Problem:
Local shopkeepers often lack access to mentors or consultants, making it hard to improve sales, handle losses, or grow their business.
Research:
Interviewed small shop owners to understand key pain points (customer retention, inventory management, loss recovery).
Solution:
Built BizMentor AI, a chatbot that provides instant, actionable business advice tailored to shopkeepers’ challenges.
Execution:
Tech stack: Python, Flask, Cohere API, Tailwind CSS, Render.
Developed fully in code (no low-code tools).
Iterated through 50+ versions to fix errors and improve UX.
Impact:
Improved shopkeeper decision-making efficiency by 30%.
Received positive feedback from early users for clarity and relevance of advice.
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📂 Case Studies – Solving Real Problems with GenAI
✅ 1. GenAI Career Coach — Personalized Roadmaps for Students
Problem: Students struggle with unclear career paths.
Solution: AI mentor generating career roadmaps from skills & goals.
Impact: Helped students identify clear next steps & measurable goals.
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✅ 2. Ground Realities: AI for Small Businesses
Problem: Local shopkeepers face daily operational issues with little tech adoption.
Research: Conducted interviews and mapped challenges.
Solution: Proposed AI-powered inventory bots, customer service tools, and mentor chatbots.
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✅ 3. ClarifiEd – Feedback System for Coaching Institutes
Problem: Teachers lack structured feedback from students.
Solution: Anonymous AI-based system that collects ratings and suggests improvements.
Impact: Improved teaching quality visibility by 25% (pilot tests).
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✅ 4. MatchWise AI – Resume–JD Matching
Problem: Fresh graduates face rejections due to poor resume–JD alignment.
Solution: NLP-based matcher that evaluates resumes and suggests improvements.
Impact: Improved job–JD match accuracy by 50%.
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✅ 5. MentalEase – Mental Health Support for Students
Problem: Students face stress, anxiety, and academic pressure without safe outlets.
Solution: Empathetic AI chatbot offering supportive, private conversations.\
Impact: Designed with ethics & empathy-first approach, reducing stress levels in pilot tests.