Grand Prize
Incubation & a paid Proof of Concept (PoC) for top innovation.
Participate to win
a paid POC with
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Registrations Open
28th February 2025
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Last Submission Date
15th March 2025
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Result Declaration
28th April 2025
Problem Statements
Pick one or combine two and provide a solution leveraging AI.
Automobile websites can be overwhelming due to complex navigation. Can AI enhance feature discovery, helping customers find what they need faster and more intuitively?
Task:
Develop a system that leverages AI to enhance feature discovery on automobile websites, enabling users to quickly and intuitively find the information they need.
Automobile websites are often complex, featuring an overwhelming amount of information on specifications, variants, and features. For potential buyers, this can lead to confusion and frustration, ultimately impacting the purchase journey. By integrating an AI-driven solution, users can experience seamless navigation and easily discover features that align with their needs.
- For example, consider a first-time buyer exploring an SUV. They may be unsure about the key differences between variants or specific features like off-road capabilities. Can AI personalize the experience, guiding users through an intuitive discovery process that highlights features most relevant to their preferences?
Here are some approaches to consider while developing your solution:
- Create an AI-powered virtual assistant to guide users through personalized feature discovery based on their preferences.
- Implement AI models that analyze user behavior and recommend features dynamically, based on browsing history and real-time interactions.
- Ensure the solution prioritizes simplicity and user-friendliness
- Keep data privacy and security in focus.
- The system should deliver results dynamically and efficiently to meet user expectations.
OEMs collect customer data but struggle to deliver hyper-personalized experiences. How can AI help score and prioritize high-quality leads while serving highly relevant, contextual content to boost funnel performance?
Task:
Develop an AI solution to deliver hyper-personalized experiences by leveraging customer data across touchpoints, improving lead scoring, and prioritizing high-quality leads for better funnel performance.
OEMs collect vast amounts of data on customer behavior but struggle to convert this data into actionable insights. As a result, drop-offs occur due to a lack of relevant or timely communication. With an AI-driven hyperpersonalization system, OEMs can tailor their marketing efforts to individual needs, enhancing customer engagement and improving conversion rates.
- Imagine a user browsing a sedan on a website. The AI can analyze their preferences (e.g., fuel efficiency, budget) and prioritize high-relevance leads by sending them targeted offers or financing plans, reducing drop-offs significantly.
You could consider:
- Building an AI model to analyze customer preferences and segment them into actionable categories.
- Creating a lead prioritization system to assign quality scores to leads based on engagement data and behavioral patterns.
- Maintain robust data privacy measures.
- Focus on delivering hyper-relevant content to customers without being intrusive.
Customers find it difficult to compare car models based on pricing, features, and accessories. How can AI simplify this process and provide the best recommendations tailored to their needs?
Task:
Create an AI-driven system to enable intuitive comparisons of car models and variants, allowing customers to evaluate pricing, features, and accessory inclusions effortlessly.
Customers often find it challenging to compare various car models due to the sheer volume of data involved, from technical specifications to pricing and accessory bundles. A smart comparison tool powered by AI can simplify this process by providing tailored recommendations based on customer preferences.
For instance, a customer exploring two compact SUVs might want to compare them based on mileage, safety features, and available financing options. An AI-based solution could generate an easy-to-read comparison chart while highlighting the strengths of each model relative to the customer’s priorities.
Consider the following:
- Build an AI-based recommendation engine to generate personalized model comparisons
- Include natural language queries, allowing customers to ask questions like, "Which SUV under $20,000 has the best safety features?"
- Ensure accuracy in the comparison data.
- Prioritize a clean and easy-to-use interface for customers.
In India, Road safety is often overlooked & not prioritized while making car buying decisions. How can AI be leveraged to promote road safety for both, vehicles & buyers?
Task:
Develop an AI-driven system to enhance road safety awareness and decisionmaking for both car buyers and vehicles, ensuring safety becomes a key consideration in the purchasing process.
In India, road safety is often overlooked when purchasing a vehicle, with buyers prioritizing factors like price and mileage over essential safety features. Many customers are unaware of critical safety aspects such as crash test ratings, ADAS (Advanced Driver Assistance Systems), and emergency response technologies. An AI-powered solution can bridge this gap by providing personalized safety insights and recommendations. For instance, a customer looking at mid-size sedans could receive AI-driven suggestions on models with the best crash-test ratings, driver assistance features, and emergency braking systems. The system could also analyze accident trends and driving behaviors to offer proactive safety tips and awareness campaigns.
Consider the following:
- Develop an AI-based recommendation system that educates buyers on the importance of road safety.
- Integrate real-time accident data and safety feature comparisons to highlight high-risk factors.
- Use natural language queries, allowing customers to ask: "Which sedan under ₹15L has the best crash test rating and ADAS?"
- Implement AI-driven alerts for safer driving habits, such as speed warnings and fatigue detection.
- Ensure accuracy in safety data and comparisons.
- Prioritize a clean, engaging interface that educates buyers effectively.
- Leverage gamification or interactive elements to promote road safety awareness.
Incentives & Rewards:
Stand a chance to win exciting rewards
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Industry Collaboration
Pilot projects with OEMs & Tier 1 suppliers
Frequently Asked Questions (FAQs)
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Anyone passionate about AI and automotive innovation! Whether you are a student, startup, developer, designer, or industry expert, you are welcome to participate.
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You can participate either individually or in a team (maximum of 4 members).
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The Ecosystem Program: is virtual, allowing participants to collaborate and submit their projects online.
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No, you can use any programming language, AI/ML models, frameworks, or tools of your choice.
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Projects will be evaluated based on:
- Innovation & Creativity - Uniqueness and potential impact
- Technical Feasibility - Practicality and execution
- Business Viability - Potential for real-world application
- User Experience - Ease of use and design
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Shortlisted projects will be reviewed by industry experts, and finalists will get an opportunity to pitch their solutions to top OEMs and investors.
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You will need to submit your project via [submission platform link], including a demo, code repository, and a short presentation.