Frizzle

Frizzle uses computer vision and LLMs to grade handwritten math at 97% accuracy, with a confidence-interval system that flags uncertain grades for human review. Students write on paper. Teachers photograph the work or run it through the copier. Within hours, Frizzle returns standards-level formative analytics, showing which CCSS standards each class and student has actually mastered, instead of waiting on spring assessments. The result: math teachers get back the 10-15 hours a week they spend grading by hand, coaches run specific standards-level conversations instead of generic ones, and districts cut math screen time without losing the classroom-level data they need. Live in 30+ schools and districts, including a college math pilot at Vanderbilt and ASU.

Frizzle transforms handwritten student work into actionable insights for K-12 math classrooms. It reads assignments in real time, identifying students' misconceptions and needs without requiring students to change their workflow. The platform enables teachers to understand their students' thought processes at a granular level, allowing for targeted teaching adjustments and support. Users photograph a stack of student papers using a phone or scanner, and Frizzle's computer vision technology reads and analyzes each page. This process involves parsing individual responses, understanding multiple methods of problem-solving, and generating live dashboards that summarize class performance and highlight areas needing attention. The platform integrates seamlessly into existing classroom practices, ensuring no disruption to students' learning routines. Frizzle is designed for K-12 math teachers and educators who seek to enhance their grading and feedback processes without requiring students to adopt new technologies. It is suitable for individual teachers and entire districts, making it adaptable regardless of the size of the educational institution.