Crack the MDCAT — Score Higher with AI Precision
AceMed AI is trained on exact FSC Federal, PTB, and provincial board books, offering precise, personalized preparation powered by advanced AI.
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AceMed AI merges technology with education to create a personalized and efficient MDCAT prep experience. It replaces costly coaching centers with smart, adaptive tools, empowering students to:
- Learn at their own pace
- Focus on weak areas
- Practice with real-time AI feedback
Mission: To make high-quality MDCAT prep accessible, smart, and personalized through the power of AI.
AceMed AI is Pakistan’s first AI-powered MDCAT preparation assistant, trained on PTB and Federal Board books. It provides:
- ✍️ Accurate, syllabus-based answers
- 📊 Performance analytics dashboard
- 🤖 Chatbot interface for MDCAT Q&A
- 📚 Curated MCQ banks
- 🔍 Step-by-step numerical solvers
- 🔄 Adaptive learning and feedback loops
| Layer | Tools & Frameworks |
|---|---|
| Frontend | Streamlit, HTML, TailwindCSS, React.js |
| Backend | FastAPI, LangChain, Python, HuggingFace |
| Database | MongoDB, Redis |
| AI Models | LLaMA, LoRA Fine-Tuning, Transformers |
| Infra | Google Colab Pro+, GitHub, Vercel |
We leverage the LLaMA model, fine-tuned using LoRA (Low-Rank Adaptation) to adapt the base language model to the MDCAT domain. This enables precise, context-aware responses aligned with FSc and PTB syllabi.
👉 Open Fine-Tuning Notebook in Colab
| Component | Details |
|---|---|
| Base Model | Meta LLaMA (7B) |
| Fine-Tuning Method | LoRA (Low-Rank Adaptation) |
| Data Used | Curated PTB + Federal Board textbook content + MDCAT MCQs |
| Framework | 🤗 Hugging Face Transformers + PEFT |
| Notebook | Google Colab for rapid iteration |
| Training Objective | SFT (Supervised Fine-Tuning) on syllabus-aligned Q&A |
| Epochs | 3–5 (adaptive based on validation loss) |
| Optimization | AdamW optimizer, 5e-5 learning rate |
| LoRA Ranks | r=8, alpha=16 |
| Hardware Used | Google Colab Pro+ (A100 GPU) |
AceMed AI follows the Agile Software Development Life Cycle (SDLC) for rapid iteration, scalability, and user-focused features.
-
Research & Requirement Analysis
- Aligning features with PMC syllabus & student feedback
-
Data Collection & Preprocessing
- Structuring PTB/Federal board content and MCQs
- Cleaning & labeling data for model training
-
AI Model Development
- Fine-tuning transformer-based models
- Developing step-by-step numerical solvers
-
System Development & Integration
- Backend infrastructure + chatbot interface
- Interactive dashboard for performance analytics
-
Testing & Deployment
- Unit & integration testing
- Beta deployment for real-world usage
-
User Feedback & Iteration
- Feedback from students & educators
- Feature refinement & model accuracy tuning
| Plan | Price | Features |
|---|---|---|
| 🎓 Free | Rs 0/month | Limited AI question generation, Basic analytics |
| 🥇 Gold | Rs 2500/month | Unlimited AI-generated questions, Detailed performance analytics, Support |
| 👑 Platinum | Rs 5000/month | All Gold features, 1-on-1 mentoring, Exclusive MCQ banks |
Q: How is AceMed AI different from ChatGPT?
A: It's trained specifically on MDCAT syllabus (Federal/PTB), ensuring relevant, accurate answers.
Q: Can AceMed AI improve my marks?
A: Yes, through adaptive learning and targeted practice.
Q: Is past paper practice included?
A: Yes, along with textbook references and explanations.
Q: How accurate are the answers?
A: 95%+ based on internal testing. Manual reviews ongoing for edge cases.
AceMed AI is open-source and welcomes contributions:
- Fork the repository
- Create a feature branch
- Commit your changes
- Open a pull request
- License: MIT License
- Website: AceMedAI.com
- Contact: support@acemed.ai |