A tutor that confidently teaches the wrong physics is worse than no tutor. Faithfulness is the entire game.
Content teams spend 60% of their time on adaptation — by language, by syllabus, by board. AI cuts this by half if done well.
Subjective questions are the bottleneck. Rubric-aware AI grading + human moderation is the right shape.
Tier-2 and Tier-3 learners need vernacular instruction. Translation alone isn't enough — you need multilingual generation grounded in syllabus.
Tutors that retrieve from your textbook + question-bank corpus, cite sources, and refuse to answer outside scope. RAGAS-scored.
Learner-state models tied to learning outcomes — not vanity engagement metrics. Cohort-level + individual-level adaptation.
Multilingual generation on syllabus-grounded prompts, with editorial-review workflows. Halves authoring cycle on average.
Rubric-aware subjective grading, plagiarism + AI-text detection, proctoring assist. Always with human moderation in the loop.
RAG over CBSE / ICSE textbook corpus + 200K-question bank. RAGAS-scored on every release. 95%+ answer faithfulness, zero out-of-scope responses.
Content-authoring copilot for 8 Indian languages. Editorial-review workflow. Cycle time down 47% with no loss in editorial quality scores.
Hard guardrails — every answer is grounded in retrieved corpus, with citations. Outside-scope queries trigger a refusal. RAGAS faithfulness < 95% blocks the deploy.
DPDP-aligned consent flows, parental opt-in for under-18, minimal PII collection, and content-safety filters tuned for the audience. Audit logs retained per regulator policy.
Yes — our content stack is syllabus-aware (CBSE / ICSE / state boards) and multilingual across the major Indian languages. Adding a board is typically a 2–3 week ingest.