{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_toma","slug":"toma","name":"Toma","type":"product","url":"https://www.toma.so","page_url":"https://unfragile.ai/toma","categories":["app-builders"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_toma__cap_0","uri":"capability://automation.workflow.ai.driven.customer.follow.up.automation","name":"ai-driven customer follow-up automation","description":"Automatically generates and schedules customer follow-up communications (email, SMS, or in-app messages) based on dealership-defined triggers (e.g., test drive completion, quote expiration, service appointment reminders). The system likely uses rule-based workflow engines combined with NLP to personalize message content based on customer interaction history and vehicle preferences, reducing manual follow-up overhead for sales teams.","intents":["Automatically send personalized follow-up messages to leads without manual intervention","Reduce response time to customer inquiries by triggering immediate automated outreach","Ensure no leads fall through the cracks by systematically tracking and re-engaging dormant prospects","Customize follow-up cadence and messaging based on customer segment or vehicle type"],"best_for":["Sales teams at mid-sized dealerships with 50+ monthly leads","Dealership managers seeking to reduce administrative burden on sales staff","Operations teams looking to standardize follow-up processes across multiple locations"],"limitations":["Likely requires manual configuration of trigger rules and message templates — no out-of-the-box industry templates documented","Effectiveness depends on quality of underlying customer data; garbage-in-garbage-out for personalization","No visibility into A/B testing capabilities for message optimization or conversion tracking","May require integration with existing DMS (Dealer Management System) to access customer interaction history"],"requires":["Active dealership DMS or CRM system with API access","Customer contact data (email, phone, vehicle history)","Configuration of business rules and message templates by dealership staff"],"input_types":["customer interaction events (test drive, quote request, service appointment)","customer profile data (name, vehicle preferences, contact info)","dealership-defined rules and message templates"],"output_types":["scheduled outbound messages (email, SMS, push notification)","delivery status and engagement metrics"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toma__cap_1","uri":"capability://planning.reasoning.lead.prioritization.and.routing.with.ai.scoring","name":"lead prioritization and routing with ai scoring","description":"Analyzes incoming leads using machine learning models trained on dealership conversion data to score lead quality and automatically route high-priority leads to appropriate sales staff. The system likely ingests historical conversion data, customer demographics, and interaction patterns to predict which leads are most likely to convert, enabling sales teams to focus on high-value prospects first.","intents":["Automatically rank incoming leads by conversion probability so sales staff prioritize high-value prospects","Route leads to the best-fit salesperson based on historical performance with similar customer profiles","Reduce time spent on low-probability leads by identifying and deprioritizing them early","Identify patterns in successful conversions to improve overall sales team performance"],"best_for":["Dealerships with 100+ monthly leads and multiple sales staff competing for assignments","Sales managers seeking data-driven lead allocation instead of manual assignment","Teams with historical conversion data (6+ months) to train ML models"],"limitations":["Model accuracy depends on quality and volume of historical conversion data — new dealerships with <3 months history will see poor predictions","No transparency on which features the ML model uses for scoring (black-box risk for sales staff trust)","Potential bias if historical data reflects demographic or geographic skew in past conversions","Requires ongoing model retraining as market conditions and sales team composition change"],"requires":["Minimum 3-6 months of historical lead and conversion data","Customer demographic and interaction data (source, vehicle interest, contact method)","Sales staff performance metrics (conversion rate, average deal size by salesperson)"],"input_types":["incoming lead data (name, contact info, vehicle interest, lead source)","historical conversion records with outcomes","sales staff performance metrics"],"output_types":["lead quality score (numeric or percentile ranking)","recommended salesperson assignment","priority tier (hot, warm, cold)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toma__cap_2","uri":"capability://text.generation.language.conversational.ai.customer.support.chatbot","name":"conversational ai customer support chatbot","description":"Deploys a natural language chatbot (likely built on LLM or retrieval-augmented generation) that handles common dealership customer inquiries (inventory questions, financing options, service scheduling, appointment reminders) without human intervention. The system integrates with dealership knowledge bases (inventory data, pricing, service menus) and escalates complex queries to human agents, reducing support ticket volume.","intents":["Answer customer questions about vehicle inventory, pricing, and availability 24/7 without human staff","Schedule service appointments or test drives through conversational interface","Provide financing and trade-in information without requiring customer to speak with sales staff","Reduce support ticket volume by handling routine inquiries automatically"],"best_for":["Dealerships receiving 50+ customer support inquiries daily","Teams seeking to reduce after-hours support burden or extend support availability","Dealerships with well-documented inventory, pricing, and service information"],"limitations":["Chatbot accuracy depends on quality of underlying knowledge base — outdated inventory or pricing data will cause incorrect responses","No visibility into escalation logic or how complex queries are routed to human agents","May struggle with context-dependent questions (e.g., 'What's the best car for my family?' requires understanding customer needs)","Integration with dealership DMS required to access real-time inventory and pricing — implementation complexity unknown"],"requires":["Dealership knowledge base (inventory data, pricing, service menus, financing options)","Integration with DMS or inventory management system for real-time data","Customer communication channels (web chat, SMS, Facebook Messenger, etc.)","Human escalation queue for complex or unresolved queries"],"input_types":["customer natural language queries (text or voice)","dealership knowledge base (inventory, pricing, service info)","customer conversation history"],"output_types":["natural language responses","appointment confirmations","escalation to human agent with context"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toma__cap_3","uri":"capability://data.processing.analysis.automated.customer.data.extraction.and.normalization","name":"automated customer data extraction and normalization","description":"Extracts and standardizes customer information from unstructured sources (emails, phone call transcripts, form submissions, SMS) into structured dealership CRM/DMS fields using NLP and entity recognition. The system identifies key data points (name, contact info, vehicle interests, budget, timeline) and maps them to dealership database schema, reducing manual data entry and improving data quality.","intents":["Automatically extract customer information from emails or call transcripts without manual typing","Standardize customer data format across multiple input sources (web forms, phone, email, SMS)","Identify and flag missing or conflicting customer information for follow-up","Reduce data entry errors and improve CRM data quality"],"best_for":["Dealerships with high-volume customer interactions across multiple channels","Teams struggling with manual data entry bottlenecks or poor CRM data quality","Operations seeking to reduce administrative overhead on sales support staff"],"limitations":["Accuracy depends on clarity and completeness of source data — garbled transcripts or incomplete forms will produce poor extraction","No visibility into how the system handles ambiguous or conflicting information (e.g., customer mentions two different budgets)","Likely requires manual review and correction of extracted data, especially for edge cases","Integration with dealership DMS schema required — may need custom mapping for non-standard field names"],"requires":["Dealership DMS or CRM with defined customer data schema","Customer interaction data sources (email, call transcripts, web forms, SMS)","Integration API or webhook to push extracted data to DMS"],"input_types":["unstructured text (emails, call transcripts, SMS messages)","semi-structured data (web form submissions)","customer interaction records"],"output_types":["structured customer records (name, contact, vehicle interest, budget, timeline)","confidence scores for extracted fields","flagged records requiring manual review"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toma__cap_4","uri":"capability://planning.reasoning.predictive.customer.lifetime.value.and.churn.analysis","name":"predictive customer lifetime value and churn analysis","description":"Analyzes customer interaction patterns, purchase history, and engagement metrics to predict customer lifetime value (CLV) and churn risk using machine learning models. The system identifies high-value customers likely to generate repeat business (service, trade-ins, referrals) and flags at-risk customers for retention outreach, enabling dealerships to allocate resources strategically.","intents":["Identify high-value customers likely to generate repeat business through service or future vehicle purchases","Predict which customers are at risk of switching to competitors and trigger retention campaigns","Segment customers by lifetime value to prioritize VIP treatment and personalized offers","Forecast future revenue from existing customer base to inform inventory and staffing decisions"],"best_for":["Dealerships with 2+ years of customer transaction history","Service departments seeking to maximize repeat business from existing customers","Management teams looking to improve customer retention and lifetime value metrics"],"limitations":["Model accuracy requires substantial historical data (2+ years) — new dealerships or those with sparse transaction records will see poor predictions","Churn prediction effectiveness depends on defining what 'churn' means for dealership (no purchase in 12 months? switched to competitor?)","No visibility into which customer behaviors drive CLV predictions — may be opaque to dealership staff","Seasonal and economic factors (recession, new model releases) can invalidate historical patterns"],"requires":["Minimum 2 years of customer transaction history (vehicle purchases, service visits, parts sales)","Customer interaction data (email opens, website visits, service appointment attendance)","Customer demographic and vehicle ownership data"],"input_types":["historical customer transactions (purchase date, vehicle, price, service visits)","customer interaction metrics (engagement, communication frequency)","customer demographic data"],"output_types":["customer lifetime value score (numeric or percentile)","churn risk score with confidence interval","recommended retention actions or offers"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toma__cap_5","uri":"capability://automation.workflow.intelligent.appointment.scheduling.and.calendar.optimization","name":"intelligent appointment scheduling and calendar optimization","description":"Automatically schedules customer appointments (test drives, service, consultations) by analyzing salesperson availability, customer preferences, and dealership capacity constraints using constraint-satisfaction algorithms. The system optimizes for minimizing customer wait times, balancing workload across staff, and maximizing dealership throughput while respecting business hours and resource availability.","intents":["Automatically schedule test drives or service appointments without manual back-and-forth","Balance appointment load across sales and service staff to prevent bottlenecks","Suggest optimal appointment times based on customer availability and staff schedules","Reduce no-shows by sending timely reminders and confirming appointments"],"best_for":["Dealerships with high appointment volume (50+ per week) and multiple staff members","Service departments seeking to optimize technician utilization and reduce wait times","Teams struggling with scheduling conflicts or uneven workload distribution"],"limitations":["Optimization quality depends on accurate staff availability and capacity data — manual calendar updates or no-shows will degrade performance","No visibility into how the system handles complex constraints (e.g., specific technician expertise, vehicle type requirements)","Likely requires integration with dealership calendar system (Outlook, Google Calendar) — implementation complexity unknown","May not account for soft constraints like customer preference for specific staff member or time-of-day preferences"],"requires":["Staff calendar system with availability data (Outlook, Google Calendar, or dealership-specific system)","Customer contact information and availability preferences","Dealership business hours, capacity constraints, and resource availability","Integration API to push appointments to customer communication channels"],"input_types":["appointment request (customer name, service type, preferred date/time range)","staff availability and capacity data","dealership constraints (hours, resources, expertise requirements)"],"output_types":["confirmed appointment with date/time","appointment reminder notifications","calendar updates for staff and customers"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toma__cap_6","uri":"capability://text.generation.language.ai.powered.sales.coaching.and.performance.analytics","name":"ai-powered sales coaching and performance analytics","description":"Analyzes sales interactions (call recordings, email transcripts, chat logs) to provide real-time coaching feedback and identify performance improvement opportunities using NLP and conversation analysis. The system evaluates sales techniques (objection handling, closing tactics, product knowledge) against dealership best practices and generates personalized coaching recommendations for individual sales staff.","intents":["Provide real-time feedback to sales staff on call quality and sales technique","Identify top-performing sales interactions to use as training examples","Track individual salesperson performance metrics (conversion rate, average deal size, customer satisfaction)","Generate personalized coaching recommendations based on interaction analysis"],"best_for":["Sales managers seeking to improve team performance through data-driven coaching","Dealerships with call recording or chat logging infrastructure","Teams looking to standardize sales processes and best practices across multiple locations"],"limitations":["Accuracy depends on quality of sales interaction recordings — poor audio quality or incomplete transcripts will degrade analysis","No visibility into how the system evaluates 'good' sales technique — may not align with dealership-specific sales methodology","Privacy and compliance concerns with recording and analyzing employee interactions (may require consent and compliance with labor laws)","Coaching recommendations may be generic rather than tailored to individual salesperson strengths/weaknesses"],"requires":["Sales interaction recordings (phone calls, video calls, chat logs)","Transcription service or integration (speech-to-text)","Dealership sales methodology or best practices documentation","Sales staff performance data (conversion rates, deal sizes, customer satisfaction scores)"],"input_types":["sales call recordings or transcripts","email or chat interactions","sales performance metrics"],"output_types":["interaction quality score","coaching recommendations","performance analytics and trends","peer comparison benchmarks"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_toma__cap_7","uri":"capability://planning.reasoning.dynamic.pricing.and.inventory.recommendation.engine","name":"dynamic pricing and inventory recommendation engine","description":"Analyzes market conditions, competitor pricing, inventory age, and customer demand patterns to recommend optimal vehicle pricing and suggest inventory adjustments using machine learning models. The system identifies slow-moving inventory and recommends price reductions or promotional strategies, while also suggesting which vehicle types to stock based on local demand patterns.","intents":["Recommend optimal pricing for vehicles based on market conditions and inventory age","Identify slow-moving inventory and suggest price adjustments or promotional strategies","Forecast demand for specific vehicle types to inform inventory purchasing decisions","Maximize profit margins by balancing pricing competitiveness with inventory turnover"],"best_for":["Dealerships with 50+ vehicle inventory seeking to optimize pricing and turnover","Management teams looking to improve inventory efficiency and reduce carrying costs","Dealerships in competitive markets where pricing agility is critical"],"limitations":["Pricing recommendations depend on accurate competitor pricing data — requires integration with market data sources or manual updates","Model accuracy depends on historical sales data and market conditions — may not adapt quickly to sudden market shifts","No visibility into how the system balances profit margin vs. inventory turnover — may recommend aggressive discounting","Requires integration with dealership inventory management system to access vehicle data and sales history"],"requires":["Dealership inventory data (vehicle details, acquisition cost, current price, days in inventory)","Historical sales data (sold vehicles, sale price, time-to-sale)","Market data (competitor pricing, local demand trends, seasonal patterns)","Integration with inventory management system"],"input_types":["vehicle inventory (make, model, year, mileage, features, current price)","historical sales transactions","market pricing data (competitor prices, market trends)","customer demand signals (search volume, inquiry patterns)"],"output_types":["recommended pricing adjustments","inventory recommendations (what to stock, what to reduce)","pricing strategy suggestions (discounts, promotions, bundles)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["Active dealership DMS or CRM system with API access","Customer contact data (email, phone, vehicle history)","Configuration of business rules and message templates by dealership staff","Minimum 3-6 months of historical lead and conversion data","Customer demographic and interaction data (source, vehicle interest, contact method)","Sales staff performance metrics (conversion rate, average deal size by salesperson)","Dealership knowledge base (inventory data, pricing, service menus, financing options)","Integration with DMS or inventory management system for real-time data","Customer communication channels (web chat, SMS, Facebook Messenger, etc.)","Human escalation queue for complex or unresolved queries"],"failure_modes":["Likely requires manual configuration of trigger rules and message templates — no out-of-the-box industry templates documented","Effectiveness depends on quality of underlying customer data; garbage-in-garbage-out for personalization","No visibility into A/B testing capabilities for message optimization or conversion tracking","May require integration with existing DMS (Dealer Management System) to access customer interaction history","Model accuracy depends on quality and volume of historical conversion data — new dealerships with <3 months history will see poor predictions","No transparency on which features the ML model uses for scoring (black-box risk for sales staff trust)","Potential bias if historical data reflects demographic or geographic skew in past conversions","Requires ongoing model retraining as market conditions and sales team composition change","Chatbot accuracy depends on quality of underlying knowledge base — outdated inventory or pricing data will cause incorrect responses","No visibility into escalation logic or how complex queries are routed to human agents","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2833333333333333,"quality":0.63,"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=toma","compare_url":"https://unfragile.ai/compare?artifact=toma"}},"signature":"bMVLa5j0EdJwyaL+xieQVca9J6Dqa03Lwt4MDJ7M7HaKuJOY4BAfP3PKik/0nIHJFpuCM9GgAEbIGPEXnjVsCA==","signedAt":"2026-06-21T01:24:25.469Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/toma","artifact":"https://unfragile.ai/toma","verify":"https://unfragile.ai/api/v1/verify?slug=toma","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"}}