Career Intelligence Platform

Longitudinal Career Guidance for India's Youth

Eklavya is a longitudinal career intelligence system that builds comprehensive, evolving profiles of students from age 12 through professional life. Using advanced machine learning algorithms, it matches individuals with scholarships, colleges, internships, and career opportunities uniquely aligned with their potential.

The Challenge

India's Career Guidance Crisis

India's education system produces millions of skilled graduates annually, yet the majority make life-defining career decisions in information vacuums. Students lack continuous, personalized guidance to understand their own potential or discover opportunities that align with their unique strengths.

The current career counseling model is fundamentally broken. Students typically receive guidance only at critical junctures (class 10, 12, or university entry), often from counselors who lack deep knowledge of individual students' evolving capabilities, interests, and learning patterns.

This fragmented approach results in mismatches: students choosing wrong streams, missing relevant opportunities, and pursuing paths misaligned with their actual potential. The consequence is wasted talent, high stress, and suboptimal outcomes for both individuals and society.

42%
of Indian students feel their stream choice was wrong after one year
7/10
students miss relevant scholarships due to lack of awareness
15M+
eligible students unaware of available opportunities
Zero
continuous guidance platforms with ML-powered matching
Our Answer

A Longitudinal Approach to Career Intelligence

Rather than one-time assessments, Eklavya builds a continuous, evolving profile of each student from age 12 through professional life. It captures academic records, skills, interests, achievements, and daily learning activities, feeding this data into advanced matching algorithms to surface opportunities uniquely suited to each individual.

Continuous Profile Evolution
Students log academic records, skills, interests, achievements, and daily progress. The system automatically updates profile completeness scores and refines understanding of career trajectory.
AI-Powered Opportunity Matching
A three-algorithm pipeline classifies students into 6 career tracks, finds similar peers for collaborative learning, and scores 500+ opportunities against individual profiles.
Multi-Platform Coverage
Web platform for exploration and guidance, mobile app for daily logging, desktop client with offline capability for remote areas.
Conversational Intelligence
Onboarding via conversational AI (Ollama LLM) makes profile building frictionless. Desktop version uses offline LLM for privacy in low-connectivity regions.
Technical Foundation

Seven Core Modules

Eklavya's architecture is built around seven interconnected modules, each handling a distinct function in the career intelligence pipeline.

Authentication & Sessions
Google OAuth 2.0
Phone OTP
JWT Sessions
Supabase Auth
Onboarding Engine
Conversational intake
Profile initialization
Education history
Skills & interests
Longitudinal Profiling
Academic tracking
Skills evolution
Achievement logging
Activity journals
Matching Engine
Decision Tree classification
K-NN clustering
Weighted scoring
Composite ranking
Opportunity Dataset
500+ opportunities
Scholarships
Colleges & internships
Jobs & certifications
Social Layer
Interest-based communities
Peer engagement
Activity feeds
Collaborative learning
Core Technology

Three-Algorithm Matching Pipeline

Eklavya's matching engine uses a sophisticated three-stage algorithm pipeline to provide highly personalized opportunity recommendations:

1
Decision Tree Classification
Classifies each student into one of 6 career tracks based on interests, skills, and academic performance.
Technical/Engineering, Medical, Creative, Business, Research, or Vocational
2
K-Nearest Neighbors
Finds 5 similar users and extracts their opportunity engagement history for collaborative filtering.
Discovers patterns in peer choices without explicit recommendations
3
Weighted Scoring
Scores each opportunity against student profile using eligibility criteria, academic scores, and career alignment.
Content-based matching on 55-dimensional user vectors
4
Composite Ranking
Combines all three scores into a final composite ranking of top 20 opportunities.
Formula: 0.5×Weighted + 0.3×KNN + 0.2×Recency
How It Works: Each student is represented as a 55-dimensional vector capturing age, education level, profile completeness, interests (16 dims), skills (10 dims), career track confidence (6 dims), and activity indicators (10 dims). The Decision Tree uses these features to classify career track. KNN finds similar students and weights their opportunity engagement. Weighted scoring evaluates each of 500+ opportunities against the student's profile. The final composite score reflects content-based relevance (50%), collaborative preference (30%), and opportunity freshness (20%).
Real-World Impact

How Eklavya Guides Career Paths

Age 12-15: Foundation Building
Priya - Pre-Board Student
Priya logs her interests (coding, design), emerging skills, and scores. Eklavya identifies her as Technical track candidate and recommends STEM-focused competitions, coding camps, and junior scholarships she wasn't aware of.
Action: Enrolls in AI workshop, scores well, builds portfolio early.
Age 16-18: Stream & College Selection
Arjun - Pre-University Student
Arjun has been logging his performance across 3 years. His profile shows strong math but emerging interest in business strategy. Eklavya surfaces IIT/NIT options and also recommends tier-2 colleges with strong management programs—and specific CMAT preparation scholarships.
Action: Applies strategically across multiple paths, gets into top college with scholarship.
Age 19-22: Opportunity Acceleration
Maya - Undergraduate
With 6+ years of longitudinal data, Eklavya knows Maya's trajectory: consistent strong performance, emerging leadership, industry interest. Recommends tier-1 internships, targeted to her profile. No generic job boards—only opportunities she's likely to succeed in and enjoy.
Action: Secures dream internship placement, returns as full-time hire.
Age 23+: Career Resilience
Rohan - Young Professional
Rohan's profile spans 12 years. When he faces job loss, Eklavya uses his historical data to identify adjacent career paths he's well-suited for—upskilling programs, emerging roles in adjacent industries, and companies actively seeking people like him.
Action: Transitions smoothly to high-growth sector, earning increase within 8 months.
Core Capabilities

What Eklavya Delivers

Longitudinal Profiling
Continuous data collection from age 12 onwards. Academic records, skills, interests, achievements, and daily activities automatically shape profile evolution and opportunity matching.
ML-Powered Matching
Decision Tree classification, K-NN collaborative filtering, and weighted scoring combine to surface 20 personalized opportunities from a database of 500+ annually updated entries.
Conversational Onboarding
AI-driven intake process (Ollama LLM) makes profile building conversational and frictionless. Desktop version uses offline LLM for privacy and connectivity resilience.
Cross-Platform Access
Web platform for exploration, mobile app for daily logging, desktop client with offline capability. Unified profiles sync seamlessly across all surfaces.
Social Learning Communities
Interest-based groups enable peer engagement, collaborative learning, activity feeds, and networking. Drives continuous engagement and collaborative filtering inputs.
Admin Opportunity Management
Curate, bulk-import, and manage 500+ opportunities. Tag by career track, eligibility criteria, deadlines. Analytics dashboard tracks matching effectiveness.
About Eklavya

Venture Overview

Eklavya is incubated under Shoonya Origins, a venture studio building transformative solutions for India's most pressing challenges. Named after the legendary student who achieved mastery through dedication and self-guidance, Eklavya represents that principle: personalized, continuous, intelligent career guidance.

The platform is designed for India's reality. Most students don't have access to high-quality career counseling. Those who do receive it only at critical junctures. Eklavya changes that by being continuously available, deeply personalized, and grounded in actual student data rather than assumptions.

Our approach is pragmatic: start with longitudinal data from age 12, build intelligence over years, and compound advantage through better decision-making at each life stage. The result is not just better individual outcomes—it's more efficient talent discovery and allocation across India's economy.

Built by Yogesh Saybu Jathalkar
Enrollment 2251513643 (IGNOU BCA)
Institution IGNOU Pune Regional Centre
Incubator Shoonya Origins
Tech Stack FastAPI, React, React Native, Supabase, scikit-learn
Deployment Render + Supabase (Cloud)