Masabi, the global leader in enterprise-grade SaaS fare collection for public transit, today announced the launch of Masabi AI Labs, an AI Incubator focused on developing tools that deliver meaningful, practical value for public transit agencies and their customers. AI Labs formalizes more than a year of work, including research and discovery sessions with transit agencies across North America and Europe, and is now working with four agencies along with technology and payment companies to test where AI can make a measurable difference.
Rather than starting with the technology, Masabi is focusing on real operational and customer challenges, developing and testing ideas collaboratively with agencies and partners before considering wider deployment. The philosophy behind the program is simple – Explore boldly. Test with the industry. Deploy thoughtfully. Initial work has focused on developing AI use cases and tools in six specific areas:
- Making transit data easier to interrogate and act on
- Improving customer service
- Strengthening revenue protection
- Augmenting repetitive operational and back-office processes
- Helping teams manage increasingly sophisticated fare systems
- Exploring how passengers could discover and purchase public transit through emerging AI interfaces
First Tool Developed with Greater Dayton Regional Transit Authority
One of the most advanced areas is self-service data analytics. With transit agencies generating significant volumes of information across ticketing, fare validation, ridership, payments and revenue, extracting useful answers can still require specialist analysts, reporting tools and manual processes. Masabi customer, Greater Dayton Regional Transit Authority (RTA), has been testing an AI-powered natural language data tool developed through Masabi AI Labs. The technology enables technical and non-technical users to ask questions about ridership and fare-validation data and receive comprehensive analytics and insights.
RTA has used the tool to explore route-level validation data, transfer patterns supporting its ongoing system redesign and fare-media usage, demonstrating how AI could help agency teams move more quickly from a question to actionable insight while retaining transparency into the underlying data.
Revenue Protection and Customer Service
For revenue protection, Masabi AI Labs has worked with a large transit agency in the US and a machine-learning partner to analyze ticket purchase and validation data. The work examined distinct sources of revenue loss and assessed whether machine learning could surface additional patterns. The longer-term aim is to give agencies clearer insight into where revenue is being lost, so investigation and enforcement resources can be focused where they will have the greatest impact.
“At Masabi we have never been about technology for technology’s sake. The company was founded with the ethos of solving everyday hassle for people using technology and our approach to AI has been the same. The question we are interested in is where it can genuinely make public transit better,” said Brian Zanghi, CEO at Masabi. “That means starting with the problems our customers and their passengers experience. By working directly with agencies and technology partners, we can explore ideas against real-world requirements, understand where they add value and establish the safeguards needed to use them responsibly.”