Imagine a world where managing your vehicle is as simple as checking your phone—no more juggling multiple apps, relying on dealership visits, or feeling blindsided by unexpected repairs. As part of my capstone project for the Designing and Building AI Products and Services program by MIT xPRO, I’ve developed a proposal for an AI-powered platform that consolidates vehicle data into a single, user-friendly interface. This innovative solution uses machine learning to monitor vehicle health, analyze driving habits, and predict potential issues, empowering owners to drive smarter, save money, and stay ahead of maintenance needs.

The Problem: Fragmented Vehicle Management

Vehicle ownership today comes with its share of frustrations. From multiple vehicle households trying to anticipate repair costs, to daily commuters panicking over unfamiliar warning lights, to electric vehicle (EV) drivers seeking charge station updates—drivers lack a centralized resource for actionable insights. Dealership visits every six months offer limited visibility, forcing owners to trust external experts without understanding their vehicle’s true condition. Worse yet, many rely on a patchwork of OEM-specific apps that fail to provide a holistic view.

Through the discovery phase of the Double Diamond Technique—a framework I applied during the xPRO program—I identified key pain points: no predictive tools for vehicle health, difficulty planning for expenses, and the inconvenience of managing multiple applications. These insights shaped my vision for a unified solution.

The Solution: An AI Mechanic in Your Pocket

Enter my proposed platform: a mobile app that consolidates all your car data into one place, acting as your personal AI mechanic. Using machine learning, it analyzes real-time data from your vehicle—think engine noises, sensor readings, and driving patterns—to deliver proactive alerts, personalized maintenance recommendations, and fuel efficiency tips. Whether you’re a multi-vehicle household, an EV owner, or someone who dreads dealership visits, this app simplifies the experience and puts control back in your hands.

Here’s the elevator pitch I crafted for a team of hypothetical investors:

"Tired of juggling multiple apps and waiting for dealership visits to understand your car? I’ve got the solution! This AI-powered platform consolidates all your car data into one place, using machine learning to analyze your vehicle’s health, driving habits, and even predict potential issues. You’ll get proactive alerts, personalized maintenance recommendations, and insights to improve fuel efficiency—like having a personal mechanic in your pocket, without the hassle of extra apps or dealership trips. Want to drive smarter and save money? Let’s talk."

Designing with the Double Diamond Technique

The Double Diamond Technique guided my development process, ensuring a thorough understanding of the problem and a robust solution. Here’s how it unfolded:

1. Discover Phase: Understanding the Needs
   I brainstormed opportunities for AI intervention, focusing on stakeholders like vehicle owners, drivers, manufacturers, and dealers. Key needs included real-time vehicle health insights, expense planning, and EV-specific features like charge station locators. I defined Key Performance Indicators (KPIs) to measure impact, such as user adoption rates, reduction in unplanned repair costs, and app engagement metrics.

2. Define Phase: Pinning Down the Pilot
   The most pressing issue? The fragmentation caused by multiple apps and reliance on dealership expertise. My pilot focuses on a unified integration app that lets users query vehicle data, record noises, and receive detailed diagnostics—positioning it as the go-to resource for vehicle owners, a gap largely unaddressed in today’s market.

3. Develop Phase: Building the Prototype
   To bring this pilot to life, I propose partnering with a prominent Original Equipment Manufacturer (OEM) to aggregate data and leverage pre-trained AI models. These models will analyze audio recordings and images to diagnose issues, offering a scalable solution that mitigates the risk of securing multiple partnerships upfront. Early adopters will drive momentum, encouraging other OEMs to join.

4. Deliver Phase: Deploying and Measuring Success
   The deployment roadmap includes three phases:

   • Phase 1: Data Collection and Analysis – Partner with OBD-II device manufacturers for seamless data integration, build initial machine learning models, and set up backend infrastructure.

   • Phase 2: MVP Development – Launch a user-friendly app with basic diagnostics and notifications, refining it through internal testing and feedback.

   • Phase 3: MVP Launch and Iteration – Roll out the app, gather real-world data, and iterate based on user insights to enhance accuracy and functionality.

   Progress will be tracked using KPIs like diagnostic accuracy, user retention, and cost savings, visualized in a four-bar chart to align with the Double Diamond stages—ensuring transparency for stakeholders and investors.

Why This Matters

This platform isn’t just about convenience—it’s about empowerment. By leveraging AI technologies like supervised learning for predictive maintenance and unsupervised learning for driver behavior analysis, it transforms raw data into actionable insights. It addresses strategic implications (e.g., reducing dependency on dealerships) and operational efficiencies (e.g., proactive repairs), while navigating risks like partnership timelines through a phased, scalable approach.

Looking Ahead: A Six-Month Action Plan

Over the next six months, I plan to:

• Model Development: Build and refine machine learning models for vehicle health and driver behavior analysis.

• Integration: Embed these models into the app’s backend, translating outputs into user-friendly insights.

• Feedback Loop: Establish a system to collect user feedback and retrain models for continuous improvement.

• Exploration: Investigate advanced AI techniques, like anomaly detection or reinforcement learning, to further personalize the experience.

Final Thoughts

This project, born from the xPRO capstone, reflects my passion for designing AI solutions that solve real-world problems. It’s more than an app—it’s a step toward smarter, more sustainable vehicle ownership. I invite feedback, collaboration, or investment inquiries to bring this vision to life. Let’s drive the future together—reach out via sofiyaa.m@gmail.com or explore more of my work at sofiyaasubramanian.com.