Masabi AI Labs

Exploring AI for transit humans

Masabi's industry-leading AI incubator explores how machine learning and AI can improve fare payment operations — from anomaly detection to demand forecasting and operational optimisation.

Spotlight on self-service analytics

Greater Dayton RTA — Masabi's AI-powered natural language data tool

Greater Dayton RTA has been testing Masabi’s AI-powered natural language data tool, part of the AI Incubator’s self-service analytics workstream. The tool lets non-technical and technical users alike query ridership and fare-validation data directly — asking questions in plain English and getting back charts, breakdowns, the underlying data, and the SQL behind it, instantly, with ridership, revenue, fare policy, and rider segmentation insight. Dayton has been using it to explore route-level scan data, transfer patterns for its ongoing system redesign, and fare-media usage, with the transit agency’s planning and finance teams identified as natural next users.

Self Service AI prototype screen

Research & development

How the incubator works

The Masabi AI Incubator starts with real operational and rider challenges, not the technology. We explore ideas with agencies, test them against real-world data, and only move capabilities into product once they've proven their value in three areas.

  • Agency impact:

    Does it make a tangible difference to the teams running the network, from planning and finance to revenue protection and customer service?

  • Rider impact:

    Does it make travel simpler, fairer or more reliable for passengers? We look for outcomes riders will notice.

  • Ready for the real world:

    Is it ready to deploy? We work alongside agencies to make sure the right data, safeguards and workflows are in place, so new capabilities fit how teams already work rather than adding complexity.

Masabi's AI programme

  • Workstream #1

    Revenue Protection

    Using machine learning to analyse ticket purchase and validation data, exploring new ways to spot where revenue is being lost, so agencies can focus investigation and enforcement where it will have the greatest impact.

  • Workstream #2

    Self-service data analytics

    Ask questions about ridership, fares and revenue in natural language and get the results alongside charts, breakdowns and the SQL behind them, so agency teams can go from question to insight instantly.

  • Workstream #3

    Rider engagement

    Exploring how passengers could discover and purchase public transit through emerging AI interfaces, meeting riders wherever they plan their journeys.

  • Workstream #4

    Customer service

    Resolving the repetitive queries that eat up Customer Service teams' time — instantly and accurately, whether a rider asks directly or an agent asks on their behalf. Enabling the team to spend more time on the cases that actually need a human.

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Masabi AI Labs

Partner with us

We partner with agencies who want to be involved in early evaluation — providing real-world data and operational feedback in return for early access.

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