building markwise - from scratch to private beta
back in march 2025, i started with just an idea: could ai genuinely improve revision for students? just a few months later, markwise was already helping students in a private beta.
where it all started
revision has always been a pain. traditional methods felt inefficient and outdated. my goal was simple: build something students actually wanted to use.
here were my initial ideas:
- personalized practice: revision that adapts in real-time based on your performance.
- exam-specific insights: leveraging examiner reports and past papers to deliver targeted practice.
- instant, actionable feedback: no more waiting - get immediate insights to correct mistakes quickly.
from concept to reality
the early days
i kicked off with a simple prototype, focusing on the core ai functionality:
- built an initial model to match exam questions to student weaknesses.
- created a straightforward ui using next.js and tailwind, prioritizing simplicity and ease of use.
developing the ai tools
building effective ai for education was challenging but rewarding:
- retrieval augmented generation (rag): integrated to provide relevant study content dynamically.
- memory agent: tracks student performance and automatically adjusts the revision material accordingly.
- intelligent question bank: ai analyzes past exams and examiner feedback to select the best questions for each student.
entering private beta
by may, markwise was ready for real-world testing:
- feedback loop: collaborated with actual students, refining features based on direct user feedback.
- iterative improvements: quickly updated the platform to meet real student needs - from ui tweaks to more complex ai enhancements.
- early successes: students reported improved revision efficiency and boosted confidence.
markwise doesn’t just improve revision; it makes revision actually effective.
key lessons learned
building markwise taught me a lot, especially about moving fast while staying student-focused:
- listen to your users: direct student feedback guided nearly every significant improvement.
- focus on core features: ai-driven personalization was the heart of markwise, and everything else supported that.
- iterate quickly: small, frequent improvements kept momentum high and users engaged.
what's next?
markwise is still evolving, with the private beta helping refine features further:
- expanding ai capabilities to cover more subjects and exam boards.
- enhancing user experience with even smoother interactions and smarter insights.
- preparing for a broader release later this year.