EdTech

Personalize learning. Cut grading time. Keep students from dropping out.

We build adaptive learning engines, AI assessment platforms, early-warning systems, and LMS integrations in 12 weeks. Software that lifts completion rates, gives instructors their week back, and makes learning actually work for each student.

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14+

Edtech products

45%

Completion rate lift

3x

Engagement increase

Overview

Your learners are dropping out because the platform can't adapt

Edtech software development at RaftLabs focuses on the three areas that most directly move learner outcomes - personalization, assessment efficiency, and retention. We bring patterns from 100+ products across adjacent industries to build adaptive learning engines, AI grading systems, early-warning tools, and content generation platforms - each engineered for measurable completion rate lift and cohort-level engagement.

Most eLearning platforms are glorified file servers. They host videos, track completion, and call it a day. The result - 85% of online courses finish below 15% completion, instructors spend more time grading than teaching, and students who struggle get no help until they fail.

We build adaptive learning systems that adjust content difficulty in real time. Assessment platforms that grade open-ended responses with 94% accuracy. Engagement engines that flag dropout risk 3-4 weeks before it happens.

Every product integrates with your existing LMS - Canvas, Blackboard, Moodle, D2L - through LTI, xAPI, and standard APIs. Your content library and student data stay put. No platform migration required.

Experience Signal

14+ edtech products shipped with adaptive learning engines, AI assessment systems, early-warning tools, and LMS integrations for coding bootcamps, universities, online degree programs, and corporate training providers. 12 weeks is our default, not our stretch goal.

14+

Edtech products

45%

Completion rate lift

See case study

Industry Pain Points

What's broken in edtech

01

Online course completion averages 5-15% because one-pace-fits-all delivery loses students who are either bored or overwhelmed

02

Instructors spend 10-15 hours a week on grading and feedback, leaving minimal time for curriculum work and student interaction

03

At-risk students get identified only after they fail an exam or stop showing up - by then recovery is expensive and unlikely

04

Content creation takes 200+ hours per hour of polished eLearning, so curriculum falls out of date faster than teams can refresh it

05

Learning analytics stop at completion rates and quiz scores, giving no insight into what students actually understand or where they struggle

06

Generic nudges and reminders get ignored because they're not tied to where each student actually is in the curriculum

Solutions

Problems we solve in edtech

Each solution is built from patterns we've validated across 100+ products. No experiments on your budget.

01

Adaptive learning engine

Adjusts content difficulty, sequence, and format based on each student's performance, pace, and engagement. Students who grasp a concept quickly move forward. Students who struggle get alternative explanations and extra practice.

02

Automated assessment and grading

Grades open-ended responses, essays, and code submissions with rubric-based feedback. Handles plagiarism detection and paragraph-level suggestions. Frees instructors from repetitive grading without dropping quality.

03

Early warning and intervention

Tracks engagement patterns, assignment completion, assessment performance, and login frequency to predict dropout risk. Triggers automated nudges and alerts instructors to step in before students disengage completely.

04

Content generation and curation

Generates practice problems, quiz questions, study guides, and explanatory content from existing course materials. Curates supplementary resources from open educational content. Cuts content creation time 60-70%.

05

Learning analytics dashboard

Goes beyond completion rates to show concept mastery, skill gaps, engagement trends, and cohort comparisons. Tells instructors and deans what students actually know - and where curriculum changes will move the needle.

06

Mobile learning and offline sync

Native mobile apps with offline-first course delivery, study tools, and push notifications. Students on inconsistent connectivity finish courses on their phone during commutes instead of abandoning them.

Use Cases

Real-world use cases

Adaptive learning platform for a coding bootcamp

Problem

A coding bootcamp with 2,400 annual students had a 32% completion rate. Students with prior experience found the curriculum too slow. Beginners got lost by week 3. Instructors couldn't personalize across 200-student cohorts.

What we built

We built an adaptive learning engine that assessed each student's skill level at enrollment, adjusted content difficulty and pacing in real time, and surfaced alternative explanations when students got stuck. AI-generated practice problems targeted each student's weak areas.

Result

Completion rate climbed from 32% to 61%. Student satisfaction grew 1.4 points. Time-to-job-ready for advanced students dropped 18%. Instructor time on individual remediation fell 55%.

Automated assessment for a university writing program

Problem

A university writing program ran 3,200 students per semester across 80 sections. Each student submitted 6 essays per term. TAs spent 20+ hours a week grading with inconsistent feedback across sections.

What we built

We built an AI grading system trained on 5 years of faculty-graded essays and rubric scores. The system evaluates thesis clarity, argument structure, evidence use, and writing mechanics - with paragraph-level feedback and suggested revisions.

Result

Grading time per essay dropped from 18 minutes to 4 minutes with AI pre-grade plus TA review. Feedback consistency across sections grew 72% by rubric score variance. Second drafts scored 0.6 points higher on average.

Early warning system for an online degree program

Problem

An online degree program with 8,000 active students had 44% first-year attrition. Student advisors managed caseloads of 400+ and couldn't proactively spot at-risk students until they stopped submitting work.

What we built

We built a predictive model using LMS engagement, assignment submission patterns, discussion forum activity, and assessment scores. The system scored risk weekly, triggered automated nudges for medium-risk students, and alerted advisors on high-risk cases.

Result

First-year attrition dropped from 44% to 31%. Advisors focused on the 15% of students who needed help most instead of reacting to the 44% who'd already disengaged. At-risk students contacted within 48 hours of risk escalation showed 3.1x higher retention.

Proof

How we've solved edtech problems

EdTech

SaaS LMS for French-speaking African K-12 schools

Affordable SaaS LMS for K-12 schools in French-speaking Africa - 4,000+ students per school, 25% boost in school community engagement, concept to launch in 16 weeks.

16 weeks

Delivery timeline

4,000+ per school

Students supported

“We needed a platform built for African schools, not adapted from a Western LMS. RaftLabs understood that from the start - multilingual, offline-aware, and priced for our market. Sixteen weeks later, schools were onboarding.”

Jennyfer Ngueno, Co-founder, SEKOU
Read case study
EdTech

Phygital music learning app for TuneClub

Phygital music learning app connecting digital practice to real-world performance - structured pathways, creator tools, and adaptive streaming in 12 weeks.

12 weeks

Time to launch

Structured with linked events

Learning pathways

“I came to RaftLabs after another agency burned through my budget without shipping anything usable. They understood the vision immediately - connecting digital practice to live performance. The platform was in learners' hands in 12 weeks.”

Gabe Moynagh, Founder & CEO, TuneClub
Read case study

Our Approach

How we approach edtech projects

1
Phase 1· Week 1-2

Learning experience audit and data readiness

We analyze your curriculum, learner data, completion patterns, and instructor workflows. Drop-off points, content gaps, and where instructor time is being wasted on tasks AI can handle all get identified.

Deliverables

  • Learner journey analysis with drop-off points and engagement patterns
  • Instructor time audit quantifying grading, admin, and student support hours
  • Prioritized opportunity list ranked by outcome impact and implementation speed
2
Phase 2· Week 2-4

Product design and LMS integration planning

We design the product with your instructional designers, faculty, and IT team. Every integration point - LMS, SIS, content library, assessment tools - gets mapped before build starts.

Deliverables

  • Product design validated by instructional design and faculty reviewers
  • LMS integration specifications for LTI, xAPI, and custom APIs
  • Data architecture for learner profiles, content metadata, and analytics
3
Phase 3· Week 4-10

Build, integrate, and cohort pilot

We build in sprints and deploy to a pilot course or cohort first. Real student interactions validate accuracy and learning outcomes before institution-wide rollout.

Deliverables

  • Working product deployed in pilot course with real students
  • Learning outcome metrics from the pilot cohort
  • Iteration backlog based on student feedback and instructor observations
4
Phase 4· Week 10-12

Rollout and continuous optimization

We roll out across courses and programs with curriculum-specific configuration. AI models improve continuously as more student interaction data feeds the system.

Deliverables

  • Full deployment across target courses and programs
  • Learning analytics dashboard for instructors and administrators
  • Quarterly optimization plan tied to completion rate and learner outcome targets

Outcomes

Measurable outcomes

40-80% lift in course completion rates through adaptive pacing and personalized content
60-75% reduction in instructor grading time through AI-powered assessment and feedback
25-35% drop in student attrition through early warning and proactive intervention
50-70% faster content creation through AI-generated practice problems and supplementary materials
3-5 redundant tools dropped from the edtech stack through consolidation and direct LMS integration

Free Tools

Free tools for edtech

Calculators and assessments built from real project data. No signup required.

See all free tools

Pattern Transfer

RaftLabs built churn prediction models for a streaming media client before applying the same engagement-scoring approach to student dropout prevention. Both problems need behavioral signals turned into an intervention at the right moment. The domain changed from canceled subscriptions to abandoned courses. The pattern transfer was direct.

Services

Services for edtech

Frequently asked questions

Projects range from $50K-$200K. An adaptive learning module that plugs into your existing LMS starts around $50K. A full custom LMS with assessment, analytics, and content tools runs $120K-$200K. We give a fixed estimate after a strategy session.

Next Step

Every semester with 40% dropout rates is another semester of wasted potential and lost tuition.

One call with a founder. No sales team, no follow-up sequence. If we can't help, we'll say so.

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