AI for Automotive Software Development

Pricing model trained on transaction history, days-on-lot data, and market-wide price signals to generate a recommended retail price per vehicle per day. Days-on-lot pricing curves that reduce the recommended price at configurable intervals as a vehicle ages in stock, based on the historical relationship between days on lot and final selling price in your own transaction data. Competitor rate monitoring via marketplace feed integration, pulling live asking prices for comparable vehicles by make, model, trim, and mileage band. Demand signal integration that weights recommendations toward faster-selling price points when comparable stock in the market is low. Automated price recommendation output sent to the inventory management or DMS system as a suggested action, not a forced update, preserving manager review before any price change is applied. The pricing layer that replaces a daily competitor check with a model built on your own sales history.

OBD-II fault pattern analysis that identifies combinations of diagnostic codes and sensor readings associated with component failure in your vehicle population, rather than relying on single fault code triggers that arrive after damage has begun. Mileage and engine hour threshold models calibrated against your own maintenance history to identify the point at which service intervention is most cost-effective for each vehicle class and drivetrain type. Failure probability scoring per vehicle updated on a configurable schedule -- daily for high-utilisation fleet assets, weekly for lower-mileage vehicles -- with a ranked list of vehicles approaching intervention thresholds. Automated service scheduling trigger that creates a workshop booking recommendation in the fleet or DMS platform when a vehicle crosses a configurable probability threshold, without requiring a fleet manager to review the raw telematics data. The predictive layer that converts continuous telemetry into a maintenance decision before a breakdown occurs.

Historical sales velocity model built from your transaction records, capturing rate of sale by make, model, trim level, fuel type, and mileage band over a configurable look-back window. Seasonality pattern extraction that identifies recurring demand shifts -- convertibles in spring, SUVs ahead of winter, diesel commercial vehicles tied to fleet renewal cycles -- and applies those patterns to forward stock depth recommendations. Regional demand variation analysis for dealer groups operating across multiple locations, identifying which vehicle classes turn faster at which sites so stock can be allocated accordingly. Stock depth recommendations by vehicle class calculated from the combination of current stock level, current sales velocity, and supplier lead time, flagging classes where stock is likely to run short before the next acquisition cycle. The forecasting layer that gives the used car buyer or fleet procurement team a data-backed view of what to acquire rather than a conversation with the sales manager about what feels short.

Surface defect detection model trained to identify and locate paint chips, scratches, dents, scuffs, and glass damage from standard photos taken on a smartphone or fixed inspection camera, without requiring specialist imaging equipment. Grading output aligned to the condition standards your operation uses -- BVRLA fair wear and tear guidelines, trade guide grading categories, or a custom scale -- so the model output maps directly to the existing appraisal language used by your team and customers. Automated condition report generation from the assessed photos, producing a structured document with defect locations, severity ratings, and photo evidence that can be attached to the vehicle record in the DMS or fleet platform. Damage cost estimation using configurable repair cost data by defect type and severity, providing an indicative repair cost at the point of assessment rather than requiring a bodyshop quote before a decision is made. The condition layer that makes vehicle appraisal faster, more consistent, and less dependent on the experience of the individual doing the inspection.

Recommendation model built from browsing and enquiry history on the marketplace or dealership website, identifying the vehicle attributes -- body style, fuel type, price band, mileage range -- that each user has shown interest in and surfacing stock that matches those preferences more closely than a generic sorted list. Re-engagement model for customers with expiring finance terms, identifying accounts approaching the end of a PCP or lease agreement and generating outbound communication with relevant stock matches based on the vehicle they currently drive and their historical browsing behaviour. Segment-level recommendation configuration for operators who want to suppress or promote certain vehicle classes to specific customer segments -- for example, promoting electric vehicles to customers in areas with charging infrastructure or surfacing commercial vehicles to customers identified as business users. Integration with the CRM or DMS to deliver recommendation outputs as a task or communication trigger for the sales team, rather than a purely automated email flow that operates without human review. The recommendation layer that makes a vehicle marketplace or dealer website behave like a sales team that knows what each customer is looking for.

OCR pipeline for service records, vehicle logbooks, and V5C registration documents that extracts structured data -- previous keeper count, date of first registration, recorded mileage at each service, and service centre details -- from scanned or photographed documents without manual transcription. Structured data extraction from unstructured dealer inventory feeds, normalising inconsistently formatted vehicle data from multiple source systems into a clean, consistent record with validated fields. VIN validation and specification matching that decodes a VIN against manufacturer databases to confirm make, model, engine, transmission, and factory-fitted options, flagging discrepancies between the stated specification and the decoded data. Integration into the intake workflow of the DMS, fleet platform, or marketplace so extracted data populates the vehicle record directly rather than being delivered as a separate output requiring copy-paste. The extraction layer that removes the manual data entry step from vehicle intake and makes historical document data searchable and structured.