Data for AI

Power AI models with structured, historical, and time-based data to build reliable, real-world vehicle intelligence.

Sign UpSchedule a Meeting

The Challenge

Clients are looking for historical automotive data to power AI models but often lack reliable, structured datasets. Incomplete or inconsistent data limits model accuracy and leads to unreliable outputs. This creates challenges in building scalable AI-driven automotive solutions.

How It Works

From raw market data to AI-ready datasets

  • Access automotive data

    Collect historical pricing, listing, and activity data using the API

  • Prepare data for AI models

    Structure, normalize, and organize VIN-level and market data for training and validation

  • Apply AI model development

    Train, validate, and update AI models using time-based automotive signals

Key Capabilities

Core capabilities for AI data preparation and usage

Historical data aggregation

Provide large-scale historical pricing and listing datasets for model training

Time series data structuring

Organize data to support forecasting and trend-based models

VIN level data consistency

Ensure clean, structured inputs for feature engineering

Continuous model data updates

Enable retraining using refreshed market data

Data Signals

Key indicators used to support AI model training

  • Historical market records

    Large-scale datasets for training predictive models

  • Pricing and activity timelines

    Time-based signals for forecasting and classification

  • VIN level feature consistency

    Structured attributes for reliable model inputs

  • Market movement patterns

    Behavioral trends across pricing and demand

  • Dataset refresh cycles

    Ongoing updates to keep models current

Who Benefits

Teams building AI-driven automotive solutions

  • Investors

    Analyze market trends and asset performance using AI models

  • Software providers

    Build AI-pBwered automotive platforms and applications

  • Warranty providers

    Support risk modeling and coverage decisions using AI

  • Startups

    Develop predictive and analytics-driven solutions

Practical Example

A software startup trains a vehicle valuation model using historical pricing and activity data across multiple regions. By incorporating time-based signals, the model learns how pricing, demand, and vehicle attributes interact over time. As new data becomes available, the model is retrained to improve prediction accuracy and reliability.

Ready to build?

Access automotive market data and APIs with a free developer account. No credit card required.