EklavAI

Eklavya India Foundation

EklavAI

AI-based mentorship that overcomes social barriers in education by providing social and multilingual support to disadvantaged students all over India.

Nagpur, Maharashtra, India

IND

Nonprofit

—

In India, Scheduled Castes (SC), Scheduled Tribes (ST), Nomadic and Denotified communities (NT/DNT), and Other Backward Classes (OBC) constitute historically marginalised students who make up 15% of the world population while experiencing systemic exclusion from education through caste-based discrimination due to unavailability of accessible mentorship and resources and information deficits. According to data, only 5% of students in India's leading private universities are from SC backgrounds, and faculty representation is a paltry 4%. Rural tribal students in government-run schools face poor college readiness, and 78% drop out before going for higher education because of unresolved socio-cultural impediments. While community-led initiatives are our first priority, we need to expand our scope. Current ed-tech solutions neglect caste-specific structural issues like addressing discrimination, accessing scholarships, or convincing illiterate parents for their children’s education. First-generation learners are also hampered by English-heavy test preparation and city-focused career advice that does not factor in caste-based workplace barriers. These failures in the system perpetuate poverty across generations, with excluded groups losing more than a decade's worth of income through exclusion from education. This cycle will otherwise continue to deny equal opportunities to millions of learners unless addressed by targeted intervention.

Eklavya AI is an innovative mentorship platform with the potential to address caste-based barriers in education and bridge the gap with technology. Our solution offers multilingual AI mentors in Indian languages that provide 24/7 academic and career advice based on shared social experiences. The platform features test preparation tools for students in low-connectivity areas to hone case-sensitive algorithms that recommend strategies and coping mechanisms, identify caste-hostile campuses, and match learners with alumni mentors from the same background. Our AI-powered scholarship-matching system uses caste-based criterias to optimize funding and affirmative action opportunities. Built on OpenAI’s NLP and trained on a proprietary dataset of 50,000+ marginalized student interactions, Eklavya AI delivers context-aware support unavailable in generic ed-tech. Unique features include AI mock interviews that simulate caste-based workplace challenges and modules to help students navigate family resistance. Currently, we plan to pilot it for 20,000+ students through our LMS backbone, with pilot data showing a 30% increase in college application rates among users. Through partnerships with government schools and 500+ educational institutions, we plan to scale to 3+ million learners across the globe by 2026-27 to provide India's first affirmative action-compliant ed-tech solution that closes equity gaps in education.

Our solution is for first-generation learners from India's marginalized communities (SC/ST/OBC) located in rural schools and urban underserved communities. These students encounter overlapping barriers of caste bias, economic disadvantage, and limited academic support. The platform touches their lives directly by: 1) Offering caste-informed mentorship addressing specific socio-cultural needs 2) Democratizing access to high-quality test preparation in local languages 3) Establishing entry routes into higher education through bespoke scholarship matching. 4) Expand to serve 10x the number of students compared to physical classrooms, ensuring unlimited scalability without additional infrastructure costs. It translates into on-the-go support for navigating college applications with limited access to the internet. To a Dalit student in urban slums, it provides survival tips for discrimination in class while studying for entrance examinations to premier educational institutes. Initial pilots indicate users are 3x as likely to fill out college applications and 2.5x as likely to be awarded scholarships as non-users. The solution specifically empowers women who are subject to layered caste-gender discrimination and domestication with functionalities addressing security issues in education migration.