Dynamic Fares

We developed a personalized mobile payment solution for one of our clients in the public transit space that allows patrons to use their smartphones to pay for fares. This solution is more convenient than using smart cards, as patrons no longer need to carry a physical card with them. It also offers a variety of benefits, such as personalized fares and incentives.

Our solution uses a big data platform with deep learning to track patron travel patterns and preferences. This data is used to personalize fares, so that patrons only pay for the rides they actually take. Patrons can also earn incentives, such as discounts or rewards, for using our solution.

We believe that our solution is a valuable asset for public transportation agencies. It can help to improve the efficiency and affordability of public transportation, while also making it more convenient for patrons

Predictive Transit Device Maintenance

We worked with our client in the public transit domain to develop a solution that uses machine learning (ML) to predict device failures and initiate maintenance activities well in advance. This solution has helped our client to improve the reliability of their transit device infrastructure in a number of ways:

• Increased revenue: By preventing unplanned downtime, our client has been able to increase revenue by an average of 5%.
• Enhanced device performance: The ML-powered solution has helped to improve the performance of our client’s transit devices, resulting in fewer breakdowns and a better overall customer experience.
• Improved budget planning: The ML solution has given our client a better understanding of their device maintenance needs, which has helped them to improve their budget planning and save money.

We are excited to continue working with our clients to develop innovative solutions that can help them to improve their operations and better serve their customers.

Insurance Risk Scoring Engine

Our machine learning solution can instantly assess the risk level of customers applying for insurance policies. It does this by analyzing a massive dataset of historical insurance data, including customer demographics, financial information, and claims history.

The solution is able to make nearly accurate predictions about the risk level associated with a customer, which allows insurance companies to make more informed decisions about whether to approve a policy. This can save insurance companies time and money, and it also provides a better customer experience, as customers can apply for policies online and receive instant approvals.