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.