RouteMind is a multi-tenant fleet management SaaS with live GPS tracking and an AI voice agent for operators. I built the full stack and the AI layer end to end.

The hardest part was the data architecture. Structured records like fleet profiles, driver data, and route history sit in MySQL. The high-frequency GPS event stream, which would choke a relational setup, runs through MongoDB instead. Vehicle positions render on a live map with latency low enough that movement feels real-time. That split is what kept the dashboard fast while thousands of GPS events landed every minute.

The AI layer is a voice agent built on a RAG pipeline and the OpenAI API. Operators call in, and the agent retrieves from a vector database of indexed fleet policies and route procedures before answering, never generic output. Questions outside its scope trigger a clean handoff to a human.

The platform is fully multi-tenant with strict data isolation between businesses. Roles and permissions are set from the dashboard, so onboarding a new operator needs no code change.

What the client gets: live fleet visibility, an AI agent that answers policy questions without tying up staff, and an architecture that holds up as the fleet grows.

Great work! my fyp was about Driver's behaviour analysis (dowsiness, distraction detection ) for fleet transportation system and generating alerts while danger is detected.

One pattern I’ve seen repeatedly while working with growing businesses: At first, manual processes seem manageable. A few spreadsheets. A few marketplaces. A few team members coordinating inventory manually. Everything works. Until growth starts. Suddenly, the same processes that supported the business become bottlenecks. Inventory mismatches increase. Orders take longer to process. Teams spend more time updating data than making decisions. And scaling requires hiring more people just to keep operations running. Recently, we worked on a project where the challenge wasn't building another customer-facing feature. The challenge was creating a system that could automate inventory management, synchronize data across multiple channels, and give the business better operational control as it grew. The project reinforced something important: Many companies think they need more people. Often, they need better systems. The strongest operational improvements usually happen when repetitive manual work is removed and teams can focus on higher-value activities instead. Technology creates the biggest impact when it solves operational friction, not just when it adds new functionality. Have you ever reached a point where growth exposed weaknesses in internal processes that seemed perfectly fine before?

Check it out