AI-native education platform used daily by 17,000+ students and teachers.
Visit Tututor.aiTeachers were spending the majority of their week assembling lesson material, quizzes, and remediation content by hand. Existing 'AI tools' produced generic output that needed so much editing they barely saved time. The school's CRM was a separate product entirely, so admins were duct-taping spreadsheets to track classes, content, and student progress.
I treated this as one product, not two. A small set of AI primitives — chatbots, quiz generation, grading, conversation analytics — composed into teacher-facing tools that share a single content + class model with the CRM. Heavy AI flows live behind a microservices boundary so the teacher UI stays responsive while the LLM is thinking. WebSockets stream partial responses, and conversations are stored structured (not blobs) so teachers can review and search them.
The boundaries that mattered: keeping the teacher UI responsive while heavy AI work happens behind a WebSocket + microservice boundary.
Lesson-prep time dropped 90–95% in measured cases. Several schools in Murcia adopted the platform; thousands of students use it daily. The architecture has held as features compounded — adding the quiz generator and conversation analytics didn't require rewriting the core.