Rankri brings together memory-system design, infrastructure, predictive modelling, interface design and exam-content automation. Each role solves a different part of the same problem: helping students remember more with less daily confusion.
The team responsible for the scheduling engine, sync system, data pipelines and the interface students use every day.
Designed Rankri's learning and revision logic. Internal development involved testing more than 10,000 algorithmic questions to understand how scheduling choices affect daily revision load. Leads the integration of modern FSRS scheduling with Rankri's subject-aware workflows.
Works on the backend and synchronization architecture that connects Rankri's web and mobile clients with Supabase and PostgreSQL, while keeping local study data responsive and reliable.
Studies retention patterns and helps tune how the scheduler reacts to review history. The focus is not on adding complexity for students, but on making the next revision date more useful.
Turns complex scheduling and progress data into simple actions. Her work focuses on reducing the number of decisions students need to make while studying.
Builds tools that turn raw exam material and mock data into structured Topics and Questions that Rankri can track, schedule and analyse.
Rankri's content system needs subject experts who can convert large chapters into small, clear and testable Questions without losing the original meaning.
Breaks formulas, methods and multi-step solutions into clear question units that can be learned, practised and scheduled properly.
Organises History, Geography, Polity, Economy and Science into concise facts, while keeping related information connected.
Maps vocabulary, idioms and grammar rules into learning screens and recall questions that are easy to understand and test.
Content-partner names can be added publicly when those partnerships are finalised.
The following results were included in Rankri's supplied internal team and validation document.
Team profiles and validation details on this page are presented from information supplied by Rankri. Public verification links can be added before launch where available.