesforce') are easy, but startup names or acronyms may be missed","Cannot distinguish between serious competitive threats and casual mentions without additional context","Requires call transcription or email access; organizations without recording/transcription infrastructure cannot use this capability","Competitive intelligence may be outdated if analysis is batch-processed rather than real-time"],"requires":["Call recording and transcription service (Gong, Chorus, Otter, or native)","Email access for message analysis","Competitor database (list of known competitors and aliases)","CRM connection to map mentions to opportunities and deals"],"input_types":["call transcripts (text)","email body text","meeting notes","competitor database (JSON with competitor names and aliases)"],"output_types":["competitor mention extraction (JSON with competitor name, context, deal ID)","competitive threat summary (which competitors mentioned most frequently)","objection categories (pricing, features, integration, support)","market trend analysis (which competitors gaining/losing traction)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_crono__cap_7","uri":"capability://data.processing.analysis.sales.rep.performance.analytics.with.coaching.recommendations","name":"sales rep performance analytics with coaching recommendations","description":"Analyzes individual rep performance across metrics (call volume, email volume, deal velocity, win rate, average deal size) and compares against team benchmarks to identify high performers and underperformers. Correlates rep behavior (talk-to-listen ratio, objection handling, discovery questions asked) with outcomes to surface coaching opportunities. Generates rep-specific recommendations (e.g., 'increase discovery questions by 20% to match top performers') based on behavioral analysis of top-performing reps.","intents":["I want to identify which reps are underperforming and understand why","I need to understand what top performers are doing differently so I can coach others","I want to track rep activity and outcomes to ensure they're following the sales process"],"best_for":["Sales managers with 5-20 direct reports who need data-driven coaching insights","Sales enablement teams building training programs based on top performer behavior","Organizations with call recording and transcription infrastructure"],"limitations":["Behavioral analysis requires call transcripts and CRM data — email-only teams get limited insights","Coaching recommendations are generic unless trained on org-specific sales playbook; may not align with company methodology","Cannot account for territory differences (e.g., rep in mature market vs. new market) — raw metrics comparisons may be unfair","Privacy concerns if performance data is too granular or used punitively rather than developmentally"],"requires":["Call recording and transcription for all reps","CRM with activity and outcome data (calls, emails, deals closed)","Sales playbook or methodology documentation for context","Minimum 3-6 months of data per rep for reliable benchmarking"],"input_types":["call transcripts (text)","CRM activity records (calls, emails, meetings, deals)","deal outcomes (won/lost, deal size, cycle time)","sales playbook (optional, for behavior benchmarking)"],"output_types":["rep performance scorecard (activity metrics, outcome metrics, benchmarks)","behavioral analysis (talk-to-listen ratio, discovery questions, objection handling)","coaching recommendations (specific, actionable suggestions)","peer comparison (how rep ranks vs. team average and top performers)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_crono__cap_8","uri":"capability://data.processing.analysis.crm.data.quality.monitoring.and.enrichment","name":"crm data quality monitoring and enrichment","description":"Continuously monitors CRM records for data quality issues (missing fields, outdated information, duplicate records, inconsistent formatting) and flags records that fall below quality thresholds. Automatically enriches contact and company records with third-party data (company size, industry, recent news, technographics) from data providers. Identifies and merges duplicate records using fuzzy matching on company name, domain, and contact email.","intents":["I want to ensure our CRM data is clean and complete so AI features work reliably","I need to automatically enrich prospect records with company data without manual research","I want to identify and merge duplicate records that are cluttering our database"],"best_for":["Sales ops teams responsible for CRM data governance","Organizations with poor data hygiene where AI features are unreliable","Teams using data enrichment to improve lead quality and targeting"],"limitations":["Enrichment accuracy depends on third-party data provider quality — may introduce incorrect or outdated information","Duplicate detection using fuzzy matching has false positive/negative rates — requires human review for high-stakes merges","Continuous monitoring adds computational overhead and API costs if using third-party enrichment services","Cannot fix structural data issues (e.g., inconsistent field naming across teams) without manual CRM configuration"],"requires":["CRM with API access (Salesforce, HubSpot, Pipedrive, etc.)","Optional: third-party data enrichment provider (Apollo, ZoomInfo, Hunter, Clearbit)","Data quality rules definition (which fields are required, acceptable formats)"],"input_types":["CRM contact records (name, email, company, title)","CRM company records (name, domain, industry, size)","third-party enrichment data (company size, revenue, recent news, technographics)"],"output_types":["data quality report (missing fields, outdated records, duplicates)","enrichment suggestions (company data, contact info, technographics)","duplicate merge recommendations (with confidence scores)","data quality scorecard (% complete, % accurate, % duplicates)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_crono__cap_9","uri":"capability://tool.use.integration.slack.native.deal.alerts.and.workflow.actions","name":"slack-native deal alerts and workflow actions","description":"Surfaces deal updates, conversation signals, and action items directly in Slack channels without requiring reps to log into Crono or CRM. Sends alerts when deals move stages, high-priority signals are detected (budget mentioned, competitor threat), or follow-ups are due. Enables reps to take actions (snooze alerts, log activities, update deal stage) directly from Slack without context switching. Uses Slack bot API and slash commands to create lightweight workflow automation.","intents":["I want deal alerts and action items to come to me in Slack instead of checking another tool","I need to quickly log activities or update deal status without leaving Slack","I want my team to stay informed about important deal signals without email overload"],"best_for":["Sales teams already using Slack as primary communication tool","Organizations with high-velocity sales where real-time alerts matter","Reps who want to minimize context switching between tools"],"limitations":["Slack-native actions are limited compared to full CRM interface — complex updates require logging into CRM","Alert fatigue if thresholds are not tuned carefully — too many alerts will be ignored","Requires Slack workspace admin approval and OAuth scopes that may conflict with security policies","Cannot guarantee message delivery or read receipts — reps may miss critical alerts if they're not actively monitoring Slack"],"requires":["Slack workspace with admin access to install apps","CRM integration (Salesforce, HubSpot, etc.) to pull deal data","Slack OAuth app with permissions for posting messages and slash commands"],"input_types":["deal updates from CRM (stage change, amount change, close date change)","conversation signals (deal signals, sentiment, competitor mentions)","activity due dates (follow-ups, next steps)"],"output_types":["Slack messages (formatted with deal summary, action buttons)","slash command responses (activity logging, deal updates)","thread-based conversations (reps can discuss deals in Slack)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Active CRM subscription (Salesforce, HubSpot, Pipedrive, or similar)","OAuth 2.0 API access credentials for CRM","Email/calendar integration (Gmail, Outlook, or Exchange)","Minimum 3-6 months of historical CRM data for pattern learning","Call recording integration (Gong, Chorus, or native Zoom/Teams recording)","Transcription service (Otter, Rev, or built-in speech-to-text)","CRM connection to map conversations to opportunities","Minimum 100 recorded conversations for model calibration","Documented sales playbook with defined stages and required activities","Call recording and transcription for behavioral verification"],"failure_modes":["Effectiveness degrades significantly if CRM data is incomplete or inconsistently formatted — garbage in, garbage out","Requires native API connectors for each CRM; unsupported systems fall back to manual integration or webhooks with higher latency","Cannot distinguish between genuine deal signals and false positives without training on org-specific sales playbooks — initial accuracy may be 60-70%","Accuracy on deal signal detection varies by industry vertical — generic models may miss domain-specific terminology (e.g., 'procurement cycle' vs 'buying process')","Requires call recording or meeting transcription; email-only teams get limited signal extraction","Real-time analysis adds 2-5 second latency to live call processing; batch analysis of recorded calls is faster but delayed","Cannot distinguish between exploratory conversations and serious deal progression without historical context","Requires explicit playbook definition in Crono — generic playbooks may not match org-specific process","Behavioral detection from call transcripts is probabilistic — may flag false positives if reps discuss discovery topics in different order than expected","Cannot enforce process if reps are not recording calls or logging activities — relies on data completeness","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"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":"2026-05-24T12:16:30.282Z","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=crono","compare_url":"https://unfragile.ai/compare?artifact=crono"}},"signature":"jeUquxmuucYe0tCtN1uXNARH33ikKzz9e2N9Nu/PM2MnYrSHAHvuVGV3RGiyYZgsAgBwtDD3UHCpuuAU7q5TDQ==","signedAt":"2026-06-20T12:09:47.565Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/crono","artifact":"https://unfragile.ai/crono","verify":"https://unfragile.ai/api/v1/verify?slug=crono","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"}}