MetroMind

Inspiration: We saw how railway and metro organizations like KMRL and others struggle with scattered information across emails, WhatsApp, SharePoint, and other systems. Officials spend hours digging for the right document, which slows down decisions. That frustration inspired us to build MetroMind.

What it does MetroMind pulls data from multiple sources, organizes it into one searchable library, and lets officials simply ask questions through a multilingual chatbot. It also provides a command centre to assign tasks, track compliance with an acknowledge feature, and view analytics on efficiency.

How we built it: We designed MetroMind on a serverless cloud setup using Google Cloud platform, so it can scale easily. The AI engine reads and summarizes documents, while connectors bring in data from tools like Maximo, SharePoint, and WhatsApp. The chatbot layer makes the system interactive and user‑friendly.

Challenges we ran into: The hardest part was handling unstructured data from so many different platforms. We also had to make sure the chatbot worked in multiple languages and that compliance tracking was fully auditable.

Accomplishments that we're proud of: We turned scattered, siloed information into a single source of truth. Officials can now get precise answers in seconds instead of wasting time searching. Building a real‑time command cent with compliance tracking was another big win.

What we learned: We learned that solving real organizational pain points requires both strong AI and thoughtful design. Scalability and multilingual support are critical when working with public sector organizations.

What's next for MetroMind: We plan to add more integrations with enterprise tools, expand analytics with predictive insights, and pilot MetroMind with more railway organizations. Long term, we see it as a SaaS solution for any enterprise or government body facing similar data challenges.

Built With

• base44
• google-cloud
• mailgun
• twilio
• vertexai