An adaptive tutoring engine for 240k students
A personalization engine that lifted average student outcomes 18% on standardized assessments — built in 12 weeks for a Series-B EdTech.
Existing personalization engines optimized for engagement metrics, not learning outcomes. Cohort needed an engine teachers could trust to align with curriculum standards.
A Bayesian knowledge-tracing model layered with an LLM-driven explanation generator. Per-curriculum constraints enforced by policy as code.
Pilot districts saw 18% improvement on year-end assessments. The system became Cohort's marquee Series-B differentiator.
“Their team understood pedagogy as well as our pedagogy team. That's not what I expected from an AI engineering firm.”
James Okafor · VP Product, Cohort.io
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