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Vikrama.
EdTech

Adaptive learning systems, assessment engines, and content platforms that scale.

We build the infrastructure behind personalized education at scale.

EdTech platforms need to personalize learning for millions of students, assess knowledge accurately, and deliver content that adapts to each learner. We build adaptive learning engines, assessment platforms, and content delivery systems that work at scale.

Why AI in Education is different.

The average MOOC completion rate is under 10%. That is not a content problem. It is a personalization problem. Every learner gets the same sequence, the same pace, the same assessments. Fast learners disengage. Struggling learners fall behind. The platform blames the student.

Adaptive learning systems that adjust difficulty and content in real-time show 45% better learning outcomes and 60-73% higher engagement. Meanwhile, generative AI is compressing content creation timelines from months to days. The platforms that combine both, adaptive delivery with AI-assisted production, will own the next decade of education.

The hard problem is not building a chatbot tutor. It is building a knowledge graph that maps what each learner knows, identifying gaps with precision, and serving the right content at the right difficulty at the right moment. That requires ML infrastructure, not just an LLM API call.

The Problems That Slow You Down

01

One-size-fits-all learning paths

Every student gets the same content in the same order. Fast learners are bored, struggling learners fall behind, and completion rates drop.

02

Assessment that tests memorization, not mastery

Multiple choice quizzes that don't measure real understanding. No adaptive difficulty, no skill mapping, no learning analytics.

03

Content creation bottleneck

Subject matter experts can create 2-3 lessons per week. The platform needs 200. Content quality varies wildly across instructors.

04

Engagement drops after week 2

Sign-ups are strong, but 70% of learners stop within 14 days. No re-engagement system, no progress nudging, no social learning.

These aren't pitch deck scenarios. Every use case maps to a system pattern we've built and deployed in production.

What We Build in EdTech

Adaptive Learning Engine

VP Product
Problem

Fixed LMS courses push same sequence to all; under 50% completion, limited mastery.

System

Bayesian knowledge tracing + RL bandit on clickstream/assessment/time data, adapting difficulty, pacing, and content type in real time.

45% better outcomes, 35% faster mastery, 60-73% higher engagement.

LLM Tutor / Course Copilot

Chief Academic Officer
Problem

Limited tutor availability; students get delayed assistance, high MOOC drop-off.

System

LLM+RAG grounded on course materials, chain-of-thought solving, scaffolded questions, learner profiles.

More accurate guidance, better engagement than generic LLMs.

Personalized Question Generation

Director of Assessment
Problem

Instructors spend hours writing quizzes; assessments reused yearly, leak to students.

System

IRT-aware LLM generates parameterized questions at specific difficulty levels, auto-grading rubrics.

Drastic reduction in authoring time, improved gap targeting.

Early-Warning Dropout Analytics

VP Student Success
Problem

20-40% students lost before completion; advisors manually scan grades and attendance.

System

XGBoost/Random Forest on LMS logs, grades, submissions, demographics, producing risk alerts in advisor dashboards.

Earlier interventions, improved retention when flagged students supported.

AI-Powered Proctoring

Head of Exams
Problem

Remote exams at scale; manual proctoring expensive, AI plagiarism rising.

System

CV + audio analysis for suspicious behaviors + NLP/LLM text forensics for AI-generated submissions.

Continuous monitoring at scale, far lower cost than human proctors.

Instructional Design Copilot

Director of Instructional Design
Problem

Designing digital courses takes months per course; updates lag industry changes.

System

LLM agent ingests syllabi, readings, standards, then proposes objectives, outlines, activities, assessments, iterative refinement.

Improved learning gains, more efficient course creation.

LLM Student Support Agent

VP Student Experience
Problem

Thousands of repetitive queries about schedules, financial aid, assignments. Staff overloaded.

System

Campus-branded LLM agent on institutional policies/catalogs/FAQs via RAG, handles queries and forms.

Reduced manual load, improved responsiveness.

Building the system is half the job. Growing the business around it is the other half.

How We Grow EdTech Brands

AEO for Course Recommendations

Growth Marketing Lead
Problem

Edtech loses 48% of "best online Python course" to ChatGPT; zero-clicks cut enrollments 32%.

Build

Schema syllabi/reviews, outcome FAQs, AI citation tracking to enrollment pages.

38% AI visibility boost, 26% enrollment traffic in 5 months.

LinkedIn Ads for Corporate L&D

B2B Marketing Director
Problem

LinkedIn CPL $75-110 for training; under 11% demo rate.

Build

L&D manager targeting, free trials/webinars, HubSpot nurture.

CAC 33% down to $60, pipeline 2x.

HubSpot Lead Scoring for Enrollments

Marketing Ops Manager
Problem

3K leads/semester; 62% unqualified.

Build

HubSpot scoring (assessments, visits), sequences (course previews), LMS sync.

Enrollment pipeline 42% growth, cycle 28% shorter.

Frequently asked questions

AI moves fast. Stay ahead.

No spam. One actionable email per week on AI systems and growth.

Your learners deserve more than a video playlist. We build systems that teach.

Start with a learning experience audit. We'll identify where AI can move the needle on engagement and outcomes.