Solution

Quantiphi identified pivotal factors impacting transaction events by leveraging the client’s data. Through meticulous data transformations and engineering, Quantiphi crafted a machine learning model using Vertex AI, predicting the probability of drivers refueling across various timeframes
A recommendation model was developed by Quantiphi to propose the closest viable fuel station for vehicles anticipated to engage in a transaction
A robust MLOps pipeline was swiftly developed by Quantiphi using Vertex AI, meeting accelerated timelines
Additionally, insights into data and model performance, along with output visualization, were provided through Looker Studio

Results

Quantiphi developed an ML model with integrated MLOps pipelines, achieving a F1 Score of 70-80% in predicting transaction events
The Car IQ team was equipped with Looker Studio Dashboards by Quantiphi, facilitating a comprehensive assessment of model results

“Car IQ benefited a lot from Quantiphi’s expertise in delivering Gen AI and Predictive Analytics solutions. They helped accelerate our timelines to market and understand industry best practices. The Quantiphi team was highly proficient, flexible and professional which made working with them a seamless experience for us.”
Khalid El-Awady, Chief Product Officer, Car IQ