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For Investors
Mastiva is an AI-native mastery platform designed to connect assessment, practice, learner intelligence and progressive preparation into one continuous experience.
The opportunity
Students solve questions, take tests, revise concepts and attempt mocks throughout preparation. Mastiva is designed to connect these signals into a more useful view of the learner.
Classes and lectures
Knowledge delivery can remain separated from performance evidence.
Question banks
Practice can become volume-driven rather than context-driven.
Tests
Scores can become endpoints rather than learning signals.
PYQs
Authentic questions can exist as repositories without longitudinal learner context.
Revision
Reinforcement decisions can depend heavily on manual interpretation.
Parent visibility
Marks do not always explain the preparation trajectory.
The Mastiva thesis
It should be one of the inputs that makes the next learning decision better.
Stage 1 of 4
Observe performance through practice and structured assessments.
Produces authentic evidence of current preparation.
Why Mastiva
Mastiva is not simply a test series, question bank or AI tutor. Its value comes from connecting these experiences around the learner.
Dimension
Conventional
Mastiva
Assessment
Score and report
Assessment as a learning signal
PYQs
Static repository
Structured question intelligence
Practice
Generic or user-selected
Context-aware progression
Progress
Periodic snapshots
Longitudinal learner evidence
Learning path
Fragmented
Topic → Chapter → cumulative progression
AI
Generic assistance
Contextual learner and assessment intelligence
Feedback
Result-focused
Connected to the next learning action
Structured question intelligence
Actual JEE PYQs are authentic assessment artifacts. Mastiva's opportunity is to structure them by Topic and Chapter and connect them to learner history.
Physics
The evidence
Research across education supports testing, retrieval and distributed practice as useful learning mechanisms. Mastiva applies this evidence to a product architecture built around repeated, contextual assessment.
g = 0.499
Testing meta-analysis
222 studies, 48,478 students - a large evidence base supporting testing as a learning intervention.
Research context: Yang et al., 2021
d = 0.40
Testing and transfer
122 experiments, 10,382 participants - testing benefits can extend to transfer, application and inference.
Research context: Adesope et al., 2018
d = 0.54
Distributed practice
22 reports, 3,000+ learners - supports deliberate spacing rather than concentrated repetition.
Research context: 2025 meta-analysis
These findings are from broader educational research and are not direct JEE-specific outcome estimates. Read the full research.
The product
Mastiva connects assessment, JEE question intelligence, learner context, progressive practice and contextual AI assistance.
Assessment
Captures learner responses and performance evidence.
Question Intelligence
Maps questions to Subject, Chapter, Topic and assessment context.
Learner Intelligence
Builds an evolving representation of preparation evidence.
Learning
Connects evidence to practice, reinforcement and progression.
AI
Provides contextual intelligence and learning assistance.
Experience
Delivers student, parent and institute workflows.
The founders
Mastiva combines product conviction, engineering depth and AI/ML capability around a clearly defined learning problem.
Co-Founder & CEO
Product, business and technology leadership; leads Mastiva product vision, business strategy and execution.
Co-Founder & CTO / AI
AI/ML technical leadership; M.Tech in AI/ML from BITS Pilani; leads APEX Engine and AI/ML architecture.
Common questions
We welcome focused conversations with investors who want to understand the product, team and opportunity behind Mastiva.
Connect with the Founders
Interested in learning more about Mastiva, our journey, or the opportunity ahead? Reach our founding team directly at founders@mastiva.ai.