Accelerating Vehicle Transaction Audits with AI-Powered Automation
The Challenge
The technology and software company manually audited over 200,000 vehicle transactions each year to ensure compliance with state registration and titling requirements. The process was labor-intensive and error-prone due to inconsistent document formats across states, poor scan quality, and the presence of handwritten information. These factors made automation difficult and slowed down audit workflows.
The Solution
A scalable document management pipeline was implemented on Google Cloud Platform, leveraging tools such as Vertex AI, BigQuery, and Datastore. AutoML Vision models were used to classify transaction documents, while Document AI—utilizing both Google-trained processors and custom form parsers—enabled accurate extraction and verification of relevant data. This automated approach significantly streamlined the audit process, improving speed and accuracy.
The Result
Accelerated Document Auditing: ML model reduced labor, accelerating document auditing process.
Scalability: Highly scalable architecture enabled the client to grow and meet evolving future needs.
Future Expansion: Repeatable architecture facilitated API developments and tailored experiences.
Efficiency and Growth: Increased efficiency and scalability support the client’s growth and operational excellence.
Accelerating Vehicle Transaction Audits with AI-Powered Automation
The Challenge
The technology and software company manually audited over 200,000 vehicle transactions each year to ensure compliance with state registration and titling requirements. The process was labor-intensive and error-prone due to inconsistent document formats across states, poor scan quality, and the presence of handwritten information. These factors made automation difficult and slowed down audit workflows.
The Solution
A scalable document management pipeline was implemented on Google Cloud Platform, leveraging tools such as Vertex AI, BigQuery, and Datastore. AutoML Vision models were used to classify transaction documents, while Document AI—utilizing both Google-trained processors and custom form parsers—enabled accurate extraction and verification of relevant data. This automated approach significantly streamlined the audit process, improving speed and accuracy.
The Result
Accelerated Document Auditing: ML model reduced labor, accelerating document auditing process.
Scalability: Highly scalable architecture enabled the client to grow and meet evolving future needs.
Future Expansion: Repeatable architecture facilitated API developments and tailored experiences.
Efficiency and Growth: Increased efficiency and scalability support the client’s growth and operational excellence.
