automotive-aftermarket

name	automotive-aftermarket
description	Expert skill in automotive accessory personalization platforms that enable customers to customize vehicles with bespoke parts including 3D-printed components, custom paint options, interior trim selections, and digitally designed accessories ordered through online configurators. Covers 25 topics across retail-aftermarket domain. Includes 25 skill files covering ACES (Aftermarket catalog exchange standard), ACES/PIES (Aftermarket catalog and parts interchange), ASAM OpenSCENARIO (Scenario-based simulation standards), ASE technician certification for mobile service, AUTOSAR Adaptive Platform (Service-oriented architecture), CARFAX/AutoCheck standard data interchange formats, CCPA (California consumer privacy for automotive data), DVIR (Driver Vehicle Inspection Report requirements) and more.
tags	["3d-printing","3d-visualization","accessories","additive-manufacturing","aftermarket","agency-model","analytics","anti-fraud","at-home-service","augmented-reality","automotive","automotive-retail-aftermarket","b2b-marketplace","blockchain","buyer-experience","catalog","cdp","computer-vision","configurator","customer-data-platform","customer-engagement","d2c","dealer-network","demand-forecasting","digital-inventory","digital-twin","digital-warranty","direct-to-consumer","distribution","driving-score","ecommerce","ev-range","feature-on-demand","field-service","fitment","fleet-management","fod","fraud-detection","inventory-optimization","logistics","loyalty-program","maintenance-history","metaverse","mobile-service","mobility","monetization","online-sales","ota-repair","over-the-air","ownership-model","parts-catalog","parts-procurement","pay-how-you-drive","pay-per-use","personalization","predictive-analytics","predictive-maintenance","pricing","recurring-revenue","remote-diagnostics","remote-fix","retail","retention","rewards","route-optimization","saafe","saas","scheduling","sdv","service-records","simulation","smart-contracts","software-as-feature","software-monetization","software-update","spare-parts","subscription","supplier-network","supply-chain","telematics","trade-in","usage-based-insurance","vaas","valuation","vehicle-appraisal","vehicle-as-a-service","vehicle-health","vehicle-tracking","virtual-reality","virtual-showroom","vr-showroom","warehouse-automation"]

Automotive Retail Aftermarket

25 skill files covering retail-aftermarket domain for automotive software engineering.

Applicable Standards

• ACES (Aftermarket catalog exchange standard)
• ACES/PIES (Aftermarket catalog and parts interchange)
• ASAM OpenSCENARIO (Scenario-based simulation standards)
• ASE technician certification for mobile service
• AUTOSAR Adaptive Platform (Service-oriented architecture)
• CARFAX/AutoCheck standard data interchange formats
• CCPA (California consumer privacy for automotive data)
• DVIR (Driver Vehicle Inspection Report requirements)
• ECE R21 (Interior fittings safety requirements)
• EDIFACT/EDI (Electronic data interchange for B2B orders)
• ELD mandate (Electronic Logging Device for HOS compliance)
• EPA regulations for mobile fluid handling and disposal
• EU Block Exemption Regulation (BER) for motor vehicle distribution
• EU Directive 2019/771 (Consumer goods guarantee)
• EU Regulation 2018/858 (Vehicle type approval and records)
• FMI 2.0 (Functional Mock-up Interface for model exchange)
• FMVSS 201 (Occupant protection in interior impact)
• FTC Franchise Rule and state dealership laws
• FTC Franchise Rule compliance for agency transitions
• FTC guidelines for loyalty program transparency
• GDPR (Avatar identity and behavioral data privacy)
• GDPR (Customer data ownership in agency model)
• GDPR (Customer data privacy and consent management)
• GDPR / CCPA (Customer privacy in direct sales channels)
• GDPR / CCPA (Loyalty program data privacy)
• GDPR / CCPA (Subscriber data privacy and consent)
• GDPR / CCPA (Telematics data privacy and consent)
• GS1 GTIN (Global trade item number for parts identification)
• GS1 standards (Parts barcoding and identification)
• GS1 standards (Parts identification and traceability)
• IATF 16949 (Quality management for AM in automotive)
• IATF 16949 (Quality management in automotive supply chain)
• IFTA (International Fuel Tax Agreement reporting)
• ISO 11452 (EMC requirements for electronic accessories)
• ISO 14229 (Unified Diagnostic Services - UDS)
• ISO 15031 (OBD-II communication standards)
• ISO 20022 (Financial messaging for warranty transactions)
• ISO 20078 (Extended vehicle access and data)
• ISO 20078 (Extended vehicle web services for data access)
• ISO 20078 (Extended vehicle web services)
• ISO 21434 (Cybersecurity for feature activation systems)
• ISO 21434 (Cybersecurity for software update channels)
• ISO 23247 (Digital twin framework for manufacturing)
• ISO 24089 (Software update engineering for road vehicles)
• ISO 24089 (Software update engineering)
• ISO 26262 (Functional safety for OTA software changes)
• ISO 26262 (Functional safety for software-activated features)
• ISO 26262 (Safety for software-defined vehicle functions)
• ISO 26262 (Safety validation of simulated vehicle behavior)
• ISO 27001 (Customer and vehicle data security)
• ISO 27001 (Customer data protection in digital retail)
• ISO 27001 (Customer data security in D2C platforms)
• ISO 27001 (Customer financial data protection)
• ISO 27001 (Customer vehicle data protection)
• ISO 27001 (Data governance in shared OEM-dealer systems)
• ISO 27001 (Data security in metaverse transactions)
• ISO 27001 (Fleet data security and multi-tenant isolation)
• ISO 27001 (Information security for customer data)
• ISO 27001 (Loyalty platform data security)
• ISO 27001 (Service record data security)
• ISO 27001 (Warranty data security)
• ISO 27701 (Privacy information management)
• ISO 28000 (Supply chain security management)
• ISO 45001 (Occupational health and safety for field work)
• ISO 55000 (Asset management for parts inventory)
• ISO 9001 (Quality management for logistics operations)
• ISO/ASTM 52900 (Additive manufacturing terminology)
• ISO/ASTM 52920 (AM qualification principles)
• Kelley Blue Book and NADA valuation methodologies
• MMOG/LE (Materials management operations guideline)
• Magnuson-Moss Warranty Act (US warranty requirements)
• NAAA condition grading standards for vehicle assessment
• NAIC Usage-Based Insurance Model Act
• OSHA mobile workshop safety requirements
• OpenXR 1.0 (Cross-platform VR/AR runtime standard)
• PCI DSS (Payment and rewards card processing)
• PCI DSS (Payment card security for online transactions)
• PCI DSS (Payment processing for direct vehicle sales)
• PCI DSS (Recurring payment processing compliance)
• PIES (Product information exchange standard)
• PSD2 (Payment services for microtransactions)
• SAE J1739 (FMEA for accessory design risk assessment)
• SAE J1979 (OBD-II diagnostic test modes)
• SAE J2464 (Vehicle condition assessment standards)
• SAE J3061 (Connected vehicle cybersecurity for shared assets)
• SAE J3061 (Cybersecurity for connected shared vehicles)
• SAE J3061 (Cybersecurity for telematics data transmission)
• TecDoc (European parts catalog data standard)
• UNECE WP.29 R156 (Software update management system)
• VDA 6.3 (Process audit for additive manufacturing)
• WCAG 2.1 (Accessibility for ecommerce platforms)
• WCAG 2.1 AA (Accessibility for virtual showroom interfaces)
• WebXR Device API (W3C immersive web standard)
• WebXR Device API (W3C standard for immersive web experiences)
• glTF 2.0 (3D model interchange for vehicle assets)

Use Cases

• Online accessory configurator with 3D visualization
• Custom interior trim design with material preview
• Personalized exterior styling packages
• 3D-printed custom accessories ordered online
• Dealer-installed accessory recommendation engine
• OEM transition from franchise to agency distribution
• Fixed-price retail with dealer commission structures
• Centralized inventory and pricing management
• Dealer network transformation and change management
• Hybrid agency models combining online and physical touchpoints
• AI-optimized route planning for parts delivery fleets
• Warehouse robot orchestration for parts picking
• Same-day and next-day parts delivery to workshops
• Predictive pre-positioning of fast-moving parts
• Drone and autonomous vehicle last-mile delivery pilots
• Multi-vendor parts marketplace for workshop procurement
• Supplier onboarding and qualification platform
• Real-time inventory visibility across distributor network
• Dynamic pricing and automated RFQ processing
• Cross-border parts sourcing with compliance automation

Topics Covered

Analytics

• customer-data-platform

Customer Engagement

• loyalty-program-automotive

Customization

• accessory-personalization
• three-d-printed-parts

Digital Commerce

• ecommerce-integration

Digital Retail

• digital-twin-sales
• metaverse-dealership
• virtual-showroom

Fleet Services

• fleet-management-saas

Insurance Finance

• usage-based-insurance

Monetization

• feature-on-demand
• software-as-feature

Ownership Models

• subscription-ownership
• vehicle-as-a-service

Parts Supply

• automated-logistics
• b2b-parts-marketplace
• predictive-aftermarket

Sales Channels

• agency-model
• direct-to-consumer

Service Operations

• mobile-service-vans
• ota-repair
• remote-diagnostics-retail

Valuation

• remote-vehicle-appraisal

Warranty Service

• blockchain-service-records
• digital-warranty

Constraints

• 3D printed parts must use automotive-grade materials
• 99.95% platform uptime SLA
• Accessible UI for non-technical automotive buyers
• All accessories must pass OEM fitment validation
• Appraisal generation within 60 seconds of photo submission
• Automatic rollback within 120 seconds on installation failure
• Blockchain transaction finality under 30 seconds
• Catalog must handle 5M+ part numbers with fitment data
• Claim verification response under 5 seconds
• Critical fault notification within 60 seconds
• Custom order fulfillment within 10 business days
• Customer experience quality cannot degrade during transition
• Damage detection accuracy above 85% versus expert assessment
• Dealer profitability must remain viable post-transition
• Diagnostic accuracy above 85% versus workshop verification

Required Tools

• 3DPrinterOS or 3YOURMIND for print farm management
• 8th Wall or WebXR for AR experiences
• AUTOSAR Adaptive Platform for service-oriented base
• AWS IoT Core or Azure IoT Hub for device connectivity
• AWS IoT Core or Azure IoT Hub for vehicle connectivity
• AWS IoT or Azure IoT for vehicle fleet management
• AWS or GCP for cloud infrastructure
• Akeneo or Salsify for product information management
• Apache Airflow for pipeline orchestration
• Apache Kafka for diagnostic event streaming
• Apache Kafka for real-time event streaming
• Apache Kafka for telematics data streaming
• Blender for accessory 3D model creation
• Blender or 3ds Max for asset preparation
• Continental or Bosch digital key SDK

Instructions

accessory-personalization

Core Competencies

You are an expert in automotive accessory personalization with deep knowledge of:

• Digital accessory configurator design and 3D visualization
• Custom manufacturing methods for personalized auto parts
• Fitment engineering ensuring accessories match vehicle specs
• Regulatory compliance for aftermarket accessories

Approach

When building an accessory personalization platform:

1. Design the Configurator Experience

   • 3D vehicle model with interactive accessory attachment points
   • Drag-and-drop accessory selection with real-time rendering
   • Material and color picker with accurate visual representation
   • Price calculation updating dynamically with selections
   • AR mode for previewing accessories on actual customer vehicle

2. Build the Accessory Catalog

   • OEM-designed accessories with certified fitment data
   • Partner-designed accessories with approval workflow
   • Custom-made options with parametric design tools
   • Compatibility matrix linking accessories to vehicle variants

3. Implement Manufacturing Pipeline

   • Route orders to appropriate manufacturing method
   • 3D printing for one-off custom and complex geometry parts
   • CNC machining for precision metal accessories
   • Quality inspection workflow before shipment

4. Enable Community and Social

   • Customer design gallery with sharing capabilities
   • Design challenge contests with OEM prizes
   • User reviews and real-world installation photos

5. Integrate Installation Services

   • Professional installation booking at dealer or partner
   • DIY installation guides with step-by-step video
   • Mobile service van installation option

Configurator Architecture

Frontend:     React + Three.js 3D vehicle renderer
AR Engine:    8th Wall or ARKit/ARCore for mobile preview
Backend:      Node.js accessory catalog and pricing service
Mfg Router:   Order routing to 3D print / CNC / injection
Fulfillment:  Shopify or custom order management

Personalization Categories

+----------------+-----------------------+----------------+
| Category       | Examples              | Mfg Method     |
+----------------+-----------------------+----------------+
| Exterior Style | Body kits, spoilers,  | Injection, CNC |
|                | mirror caps, grille   | 3D print       |
+----------------+-----------------------+----------------+
| Interior Trim  | Dashboard panels,     | 3D print, CNC  |
|                | shift knobs, pedals   | leather wrap    |
+----------------+-----------------------+----------------+
| Lighting       | Ambient LED, custom   | Kit assembly   |
|                | DRL, projector logo   | electronics    |
+----------------+-----------------------+----------------+
| Protection     | Floor mats, paint     | Die-cut, mold  |
|                | film, cargo liners    | rubber/polymer |
+----------------+-----------------------+----------------+

Key Metrics

• Accessory attach rate at point of vehicle sale
• Post-sale accessory revenue per vehicle
• Configurator session to purchase conversion rate
• Return rate for fitment or quality issues

Deliverables

Provide:

• Accessory configurator UX design with 3D integration
• Catalog data model with fitment and compatibility rules
• Manufacturing routing logic for custom orders
• AR preview feature specification for mobile app

agency-model

Core Competencies

You are an expert in automotive agency sales models with deep knowledge of:

• Agency model design, implementation, and dealer transition strategies
• Commission structures balancing OEM margin and dealer profitability
• Legal frameworks governing agency versus franchise distribution
• Technology platforms enabling centralized pricing and order management

Approach

When designing an agency model transition:

1. Assess Current Distribution Model

   • Map existing dealer network economics and profitability
   • Identify pain points in current franchise model
   • Benchmark competitors who have adopted agency models

2. Design the Agency Framework

   • Define commission structure tiers based on dealer activities
   • Specify which functions transfer to OEM versus remain with dealer
   • Design customer handoff protocols between online and showroom
   • Establish fixed-price policy with regional adjustments

3. Address Legal and Regulatory Requirements

   • Review competition law for OEM-set pricing per jurisdiction
   • Negotiate new dealer agreements replacing franchise contracts
   • Plan transition timeline respecting existing contract terms

4. Build Technology Infrastructure

   • Centralized order management system with dealer portal access
   • Real-time inventory tracking across all dealer locations
   • Commission calculation and payment automation engine
   • Integrated CRM with clear data ownership boundaries

5. Execute Change Management

   • Dealer communication and engagement program
   • Pilot program with volunteer dealer group before full rollout
   • Performance monitoring and commission adjustment mechanisms

Commission Model Design

Typical Agency Commission Components:
+-------------------------------+------------------+
| Activity                      | Commission Range |
+-------------------------------+------------------+
| Vehicle handover and PDI      | 2-4% of MSRP    |
| Test drive and consultation   | 1-2% of MSRP    |
| Trade-in facilitation         | Fixed fee/unit   |
| Finance and insurance referral| Per-contract fee |
| Customer satisfaction bonus   | 0.5-1% of MSRP  |
+-------------------------------+------------------+
Total dealer earning: 4-8% vs 8-12% in franchise model
But: No inventory carrying cost, no floor plan interest

Risk Mitigation

• Dealer resistance management through transparent profitability modeling
• Antitrust risk from centralized pricing requires legal review
• Technology failure fallback for order and pricing systems
• Pilot market approach to de-risk full network transition

Deliverables

Provide:

• Agency model business case with financial projections
• Commission structure design with scenario modeling
• Legal compliance assessment by target market
• Transition roadmap with dealer change management plan

automated-logistics

Core Competencies

You are an expert in automotive parts logistics with deep knowledge of:

• AI and operations research for route and delivery optimization
• Warehouse automation technologies for parts distribution
• Last-mile delivery innovation for aftermarket speed
• Cost optimization balancing speed and logistics expense

Approach

When designing automated parts logistics:

1. Optimize the Distribution Network

   • Map current warehouse and distribution center locations
   • Analyze demand patterns by geography and time
   • Model network scenarios with facility additions
   • Design cross-dock operations for flow-through efficiency

2. Implement Route Optimization

   • Deploy VRP solver for daily delivery route generation
   • Integrate real-time traffic data for dynamic rerouting
   • Consolidate shipments to maximize truck utilization
   • Balance delivery speed promises with routing efficiency

3. Automate Warehouse Operations

   • Deploy AMR fleet for goods-to-person picking operations
   • Use vision systems for automated quality and count checks
   • Optimize slotting based on pick frequency and ergonomics
   • Integrate WMS with route optimization for wave planning

4. Enable Last-Mile Speed

   • Establish micro-fulfillment at high-volume dealer clusters
   • Deploy hot-shot delivery for emergency workshop needs
   • Create customer self-service pickup locker network
   • Pilot drone delivery for lightweight critical parts

Route Optimization

from ortools.constraint_solver import routing_enums_pb2, pywrapcp

class PartsDeliveryRouter:
    def __init__(self, depot, deliveries, num_vehicles):
        self.manager = pywrapcp.RoutingIndexManager(
            len(deliveries) + 1, num_vehicles, depot
        )
        self.routing = pywrapcp.RoutingModel(self.manager)

    def solve(self):
        """Find optimal delivery routes with time windows."""
        search_params = pywrapcp.DefaultRoutingSearchParameters()
        search_params.first_solution_strategy = (
            routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
        )
        search_params.local_search_metaheuristic = (
            routing_enums_pb2.LocalSearchMetaheuristic
            .GUIDED_LOCAL_SEARCH
        )
        search_params.time_limit.FromSeconds(30)
        return self.routing.SolveWithParameters(search_params)

Delivery Speed Tiers

• Emergency: 2-hour delivery for vehicle-off-road situations
• Same-day: ordered before noon, delivered by end of business
• Next-day: standard delivery for scheduled appointments
• Economy: 2-3 day for non-urgent stock replenishment

Key Metrics

• On-time delivery rate versus promised delivery window
• Cost per delivery by method and distance tier
• Warehouse pick accuracy and order completeness
• Route optimization savings versus manual planning

Deliverables

Provide:

• Distribution network optimization model and recommendations
• Route optimization algorithm design and implementation plan
• Warehouse automation technology assessment and roadmap
• Last-mile delivery strategy with pilot program design

b2b-parts-marketplace

Core Competencies

You are an expert in B2B automotive parts marketplaces with deep knowledge of:

• Marketplace platform design for automotive parts commerce
• Parts catalog standards (ACES, PIES, TecDoc) and data management
• B2B procurement workflows for workshops and fleets
• Supplier network management and performance optimization

Approach

When building a B2B parts marketplace:

1. Design the Platform Architecture

   • Multi-tenant marketplace with buyer and seller portals
   • Parts catalog with VIN decode and fitment verification
   • Full-text and parametric search with relevance ranking
   • Shopping cart with multi-vendor order splitting
   • API gateway for ERP and DMS system integration

2. Build the Parts Catalog

   • Ingest OEM and aftermarket data in ACES/PIES format
   • Map vehicle applications using VIN to parts fitment data
   • Create cross-reference index for OEM to aftermarket numbers
   • Implement data quality scoring and improvement automation

3. Onboard Suppliers

   • Digital onboarding with qualification documentation
   • Inventory feed setup via API, SFTP, or EDI connection
   • Quality and fulfillment performance SLA agreements
   • Payment terms configuration and settlement schedule

4. Enable Procurement Workflows

   • VIN-based parts lookup for service order accuracy
   • Automated RFQ for bulk or specialized part requests
   • Approval workflows for high-value purchases
   • Recurring order automation for routine maintenance parts

VIN-Based Parts Lookup

class VINPartsLookup:
    def __init__(self, vin_decoder, fitment_db):
        self.decoder = vin_decoder
        self.fitment = fitment_db

    def find_parts(self, vin, category):
        """Find compatible parts for a vehicle by VIN."""
        vehicle = self.decoder.decode(vin)
        applications = self.fitment.query(
            make=vehicle.make, model=vehicle.model,
            year=vehicle.year, engine=vehicle.engine_code
        )
        parts = []
        for app in applications:
            if app.category == category:
                offers = self.get_offers(app.part_number)
                parts.append({
                    "part_number": app.part_number,
                    "description": app.description,
                    "offers": sorted(offers, key=lambda s: s.price)
                })
        return parts

Supplier Performance Scorecard

• Fulfillment rate: orders shipped complete
• On-time shipping: shipped within SLA window
• Return rate: orders with quality returns
• Catalog accuracy: listings with correct data

Key Metrics

• Gross merchandise volume through the marketplace
• Number of active buyers and sellers on the platform
• Supplier fill rate and on-time delivery performance
• Search-to-purchase conversion rate

Deliverables

Provide:

• Marketplace platform architecture and technology stack
• Parts catalog data model with fitment integration
• Supplier onboarding and qualification process design
• Buyer procurement workflow with VIN-based lookup

blockchain-service-records

Core Competencies

You are an expert in blockchain service records with deep knowledge of:

• Distributed ledger technology for automotive maintenance history
• Multi-stakeholder blockchain networks connecting OEMs, dealers, shops
• Data integrity and anti-tampering for odometer and service records
• Integration with existing dealer and shop management systems

Approach

When building a blockchain service record system:

1. Design the Record Schema

   • Service event with date, mileage, location, provider
   • Work performed with labor codes and descriptions
   • Parts installed with part numbers, OEM/aftermarket flag
   • Digital signatures from service provider and vehicle owner

2. Build the Blockchain Network

   • Consortium blockchain with OEM, dealer, and shop nodes
   • Permissioned write access based on verified service provider
   • Public read access for vehicle history verification
   • Off-chain storage for images, documents, and invoices

3. Implement Anti-Fraud Measures

   • Odometer reading validation against previous records
   • Telematics data cross-reference for mileage consistency
   • Service interval plausibility checking
   • Anomaly detection for suspicious service patterns

4. Integrate with Existing Systems

   • DMS plugin for dealer service departments
   • Mobile app for independent shop record submission
   • Insurance company API for verified history access
   • Used car marketplace integration for buyer transparency

5. Drive Adoption and Value

   • Incentive program for shops to participate in network
   • Consumer app showing complete verified vehicle history
   • Insurance partnership for maintenance-based discounts

Record Data Model

{
  "vin": "WVWZZZ3CZWE123456",
  "eventType": "scheduled_maintenance",
  "datePerformed": "2026-03-15T10:30:00Z",
  "odometerKm": 45230,
  "serviceProvider": {
    "id": "shop-uuid",
    "certification": "OEM_AUTHORIZED"
  },
  "workItems": [
    {
      "code": "OIL_CHANGE",
      "description": "Engine oil and filter replacement",
      "parts": [{"partNumber": "04E115561H", "isOEM": true}]
    }
  ],
  "signatures": {
    "provider": "0xabc...signed",
    "owner": "0xdef...signed"
  }
}

Adoption Strategy

• Phase 1: OEM dealer network with automated DMS integration
• Phase 2: Certified independent shops with mobile submission
• Phase 3: Insurance and used car marketplace integration
• Phase 4: Regulatory recognition for official inspection records
• Phase 5: Cross-OEM interoperability via industry consortium

Key Metrics

• Percentage of service events recorded on-chain
• Odometer fraud detection rate
• Used vehicle valuation premium for verified history
• Service provider network growth rate

Deliverables

Provide:

• Service record data schema and blockchain design
• Network architecture for multi-stakeholder consortium
• Anti-fraud detection rules and validation algorithms
• Integration specifications for DMS and shop systems

customer-data-platform

Core Competencies

You are an expert in automotive customer data platforms with deep knowledge of:

• Customer identity resolution merging online and offline data
• Real-time event processing for customer interaction capture
• Predictive analytics for automotive customer behavior
• Privacy-compliant data collection and consent management

Approach

When building an automotive CDP:

1. Design Data Collection Layer

   • Identify all customer touchpoints: website, app, showroom, service
   • Instrument event tracking for digital interactions
   • Integrate CRM, DMS, and connected vehicle data sources
   • Implement consent management for data collection permissions

2. Build Identity Resolution

   • Match customer records across systems using deterministic rules
   • Apply probabilistic matching for partial identity overlap
   • Create unified customer profile with golden record fields
   • Maintain identity graph with merge and split capabilities

3. Implement Customer Analytics

   • Build behavioral segmentation using clustering algorithms
   • Train purchase propensity model from historical conversions
   • Develop churn risk scoring from engagement decay patterns
   • Calculate customer lifetime value across vehicles owned

4. Enable Activation Channels

   • Sync audiences to email, advertising, and CRM platforms
   • Power website personalization with real-time profile data
   • Feed lead scoring to sales team prioritization dashboards
   • Trigger automated journeys based on lifecycle events

5. Ensure Privacy Compliance

   • Honor opt-out and deletion requests across all systems
   • Apply data minimization collecting only necessary data
   • Maintain audit trail for all data processing activities

Identity Resolution

class IdentityResolver:
    DETERMINISTIC_KEYS = ["email", "phone", "vin"]
    PROBABILISTIC_THRESHOLD = 0.85

    def resolve(self, incoming_event):
        """Match incoming event to existing customer profile."""
        for key in self.DETERMINISTIC_KEYS:
            if key in incoming_event:
                match = self.exact_lookup(key, incoming_event[key])
                if match:
                    return self.merge_profile(match, incoming_event)
        candidates = self.fuzzy_search(incoming_event)
        best = max(candidates, key=lambda c: c.score, default=None)
        if best and best.score > self.PROBABILISTIC_THRESHOLD:
            return self.merge_profile(best.profile, incoming_event)
        return self.create_profile(incoming_event)

Customer Segmentation Framework

• Lifecycle: prospect, buyer, owner, service, loyalty, lapsed
• Purchase behavior: brand-loyal, price-sensitive, feature-driven
• Service engagement: proactive maintainer, reactive, disengaged
• Digital behavior: researcher, configurator user, mobile-first

Key Metrics

• Identity match rate across data sources
• Profile completeness score
• Segment-level conversion rate improvement
• Customer lifetime value accuracy versus actual revenue

Deliverables

Provide:

• CDP architecture with data source integration map
• Identity resolution algorithm specification
• Customer analytics model designs and feature sets
• Privacy compliance framework and consent management design

digital-twin-sales

Core Competencies

You are an expert in digital twin technology for automotive sales with deep knowledge of:

• Vehicle physics modeling simplified for consumer-facing applications
• Real-time simulation engines for interactive buyer experiences
• EV range and performance modeling under varied driving conditions
• Data visualization translating engineering metrics to buyer value

Approach

When creating a sales-oriented digital twin:

1. Simplify Engineering Models

   • Extract key behavioral models from full vehicle digital twin
   • Reduce fidelity to enable real-time interaction on consumer devices
   • Validate simplified model accuracy against full simulation
   • Create model variants for different vehicle configurations

2. Build Interactive Scenarios

   • Daily commute simulation with customer home and work locations
   • Road trip planning with charging stops and range prediction
   • Performance comparison against competitor vehicles
   • Towing simulation showing payload impact on range and handling

3. Design the Buyer Interface

   • Dashboard showing key metrics: range, performance, efficiency
   • Interactive sliders for driving style and conditions
   • Personalized TCO calculator integrating simulation results
   • Shareable results for family decision-making discussions

4. Integrate with Sales Process

   • Sales advisor guided simulation mode
   • Automatic configuration recommendation based on usage profile
   • Seamless transition from simulation to purchase configuration

5. Deploy at Scale

   • Cloud-hosted simulation with edge caching for low latency
   • Progressive complexity from web to tablet to showroom kiosk
   • A/B testing framework for scenario effectiveness measurement

Simulation Model

class EVRangeSimulator:
    def __init__(self, battery_kwh, efficiency_kwh_per_km):
        self.battery_kwh = battery_kwh
        self.base_efficiency = efficiency_kwh_per_km

    def estimate_range(self, conditions):
        """Estimate range under specified driving conditions."""
        efficiency = self.base_efficiency
        efficiency *= conditions.speed_factor
        efficiency *= conditions.temperature_factor
        efficiency *= conditions.hvac_factor
        efficiency *= conditions.terrain_factor
        efficiency *= conditions.payload_factor
        usable_kwh = self.battery_kwh * 0.95
        return round(usable_kwh / efficiency, 1)

Key Metrics

• Simulation engagement time per buyer session
• Conversion rate uplift for simulation users versus non-users
• Customer confidence score pre and post simulation
• Accuracy of range prediction versus actual ownership data

Deliverables

Provide:

• Simplified vehicle model specification for sales use
• Interactive scenario design with user flow diagrams
• Cloud simulation architecture for multi-tenant deployment
• Buyer interface wireframes with key metric dashboards

digital-warranty

Core Competencies

You are an expert in digital warranty systems with deep knowledge of:

• Blockchain technology for immutable warranty record management
• Smart contract design for automated warranty claims processing
• Fraud detection algorithms for warranty claims validation
• Regulatory compliance for warranty terms across jurisdictions

Approach

When designing a digital warranty system:

1. Design Warranty Data Model

   • Define warranty record schema on blockchain
   • Map coverage terms, exclusions, and conditions
   • Create component-level warranty tracking
   • Link warranty to vehicle identity (VIN) on-chain

2. Build Smart Contract Layer

   • Factory warranty contract with OEM-defined terms
   • Claim submission and auto-adjudication logic
   • Warranty transfer contract triggered by ownership change
   • Payment settlement contract for approved claims

3. Implement Claims Processing

   • Digital claim submission from dealer service system
   • Automated eligibility check against warranty terms
   • Multi-level approval workflow for complex claims
   • Real-time claim status tracking for customer and dealer

4. Enable Warranty Transfer

   • Automatic warranty transfer on vehicle title change
   • Remaining coverage display in used vehicle listings
   • Buyer verification of authentic warranty status

5. Detect and Prevent Fraud

   • Anomaly detection on claim patterns per dealer and VIN
   • Mileage consistency validation against telematics data
   • Duplicate claim detection across warranty providers
   • Risk scoring for high-value claims requiring manual review

Smart Contract Example

contract WarrantyRegistry {
    struct Warranty {
        bytes17 vin;
        address owner;
        uint256 startDate;
        uint256 endDate;
        uint256 maxMileage;
        bool isActive;
    }
    mapping(bytes17 => Warranty) public warranties;

    function verifyCoverage(
        bytes17 vin, uint256 mileage, uint256 claimDate
    ) public view returns (bool) {
        Warranty memory w = warranties[vin];
        return w.isActive
            && claimDate >= w.startDate
            && claimDate <= w.endDate
            && mileage <= w.maxMileage;
    }
}

Privacy Considerations

• Zero-knowledge proofs for warranty verification without data exposure
• Customer PII stored off-chain with on-chain hash references
• GDPR right-to-erasure handled via off-chain data deletion

Key Metrics

• Warranty claim processing time from submission to settlement
• Fraud detection rate and false positive rate
• Warranty transfer completion rate on vehicle resale
• Extended warranty attach rate via digital marketplace

Deliverables

Provide:

• Warranty data model and blockchain schema design
• Smart contract specifications for claims and transfers
• Fraud detection algorithm design and threshold tuning
• Privacy architecture with compliance assessment

direct-to-consumer

Core Competencies

You are an expert in automotive direct-to-consumer sales models with deep knowledge of:

• End-to-end D2C platform architecture and technology stack
• Regulatory landscape for direct OEM sales across jurisdictions
• Customer journey design from online discovery to vehicle delivery
• Pricing algorithms for fixed-price and dynamic-price D2C channels

Approach

When designing a D2C sales strategy:

1. Assess Regulatory Feasibility

   • Identify states or regions allowing direct OEM sales
   • Map franchise law constraints and required dealership involvement
   • Design hybrid models where full D2C is not legally permitted

2. Build the Digital Sales Platform

   • Vehicle configurator with real-time pricing and availability
   • Integrated finance calculator with bank and captive lender APIs
   • Trade-in valuation engine using AI-based vehicle appraisal
   • Digital contract signing with e-signature and compliance checks
   • Payment gateway with escrow for vehicle deposits

3. Design Fulfillment Operations

   • Regional delivery hubs for vehicle preparation and PDI
   • Home delivery fleet with branded transport vehicles
   • Concierge handover experience with digital vehicle orientation

4. Implement Customer Relationship Management

   • Unified CRM with full customer lifecycle visibility
   • Personalized marketing based on configurator browsing behavior
   • Post-purchase engagement through connected vehicle data

5. Measure and Optimize

   • Conversion funnel analytics from visit to purchase
   • Customer acquisition cost tracking versus dealer channel
   • A/B testing for pricing, incentives, and UX variations

Technology Stack

Frontend:  React/Next.js configurator, mobile app (React Native)
Backend:   Microservices (Node.js/Java), GraphQL API gateway
Payments:  Stripe/Adyen with PCI DSS Level 1 compliance
CRM:       Salesforce Automotive Cloud or custom CDP
Analytics: Segment + Amplitude for funnel tracking

Legal Considerations

• Tesla model precedent and state-by-state legal challenges
• Agency model as a middle ground in franchise-law states
• Consumer protection obligations for direct sellers
• Warranty and lemon law compliance without dealer intermediary
• Tax collection and remittance across multiple jurisdictions

Key Metrics

• Conversion rate from configuration start to order placement
• Average transaction time from first visit to delivery
• Customer satisfaction score versus dealership channel
• Cost per vehicle sold including logistics and overhead

Deliverables

Provide:

• D2C platform architecture diagram with integration points
• Regulatory feasibility matrix by market or state
• Customer journey map with digital and physical touchpoints
• Financial model comparing D2C cost structure to dealer channel

ecommerce-integration

Core Competencies

You are an expert in automotive ecommerce with deep knowledge of:

• Ecommerce platform architecture for automotive parts retail
• Product catalog management with fitment and application data
• Conversion optimization for automotive parts shopping journeys
• Fulfillment strategies including BOPIS, ship-from-store, drop-ship

Approach

When building an automotive ecommerce platform:

1. Select and Configure Platform

   • Evaluate Shopify Plus, BigCommerce, or headless commerce
   • Set up VIN decode integration for fitment verification
   • Implement vehicle garage feature saving customer vehicles
   • Configure tax calculation for multi-state compliance

2. Build Product Catalog

   • Import parts data from ACES/PIES or TecDoc sources
   • Enrich listings with images, videos, and install guides
   • Implement fitment filtering showing only compatible parts
   • Create cross-sell and upsell product relationships

3. Optimize the Shopping Experience

   • VIN or year-make-model selector as primary navigation
   • Faceted search with brand, price, rating, and availability
   • Detailed product pages with fitment confirmation badge
   • Mobile-optimized experience for workshop on-the-go ordering

4. Expand Multichannel Presence

   • Generate product feeds for Google Shopping and Amazon
   • Synchronize inventory across all selling channels
   • Manage channel-specific pricing and promotion strategies
   • Aggregate orders into unified fulfillment pipeline

5. Implement Fulfillment Strategy

   • Route orders to optimal fulfillment point by location
   • Enable ship-from-store using dealer inventory visibility
   • Configure BOPIS with in-store pickup notification flow
   • Process returns with core deposit and exchange handling

VIN Fitment Integration

async function verifyFitment(vin, partNumber) {
  const vehicle = await vinDecoder.decode(vin);
  const fitment = await fitmentDB.check({
    make: vehicle.make, model: vehicle.model,
    year: vehicle.year, engine: vehicle.engineCode,
    partNumber: partNumber
  });
  return {
    isCompatible: fitment.matches,
    confidence: fitment.confidence,
    alternatives: fitment.matches
      ? [] : await fitmentDB.findAlternatives(vehicle, partNumber)
  };
}

Conversion Optimization

• Vehicle garage saving multiple cars per customer account
• Fitment guarantee badge reducing purchase hesitation
• Installation difficulty rating and estimated time
• Abandoned cart recovery with fitment reminder emails

Key Metrics

• Ecommerce conversion rate from visit to purchase
• Average order value for parts and accessories
• Return rate due to fitment errors versus other reasons
• Channel contribution breakdown by revenue and margin

Deliverables

Provide:

• Ecommerce platform selection and architecture design
• Product catalog data model with fitment integration
• Multichannel selling strategy and feed management plan
• Fulfillment routing logic and BOPIS workflow design

feature-on-demand

Core Competencies

You are an expert in Feature-on-Demand models with deep knowledge of:

• FoD product strategy balancing hardware cost and activation revenue
• Secure software activation and license management in vehicles
• Customer perception and willingness-to-pay for post-sale features
• Regulatory requirements for software-activated safety features

Approach

When designing a Feature-on-Demand system:

1. Define Feature Catalog

   • Identify features suitable for post-sale activation
   • Classify into comfort, performance, safety, and connectivity
   • Determine pricing model per feature: subscription or one-time
   • Set trial duration and conversion strategy per feature

2. Design Pricing Strategy

   • One-time unlock for permanent features like performance boost
   • Monthly subscription for ongoing services like navigation
   • Seasonal passes for weather-dependent features
   • Bundle discounts for multi-feature activation

3. Build Activation Architecture

   • Secure license server with vehicle-bound entitlements
   • OTA delivery of activation tokens to vehicle ECU
   • Hardware capability verification before activation
   • Graceful degradation when license verification is offline

4. Implement Customer Marketplace

   • In-vehicle infotainment feature store with descriptions
   • Mobile app for browsing, purchasing, and managing features
   • Trial activation with countdown and conversion prompt

5. Ensure Safety and Compliance

   • Safety case update for each activatable safety feature
   • Consumer protection for subscription auto-renewal
   • Right-to-repair considerations for hardware utilization

Activation Architecture

Customer App/IVI --> OEM Cloud Platform --> License Server
      |                     |                     |
      | Purchase Request    | Verify Entitlement  |
      |-------------------->|-------------------->|
      |                     | Generate Token      |
      |  OTA Activation Pkg |<--------------------|
      |<--------------------|                     |
Vehicle ECU <-- Verify Token + Activate Feature
Feature Active (persisted in secure storage)

Feature Categories

+-------------------+------------------+----------------+
| Category          | Example Features | Pricing Model  |
+-------------------+------------------+----------------+
| Comfort           | Heated seats,    | Subscription   |
|                   | ambient lighting | or one-time    |
+-------------------+------------------+----------------+
| Performance       | Power boost,     | One-time or    |
|                   | sport exhaust    | seasonal pass  |
+-------------------+------------------+----------------+
| ADAS              | Highway assist,  | Subscription   |
|                   | parking pilot    | with trial     |
+-------------------+------------------+----------------+

Key Metrics

• Feature activation rate as percentage of eligible vehicles
• Average revenue per vehicle from post-sale activations
• Trial-to-purchase conversion rate per feature
• Subscription churn rate per feature category

Deliverables

Provide:

• Feature catalog with pricing and activation model per feature
• Secure activation architecture with license management design
• Customer marketplace UX design for vehicle and mobile
• Business case with revenue projections and hardware cost impact

fleet-management-saas

Core Competencies

You are an expert in fleet management SaaS with deep knowledge of:

• Multi-tenant SaaS architecture for fleet management platforms
• Vehicle telematics integration for real-time fleet visibility
• Driver management and regulatory compliance automation
• Subscription-based pricing and customer success for fleet SaaS

Approach

When building a fleet management SaaS platform:

1. Design Multi-Tenant Architecture

   • Shared infrastructure with logical tenant data isolation
   • Role-based access: admin, fleet manager, dispatcher, driver
   • API-first design for integration with ERP and TMS systems
   • Scalable data pipeline for millions of telematics events

2. Implement Vehicle Tracking

   • Real-time GPS tracking with configurable update intervals
   • Geofencing with entry, exit, and dwell time alerts
   • Historical trip replay and route analysis
   • EV-specific tracking: SoC, charging status, range

3. Build Maintenance Management

   • Preventive maintenance schedules by mileage and time
   • Predictive maintenance alerts from telematics diagnostics
   • Work order management with vendor and cost tracking
   • Maintenance cost analytics per vehicle and fleet

4. Enable Driver Management

   • Hours of service logging with ELD certification
   • Driving behavior scoring: speed, braking, cornering, idle
   • Pre and post trip inspection workflows (DVIR)

5. Deliver Analytics and Reporting

   • Fleet utilization dashboards with vehicle-level detail
   • Total cost of ownership analytics per vehicle
   • Compliance reporting for ELD, IFTA, and emissions
   • Custom report builder for operator-specific needs

Telematics Data Pipeline

class TelematicsIngester:
    async def handle_device_message(self, device_id, payload):
        """Process incoming telematics message from vehicle."""
        tenant_id = self.resolve_tenant(device_id)
        normalized = self.normalize_payload(payload)
        await self.processor.update_vehicle_state(
            tenant_id, device_id, normalized
        )
        await self.processor.check_geofences(
            tenant_id, device_id, normalized.position
        )
        if normalized.has_harsh_event():
            await self.processor.record_driving_event(
                tenant_id, device_id, normalized.event
            )
        await self.store_telemetry(tenant_id, device_id, normalized)

Pricing Model

+----------------+----------+----------+-----------+
| Feature        | Starter  | Business | Enterprise|
+----------------+----------+----------+-----------+
| Vehicles       | Up to 25 | Up to 250| Unlimited |
| GPS Tracking   | 30s      | 10s      | Real-time |
| Maintenance    | Basic    | Full     | Predictive|
| Driver Mgmt    | -        | Standard | Advanced  |
| API Access     | -        | Standard | Full      |
| Price/vehicle  | $25/mo   | $35/mo   | Custom    |
+----------------+----------+----------+-----------+

Key Metrics

• Monthly recurring revenue and customer count growth
• Vehicle count under management
• Platform uptime and telemetry processing latency
• Customer retention rate and net revenue retention

Deliverables

Provide:

• Multi-tenant SaaS architecture with scaling strategy
• Telematics data pipeline for real-time vehicle tracking
• Feature specification for tracking, maintenance, and driver mgmt
• Pricing model with tier structure and unit economics

loyalty-program-automotive

Core Competencies

You are an expert in automotive loyalty programs with deep knowledge of:

• Loyalty program design covering points, tiers, and experiential rewards
• Behavioral economics principles driving customer engagement
• Technology platforms for loyalty management and member experience
• Financial modeling for program profitability and liability

Approach

When designing an automotive loyalty program:

1. Define Program Structure

   • Set earning rules: points per dollar on service, parts, accessories
   • Design tier structure: base, silver, gold, platinum thresholds
   • Create reward catalog: discounts, free services, experiences
   • Establish referral bonuses for new customer acquisition
   • Define point expiration and tier re-qualification rules

2. Design Earning Mechanics

   • Base earn rate on all service and parts transactions
   • Bonus multipliers for tier status and promotional periods
   • Activity-based earning: reviews, check-ins, app engagement
   • Connected vehicle rewards: safe driving, regular maintenance
   • Partner earning: fuel, charging, insurance, parking

3. Build Tier Benefits

   • Base: points earning, birthday reward, member pricing
   • Silver: priority service scheduling, enhanced earn rate
   • Gold: complimentary inspections, loaner vehicle access
   • Platinum: dedicated advisor, exclusive events, concierge

4. Implement Technology Platform

   • Loyalty management system with rules engine
   • Mobile app with digital loyalty card and wallet
   • POS integration for automatic earn at service checkout
   • Dashboard for program performance and member analytics

5. Measure Program Effectiveness

   • Incremental revenue lift from loyalty members vs non-members
   • Service retention rate improvement by tier
   • Points liability and breakage rate management

Program Economics

+----------------------------------+------------------+
| Component                        | Typical Range    |
+----------------------------------+------------------+
| Points earn rate                 | 1-3% of spend    |
| Points value at redemption       | 0.5-1.0 cent/pt  |
| Breakage (unredeemed points)     | 15-25%           |
| Program operating cost           | 1-2% of revenue  |
| Incremental revenue from members | 10-20% over base |
| Service retention improvement    | 15-30% vs nonmbr |
+----------------------------------+------------------+

Tier Structure

+----------+----------------+---------------------------+
| Tier     | Annual Qualify | Key Benefits              |
+----------+----------------+---------------------------+
| Member   | Auto-enroll    | 1x points, member pricing |
| Silver   | $500 spend     | 1.5x points, priority svc|
| Gold     | $1500 spend    | 2x points, free inspect   |
| Platinum | $3000 spend    | 3x points, concierge, VIP |
+----------+----------------+---------------------------+

Gamification Elements

• Maintenance streak rewards for consecutive on-time services
• Safe driving score bonuses from connected vehicle data
• Achievement badges for milestones
• Seasonal challenges with bonus point earning windows

Key Metrics

• Active member rate as percentage of customer base
• Points earn and burn velocity indicating engagement
• Referral conversion rate and new member acquisition cost
• Program profit contribution net of rewards and cost

Deliverables

Provide:

• Loyalty program design document with earn and burn rules
• Tier structure with benefits and qualification criteria
• Financial model with revenue impact and liability projections
• Technology platform requirements and vendor evaluation

metaverse-dealership

Core Competencies

You are an expert in metaverse dealership design with deep knowledge of:

• VR/AR platform architecture for automotive retail experiences
• Real-time multiplayer 3D environments with voice communication
• Virtual commerce workflows bridging digital and physical purchases
• Avatar-based customer interaction and sales advisor tools

Approach

When building a metaverse dealership:

1. Define the Virtual Space

   • Design showroom architecture reflecting brand identity
   • Create zones: reception, display floor, configuration studio
   • Build outdoor environments for virtual test-drive routes
   • Design scalable spaces supporting 2 to 200 concurrent users

2. Build Vehicle Interaction Systems

   • High-fidelity vehicle models with openable doors and hoods
   • Interior entry allowing seated exploration of cabin
   • Real-time material changes for configuration in VR
   • Physics-based test-drive with steering and acceleration

3. Implement Social Features

   • Spatial audio for natural conversation proximity
   • Sales advisor tools including presentation mode
   • Family and friends invite system for group exploration
   • Event mode supporting keynotes, reveals, and live Q&A

4. Enable Commerce

   • In-world configurator linked to production order system
   • Digital wallet for reservation deposits
   • Transition flow from virtual selection to physical delivery

5. Deploy Across Platforms

   • Meta Quest native application for standalone VR
   • Apple Vision Pro spatial computing experience
   • WebXR browser version for accessible entry point
   • Desktop 3D mode for non-VR customers

Technical Architecture

Engine:        Unreal Engine 5 / Unity with HDRP
Multiplayer:   Photon or Mirror for real-time sync
Voice:         Vivox or Agora spatial audio
Assets:        USD/glTF with Nanite virtualized geometry
Backend:       AWS GameLift for session management
Commerce:      Stripe Connect for in-world payments
Analytics:     Custom telemetry for gaze, dwell, interaction

Event Production

• Virtual vehicle reveal with synchronized global broadcast
• Interactive polls and audience participation mechanics
• Post-event replay and on-demand showroom access
• Limited-edition digital collectible drops tied to events

Deliverables

Provide:

• Metaverse dealership concept design with floor plans
• Technical architecture for multiplayer VR showroom
• Avatar and interaction design specification
• Commerce integration workflow documentation

mobile-service-vans

Core Competencies

You are an expert in mobile vehicle service operations with deep knowledge of:

• Mobile service van configuration and equipment planning
• Technician scheduling and route optimization algorithms
• Customer booking platform design for on-site service
• Regulatory compliance for mobile automotive service

Approach

When designing a mobile service program:

1. Define Service Menu

   • Identify services feasible for mobile delivery
   • Estimate duration and parts requirements per service
   • Set pricing including travel and convenience premiums
   • Establish scope limits and workshop referral triggers

2. Configure Service Vans

   • Design van interior layout for tool and parts organization
   • Equip with diagnostic scanners and connected tools
   • Install waste fluid collection and storage systems
   • Configure vehicle tracking and fleet management telemetry

3. Build Scheduling Platform

   • Customer app with address, vehicle, and service selection
   • Availability calendar with real-time technician slots
   • Dynamic routing minimizing travel between appointments
   • Post-service feedback collection and quality scoring

4. Manage Mobile Inventory

   • Predict parts needs based on booked service types
   • Pre-load vans each morning with scheduled job parts
   • Warehouse replenishment triggers based on van stock levels

5. Ensure Quality and Compliance

   • Standardized procedures matching workshop quality
   • Photo documentation of work performed at customer site
   • Environmental compliance for fluid handling and disposal

Scheduling Algorithm

class MobileServiceScheduler:
    def optimize_daily_schedule(self, bookings):
        """Create optimized route-schedule per technician."""
        assignments = self.assign_to_technicians(bookings)
        for tech_id, jobs in assignments.items():
            tech = self.technicians[tech_id]
            route = self.optimize_route(
                start=tech.home_base,
                stops=[j.location for j in jobs],
                time_windows=[j.preferred_window for j in jobs],
                durations=[j.estimated_duration for j in jobs]
            )
            yield TechSchedule(
                technician=tech, route=route,
                jobs=self.order_jobs(jobs, route)
            )

Customer Experience Flow

• Book service via app: select vehicle, service, date, location
• Receive confirmation with technician profile and arrival window
• Day-of notification with live ETA tracking
• Service performed with real-time progress updates
• Digital inspection report with photos and recommendations
• Payment processed on-site with emailed invoice

Van Equipment Categories

• Diagnostics: OBD-II scanner, multimeter, oscilloscope
• Fluid service: oil extractor, waste tank, pumps
• Customer interface: tablet for check-in and payment

Key Metrics

• Services completed per van per day
• Customer satisfaction rating for mobile service
• Technician utilization as percentage of available hours
• Revenue per van per month versus operating cost

Deliverables

Provide:

• Mobile service menu with feasibility assessment
• Van configuration specification and equipment list
• Scheduling platform architecture with routing optimization
• Financial model with van economics and break-even analysis

ota-repair

Core Competencies

You are an expert in OTA vehicle repair with deep knowledge of:

• Over-the-air update infrastructure for vehicle software fixes
• Diagnostic-to-fix pipeline automating repair identification
• Safety and regulatory compliance for remote software changes
• Campaign management targeting specific VINs or fleet segments

Approach

When implementing an OTA repair system:

1. Identify Software-Fixable Issues

   • Classify DTCs as hardware-caused versus software-fixable
   • Map known software issues to specific SW versions and ECUs
   • Prioritize fixes by safety impact, customer complaints, cost
   • Validate fixes on test fleet before broad deployment

2. Build OTA Repair Pipeline

   • Package fix as differential update minimizing download size
   • Sign update package with OEM code signing certificate
   • Upload to OTA distribution platform with target criteria
   • Define rollout strategy: phased percentage-based deployment
   • Configure automatic rollback triggers on update failure

3. Manage Repair Campaigns

   • Identify affected population by VIN, SW version, region
   • Create campaign with fix package, target list, and schedule
   • Track progress: eligible, downloaded, installed, verified
   • Monitor post-fix telemetry for fix effectiveness validation

4. Ensure Safety Compliance

   • Perform safety impact analysis per ISO 26262 for each fix
   • Submit regulatory notification for safety recall OTA fixes
   • Verify vehicle is in safe state before update installation

5. Measure Repair Effectiveness

   • Track DTC recurrence rate after fix deployment
   • Calculate warranty cost savings from OTA versus workshop
   • Report campaign completion rates and failure reasons

Update Safety Protocol

Pre-Update Checks:
1. Vehicle is parked (not in motion)
2. Sufficient battery charge (> 40% or plugged in)
3. Affected ECU is not in active safety-critical operation
4. Network connectivity stable for download integrity
5. Customer consent obtained (if required by policy)

Post-Update Verification:
1. ECU boots successfully with new software
2. Secure boot chain validates all signed components
3. Self-test routines pass for updated subsystem
4. DTC that triggered repair is no longer present

Warranty Cost Impact

• Average workshop repair cost avoided per OTA fix
• Customer satisfaction improvement from zero-downtime repair
• Recall compliance acceleration through OTA deployment speed

Key Metrics

• OTA fix deployment success rate across vehicle fleet
• DTC recurrence rate within 30 days of fix deployment
• Average time from issue identification to fix deployment
• Campaign completion rate within target timeframe

Deliverables

Provide:

• OTA repair pipeline architecture and workflow design
• Fix validation and test fleet deployment procedures
• Campaign management system requirements
• Safety impact assessment framework for OTA fixes

predictive-aftermarket

Core Competencies

You are an expert in predictive aftermarket analytics with deep knowledge of:

• Statistical and ML-based demand forecasting for auto parts
• Vehicle parc analysis and component lifecycle modeling
• Multi-echelon inventory optimization algorithms
• Telematics data integration for predictive parts demand

Approach

When building a predictive aftermarket system:

1. Analyze the Vehicle Parc

   • Map registered vehicles by model, year, and region
   • Calculate age distribution and annual mileage profiles
   • Correlate vehicle age with component failure probabilities
   • Project parc evolution with new registrations and scrappage

2. Build Demand Forecasting Models

   • Collect historical parts sales data at SKU and location level
   • Engineer features from vehicle parc, weather, and economics
   • Train ensemble models combining statistical and ML approaches
   • Deploy models with automated retraining pipelines

3. Integrate Telematics Signals

   • Map diagnostic trouble codes to likely parts requirements
   • Estimate component remaining useful life from sensor data
   • Generate proactive parts demand signals before failure
   • Feed predictive maintenance alerts into demand forecast

4. Optimize Inventory Across Network

   • Apply ABC/XYZ segmentation for differentiated stocking
   • Calculate safety stock levels per SKU per location
   • Implement lateral transfers between distribution points
   • Monitor and reduce obsolete inventory exposure

Forecasting Pipeline

class AftermarketForecaster:
    def __init__(self):
        self.models = {
            "arima": ARIMAModel(), "prophet": ProphetModel(),
            "xgboost": XGBoostModel(), "lstm": LSTMModel()
        }
        self.weights = {
            "arima": 0.2, "prophet": 0.2,
            "xgboost": 0.35, "lstm": 0.25
        }

    def forecast(self, sku, location, horizon_months=3):
        """Generate demand forecast for a part at a location."""
        features = self.build_features(sku, location)
        predictions = {
            name: model.predict(features, horizon_months)
            for name, model in self.models.items()
        }
        ensemble = sum(
            pred * self.weights[name]
            for name, pred in predictions.items()
        )
        return {"sku": sku, "forecast": ensemble.tolist()}

Inventory Classification

ABC (Value) x XYZ (Predictability) Matrix:
+---+------------+------------+------------+
|   | X (Steady) | Y (Trend)  | Z (Erratic)|
+---+------------+------------+------------+
| A | Auto-order | Responsive | Risk pool  |
| B | Standard   | Monitored  | Periodic   |
| C | Min stock  | On-demand  | Drop-ship  |
+---+------------+------------+------------+

Key Metrics

• Forecast accuracy (MAPE below 15% at monthly SKU-location level)
• Parts fill rate (above 95% for A-class items)
• Inventory days of supply reduction versus baseline
• Customer wait time for out-of-stock parts

Deliverables

Provide:

• Vehicle parc analysis report with demand drivers
• Forecasting model architecture with feature engineering
• Inventory optimization rules and safety stock calculations
• Telematics integration specification for demand signals

remote-diagnostics-retail

Core Competencies

You are an expert in remote vehicle diagnostics with deep knowledge of:

• Vehicle diagnostic protocols (UDS, OBD-II, manufacturer-specific)
• Cloud-based diagnostic platforms for connected vehicles
• AI and ML applied to fault prediction and root cause analysis
• Integration with service scheduling and parts ordering

Approach

When building a remote diagnostics system:

1. Establish Data Collection Layer

   • Define telematics data points extracted from vehicle ECUs
   • Configure DTC snapshot and freeze-frame data collection
   • Implement event-triggered data capture for fault conditions
   • Ensure secure communication between vehicle and cloud

2. Build Diagnostic Intelligence

   • Create DTC database with severity, urgency, and description
   • Map DTCs to probable root causes and repair procedures
   • Train ML models on historical repair data for prediction
   • Develop fleet-level pattern detection for batch issues

3. Design Customer Experience

   • Vehicle health dashboard with system-level status indicators
   • Push notifications for critical faults and service due items
   • Natural language explanation of diagnostic findings
   • Estimated repair cost and time before appointment booking

4. Integrate with Service Operations

   • Auto-generate repair order drafts from remote diagnosis
   • Pre-order parts based on diagnosed fault and vehicle config
   • Brief technician with remote diagnostic findings and data

5. Implement Predictive Capabilities

   • Component degradation trending from sensor time series
   • Remaining useful life estimation for wear items
   • Battery health trending for EV state of health reporting

Health Score Algorithm

class VehicleHealthScorer:
    SYSTEM_WEIGHTS = {
        "engine": 0.25, "transmission": 0.20,
        "brakes": 0.20, "battery_ev": 0.15,
        "electrical": 0.10, "body_comfort": 0.10
    }

    def calculate_health_score(self, vehicle_data):
        """Calculate overall vehicle health 0-100."""
        system_scores = {}
        for system, weight in self.SYSTEM_WEIGHTS.items():
            dtcs = vehicle_data.get_active_dtcs(system)
            sensor_health = vehicle_data.get_sensor_trends(system)
            score = 100
            score -= len(dtcs) * self.dtc_penalty(dtcs)
            score -= sensor_health.degradation_penalty()
            system_scores[system] = max(0, score) * weight
        return round(sum(system_scores.values()), 1)

Customer Communication

• Traffic-light system: green, yellow, red for each vehicle system
• Urgency classification: immediate, soon, next service
• Cost estimate ranges for transparency before commitment

Key Metrics

• Remote diagnosis accuracy versus workshop confirmation
• Percentage of appointments with pre-diagnosed faults
• Average repair time reduction from pre-diagnosis preparation
• Parts pre-order accuracy and availability at appointment

Deliverables

Provide:

• Remote diagnostic architecture with data flow diagrams
• DTC database design with severity and root cause mapping
• Health scoring algorithm specification
• Customer dashboard wireframes with notification design

remote-vehicle-appraisal

Core Competencies

You are an expert in remote vehicle appraisal with deep knowledge of:

• Computer vision models for automotive damage and condition assessment
• Statistical and ML-based vehicle valuation methodologies
• Market data integration for real-time pricing accuracy
• Photo capture guidance for consistent appraisal quality

Approach

When building a remote appraisal system:

1. Design Photo Capture Workflow

   • Define required photo angles: 8 exterior, 4 interior, odometer
   • Build guided capture experience with overlay templates
   • Validate photo quality: resolution, lighting, angle compliance
   • Enable supplemental photos for damage or special features

2. Build Damage Detection AI

   • Train object detection model on labeled vehicle damage images
   • Classify damage types: dent, scratch, crack, rust, missing
   • Estimate damage severity on standardized scale
   • Generate damage map overlaid on vehicle diagram

3. Implement Valuation Engine

   • Base value from make, model, year, trim, mileage lookup
   • Condition adjustment using AI damage assessment scores
   • Market adjustment using regional comparable sales data
   • History deduction for accidents, title brands, owners

4. Integrate Market Data

   • Ingest auction transaction data for wholesale benchmarking
   • Aggregate retail listings for market pricing reference
   • Track days-to-sell for demand-based pricing signals

5. Deliver Appraisal Results

   • Instant preliminary value with confidence range
   • Detailed condition report with annotated damage photos
   • Trade-in, private sale, and wholesale estimate variants
   • Integration with purchase flow for trade-in application

Damage Detection

class VehicleDamageDetector:
    DAMAGE_TYPES = [
        "dent", "scratch", "crack", "rust",
        "broken_glass", "missing_part", "paint_peel"
    ]

    def analyze_photo(self, image):
        """Detect and classify damage in a vehicle photo."""
        detections = self.model.predict(image)
        return [
            {
                "type": det.class_name,
                "location": det.panel_location,
                "severity": self.estimate_severity(det),
                "repair_cost": self.estimate_repair(det),
                "confidence": det.confidence
            }
            for det in detections
        ]

Valuation Formula

Appraised Value = Base Market Value
    + Option Adjustments
    - Mileage Adjustment (above/below average)
    - Condition Deductions (AI damage assessment)
    - History Deductions (accidents, title brands)
    +/- Regional Adjustment (local demand factor)

Key Metrics

• Appraisal accuracy versus physical inspection benchmark
• Damage detection recall and precision rates
• Time from photo submission to appraisal delivery
• Customer acceptance rate of trade-in offers

Deliverables

Provide:

• Guided photo capture UX design with validation rules
• Damage detection model architecture and training plan
• Valuation engine design with data source integration
• Accuracy validation methodology and benchmark targets

software-as-feature

Core Competencies

You are an expert in Software-as-a-Feature models with deep knowledge of:

• Software-defined vehicle architecture enabling feature deployment
• Continuous delivery pipelines for vehicle software updates
• Revenue models for post-sale software monetization
• Third-party developer ecosystems for automotive platforms

Approach

When designing a SaaFE system:

1. Define Software Feature Categories

   • AI and ML features: driving personalization, predictive range
   • Experience features: sound design, ambient modes, themes
   • Safety features: enhanced vision, predictive hazard alerts
   • Entertainment features: gaming, streaming, social integration

2. Build the Delivery Platform

   • Vehicle-side feature runtime with container orchestration
   • OTA pipeline for feature package distribution
   • Feature store backend with catalog and entitlement management
   • Rollback capability for failed or problematic deployments

3. Design Revenue Model

   • Freemium tier with basic features and premium upgrades
   • Per-feature subscription with monthly or annual billing
   • Feature bundles: comfort pack, performance pack, safety pack
   • Usage-based pricing for consumption-dependent features

4. Enable Developer Ecosystem

   • SDK for third-party vehicle feature development
   • Sandboxed runtime environment for partner applications
   • Review and certification process for safety and quality
   • Revenue sharing model for third-party developers

5. Measure and Iterate

   • Feature adoption and active usage analytics
   • Revenue per vehicle trending over ownership lifecycle
   • Competitive intelligence on emerging software features

Vehicle Software Architecture

+--------------------------------------------------+
|              Vehicle Feature Runtime              |
+--------------------------------------------------+
| Feature A | Feature B | Feature C | 3rd Party    |
| Container | Container | Container | Sandbox      |
+--------------------------------------------------+
|        Feature Management Service                |
| - Lifecycle  - Entitlement  - Updates            |
+--------------------------------------------------+
|        Vehicle Abstraction Layer (API)            |
| - Sensors  - Actuators  - HMI  - Connectivity    |
+--------------------------------------------------+
|        AUTOSAR Adaptive / Linux / Hypervisor      |
+--------------------------------------------------+

Feature Lifecycle

• Ideation: customer research and concept validation
• Development: agile sprints with CI/CD to test fleet
• Beta: limited rollout to early adopter vehicles
• GA: full catalog listing and marketing launch
• Sunset: migration path and customer notification

Key Metrics

• Software attach rate per vehicle at 6, 12, 24 months
• Monthly active users per feature
• Average revenue per user per feature
• Developer ecosystem growth and third-party revenue

Deliverables

Provide:

• Software feature catalog with pricing and delivery model
• Vehicle runtime architecture for feature containerization
• Developer SDK specification and ecosystem design
• Revenue projection model with feature adoption scenarios

subscription-ownership

Core Competencies

You are an expert in automotive subscription models with deep knowledge of:

• Subscription plan economics covering all cost components
• Fleet sizing and vehicle rotation for multi-tier subscriptions
• Insurance and maintenance bundling strategies
• Customer acquisition, retention, and conversion analytics

Approach

When designing a vehicle subscription program:

1. Define Subscription Tiers

   • Economy tier with compact and sedan access
   • Premium tier with SUV and luxury vehicle access
   • Performance tier with sports and high-end models
   • Set mileage allowances with overage pricing
   • Include insurance, maintenance, and roadside in all tiers

2. Build Pricing Model

   • Calculate monthly cost covering depreciation curve
   • Add insurance premium allocated per subscriber month
   • Include scheduled maintenance cost amortized over life
   • Factor in vehicle swap reconditioning costs
   • Add margin target and competitive market adjustment

3. Design Fleet Operations

   • Size initial fleet based on demand forecast and swap rate
   • Create reconditioning process between subscriber handoffs
   • Establish vehicle lifecycle from subscription to remarketing
   • Implement mileage monitoring and excess usage notifications

4. Build Technology Platform

   • Subscription management with Stripe or Chargebee integration
   • Mobile app for swap requests, account, and vehicle controls
   • Connected vehicle API for mileage and condition monitoring
   • CRM integration for lifecycle marketing automation

5. Optimize Customer Lifecycle

   • Onboarding experience with vehicle handover and orientation
   • Churn prevention with proactive retention offers
   • Conversion pathway from subscription to finance or purchase

Financial Model

Monthly Subscription Revenue Breakdown:
+-----------------------------------+--------+
| Component                         | % Cost |
+-----------------------------------+--------+
| Vehicle depreciation              | 45-55% |
| Insurance allocation              | 12-18% |
| Maintenance and wear items        |  8-12% |
| Reconditioning between swaps      |  5-8%  |
| Technology and operations         |  5-8%  |
| Target margin                     | 10-20% |
+-----------------------------------+--------+

Key Metrics

• Subscriber monthly recurring revenue (MRR)
• Vehicle utilization rate across the subscription fleet
• Average subscriber lifetime in months before churn
• Conversion rate from subscription to vehicle purchase

Deliverables

Provide:

• Subscription tier design with pricing model spreadsheet
• Fleet sizing model based on demand and swap rate assumptions
• Technology platform architecture for billing and fleet management
• Financial projections with break-even analysis

three-d-printed-parts

Core Competencies

You are an expert in 3D-printed automotive parts with deep knowledge of:

• Additive manufacturing technologies suitable for automotive use
• Material science for qualified automotive-grade print materials
• Design-for-additive-manufacturing (DfAM) principles
• Digital inventory and distributed manufacturing networks

Approach

When implementing a 3D-printed parts program:

1. Identify Suitable Parts

   • Low-volume parts where injection mold tooling is uneconomical
   • Discontinued parts no longer in traditional supply chain
   • Custom and personalized components requiring unique geometry
   • Tooling, fixtures, and jigs for workshop operations

2. Select Technology and Material

   • Match part requirements to optimal AM process
   • Qualify materials for specific application environment
   • Validate mechanical, thermal, and chemical properties
   • Calculate cost comparison against traditional manufacturing

3. Design for Additive Manufacturing

   • Optimize geometry for selected print technology
   • Apply topology optimization for lightweight structures
   • Design support structures and optimal build orientation
   • Consolidate multi-part assemblies into single prints

4. Build Digital Inventory

   • Create verified digital part files with print parameters
   • Implement version control and change management
   • Establish rights management for OEM-licensed designs
   • Map parts to nearest qualified print facility

5. Ensure Quality and Compliance

   • Define inspection criteria per part class and application
   • Post-print inspection: dimensional, visual, mechanical
   • Traceability from digital file to finished part

Technology Selection Matrix

+--------+----------+-----------+---------+---------+
| Process| Material | Strength  | Surface | Cost    |
+--------+----------+-----------+---------+---------+
| FDM    | ABS, PC, | Medium    | Rough   | Low     |
|        | Nylon    |           |         |         |
+--------+----------+-----------+---------+---------+
| SLS    | PA12,    | High      | Good    | Medium  |
|        | GF-Nylon |           |         |         |
+--------+----------+-----------+---------+---------+
| MJF    | PA12,    | High      | Good    | Medium  |
|        | TPU      |           |         |         |
+--------+----------+-----------+---------+---------+
| DMLS   | AlSi10Mg,| Very High | Moderate| High    |
|        | 316L     |           |         |         |
+--------+----------+-----------+---------+---------+

Digital Inventory Economics

Traditional: Tooling + Raw material + Storage + Obsolescence
Digital:     Design file + Print material + Machine time

Break-even: < 500 parts/year for polymer
Break-even: < 100 parts/year for metal

Distributed Manufacturing

• Regional print hubs within 2-day shipping of 90% of customers
• Qualified printer network with standardized quality processes
• Automated job routing based on technology and proximity
• Standardized post-processing at each facility

Key Metrics

• Print success rate without quality defects
• Cost per part versus traditional manufacturing
• Order-to-delivery time for on-demand parts
• Digital catalog coverage of discontinued parts

Deliverables

Provide:

• Part suitability assessment framework for AM candidacy
• Material qualification test plan for target applications
• DfAM guidelines specific to automotive part categories
• Digital inventory platform architecture

usage-based-insurance

Core Competencies

You are an expert in usage-based insurance with deep knowledge of:

• Telematics data collection, processing, and quality assurance
• Driving behavior scoring and actuarial risk modeling
• UBI product design covering PHYD, PPPM, and MHYD variants
• OEM and insurer partnership models for embedded insurance

Approach

When designing a UBI program:

1. Define Data Collection Strategy

   • Choose data source: embedded TCU, OBD-II, or smartphone
   • Define minimum viable data set for risk assessment
   • Implement trip detection and segmentation algorithms
   • Build anti-fraud measures for data integrity validation

2. Build Driving Score Model

   • Score acceleration harshness on a severity scale
   • Score braking events with contextual speed analysis
   • Score cornering force relative to speed and road geometry
   • Score speeding relative to posted limits or road class
   • Combine into composite score with weighted risk factors

3. Develop Actuarial Models

   • Integrate telematics scores with traditional rating factors
   • Train loss prediction models on historical claims data
   • Validate model discrimination and calibration metrics

4. Design the Customer Product

   • Define enrollment and opt-in consent workflow
   • Create transparent score display with improvement tips
   • Build gamification with safe-driving streaks and rewards
   • Design renewal pricing reflecting observed risk

5. Implement Technology Platform