| 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 Engagement
- loyalty-program-automotive
Customization
- accessory-personalization
- three-d-printed-parts
Digital Commerce
Digital Retail
- digital-twin-sales
- metaverse-dealership
- virtual-showroom
Fleet Services
Insurance Finance
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
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:
-
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
-
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
-
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
-
Enable Community and Social
- Customer design gallery with sharing capabilities
- Design challenge contests with OEM prizes
- User reviews and real-world installation photos
-
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:
-
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
-
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
-
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
-
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
-
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:
-
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
-
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
-
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
-
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:
-
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
-
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
-
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
-
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:
-
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
-
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
-
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
-
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
-
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:
-
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
-
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
-
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
-
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
-
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:
-
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
-
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
-
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
-
Integrate with Sales Process
- Sales advisor guided simulation mode
- Automatic configuration recommendation based on usage profile
- Seamless transition from simulation to purchase configuration
-
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:
-
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
-
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
-
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
-
Enable Warranty Transfer
- Automatic warranty transfer on vehicle title change
- Remaining coverage display in used vehicle listings
- Buyer verification of authentic warranty status
-
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:
-
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
-
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
-
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
-
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
-
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:
-
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
-
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
-
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
-
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
-
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:
-
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
-
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
-
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
-
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
-
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:
-
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
-
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
-
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
-
Enable Driver Management
- Hours of service logging with ELD certification
- Driving behavior scoring: speed, braking, cornering, idle
- Pre and post trip inspection workflows (DVIR)
-
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:
-
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
-
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
-
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
-
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
-
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:
-
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
-
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
-
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
-
Enable Commerce
- In-world configurator linked to production order system
- Digital wallet for reservation deposits
- Transition flow from virtual selection to physical delivery
-
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:
-
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
-
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
-
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
-
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
-
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:
-
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
-
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
-
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
-
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
-
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:
-
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
-
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
-
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
-
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:
-
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
-
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
-
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
-
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
-
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:
-
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
-
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
-
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
-
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
-
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:
-
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
-
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
-
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
-
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
-
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:
-
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
-
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
-
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
-
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
-
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:
-
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
-
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
-
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
-
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
-
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:
-
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
-
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
-
Develop Actuarial Models
- Integrate telematics scores with traditional rating factors
- Train loss prediction models on historical claims data
- Validate model discrimination and calibration metrics
-
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
-
Implement Technology Platform
