Contact us
Book a call

How Moveally Scaled Online Dance Training for a Global Virtual Dance Academy With a Custom AI Dance App That Improved Student Learning by 40%

Book a call with our AI experts

Impact

70%

Student performance analysis and feedback automated

40%

Improvement in student learning outcomes

30%

Instructor time saved

Project Overview

ACKTEC Technologies, an early-stage EdTech company based in Singapore, wanted to build a global virtual dance academy that could deliver expert coaching to home learners without requiring a live instructor for every session. Their existing approach, pre-recorded video classes and live video calls, did not scale. Students had no real-time corrective feedback, instructors spent hours on repeat manual corrections, and progress was nearly impossible to measure objectively.

Tezeract was engaged to design and build Moveally, a custom AI dance app and virtual dance learning platform that uses pose detection, similarity scoring, and style-aware AI analysis to give every student clear, instant, personalized guidance, at home, on any device, at any time.

What Changed

Instructors upload one master video. Students record their take at home. The AI dance tutor compares both, scores every joint and movement, and delivers targeted coaching tips instantly. Instructors review, add a short personalized note, and move on. The result is an online dance training model that grows without growing the instructor headcount.

Dance Motion Tezeract

Customer Profile

ACK Technologies is an early-stage EdTech company that builds consumer learning products. For this project, the product was named Moveally, a custom AI dance app that brings a virtual dance academy to home learners worldwide. The business model is B2C, with a focus on simple, effective online dance training accessible from any device.

Client Name

Ivan Ng

Industry

EdTech, Entertainment, Dance

Business Model

B2C — direct-to-consumer virtual dance learning

Location

Singapore (global online delivery)

Target Audience

Home learners, hobbouyists, and students seeking expert dance guidance

Decision Maker

Founder & CEO

Company Stage

Early stage — consumer product, global reach

Pain Point

No real-time corrective feedback, no objective progress tracking, instructor time not scalable

Prior Tech Stack

Pre-recorded video classes, live video calls, manual instructor review

Why This Matters for Buyers Like You?

If you run a dance studio, fitness platform, or any skills-based learning product where instructor time is the bottleneck, ACKTEC Technologies’ situation before Moveally will look familiar. The challenge of delivering personalized, expert-quality feedback at scale is not unique to dance, it applies to any movement-based learning category. 

The AI dance app Tezeract built for Moveally is designed to solve exactly that problem, and the architecture scales to new styles, new markets, and new learner volumes without rebuilding from scratch.

Book a call with our AI experts
sub heading component

The Challenge

Scaling Expert Dance Coaching Without Scaling the Instructor Headcount

Dance Motion Tezeract

01

Primary Problem

ACKTEC Technologies set out to deliver online dance training that feels like expert coaching. The primary problem was scale. Home learners received no real-time corrective feedback, and instructors spent long hours on repeat manual corrections. Progress was hard to measure objectively, which led to poor engagement and weak retention.

Secondary Challenges

Virtual dance class issues

Uneven video quality across devices, no standard scoring rubric, and inconsistent feedback quality between instructors

02

Dance teacher workload management

Instructors spending hours on repetitive manual review of student recordings with no tools to batch or automate

03

Dance choreography learning problems

Students struggling to memorize sequences without a clear, objective breakdown of where they were going wrong

04

Online dance learning challenges

Low engagement, home practice difficulties, and high drop-off rates without visible progress milestones

05

No AI dance tutor layer

Existing tools were either passive video players or generic fitness apps, none offered pose-based scoring or movement similarity analysis

06

Reduce Instructor Workload With AI-Powered Feedback

Stop relying on repetitive manual corrections. Build a system where your instructors upload once and AI handles scoring, feedback, and progress tracking across thousands of students.

Automate My Dance Platform

What Slowed Down Operations and Triggered the Need for Immediate Change

Previous Solutions Tried

  • Off-the-shelf LMS tools with no pose analysis or similarity scoring
  • General fitness apps and basic spreadsheets for tracking, not fit for an AI dance app use case
  • Live video calls with manual corrections that did not scale beyond a small cohort
  • Pre-recorded video classes without any feedback mechanism

Business Impact

The company had high instructor time on repeat reviews and operational inefficiencies across content review and class management. This led to lower student retention due to a lack of visibility into progress and weak feedback loops.

Urgency Factors

  • Rising demand for an AI dance app that offers clear, instant guidance
  • Pressure to improve retention and learning outcomes before scaling paid growth
  • Need to launch a global virtual dance learning model without hiring an instructor team
  • Competitive pressure from consumer apps positioning themselves as AI choreographer tools, without the objective scoring that genuine online dance training requires
Dance Motion Tezeract
sub heading

Journey Overview

Why Tezeract

ACK Technologies ran a structured search for a way to scale online dance training without losing expert guidance. The team mapped learner needs, spoke with instructors, and ran small trials to test whether an AI choreographer model could work for B2C home learners. 

 

They asked a critical question early: 

  • Should the system try to AI choreograph a dance, or should it act as a precise AI dance tutor that scores and guides in real time? 

 

The answer shaped the entire product direction. They needed a custom dance system that gives clear, objective feedback, not a one-size-fits-all content app.

Following was the evaluation criteria for winning the project

  • Feedback accuracy and stability across devices and lighting conditions
  • Time to give feedback for home dance practice, must feel instant
  • Progress tracking with clear, objective metrics to support retention
  • Admin effort for class setup and student review
  • Privacy and control of student data

Why Tezeract won the evaluation?

The leadership was looking for someone whose domain expertise was in pose detection, similarity scoring, and real-time dance correction, not a generic CV vendor. Tezeract had a flexible web stack with room for growth, plus a shared delivery roadmap and clear IP ownership. We also have experience in building custom AI tools that act as a reliable AI dance tutor, with clear scores, per-joint tips, and style-aware checks

sub heading component

The Solution

Moveally - A Custom AI Dance App for Scalable Virtual Dance Learning

Dance Motion Tezeract
Tezeract built Moveally as a fully integrated AI dance app for virtual dance learning that follows a simple, repeatable loop. Instructors upload a master video once. Students watch, record their take, and the AI dance tutor compares both to give clear, real-time guidance. Instructors can then generate a personalized response and a follow-up video for each student.  This reduces repetitive manual corrections, improves progress tracking, and supports online dance training at scale, without adding more tools or more headcount.

How Navex Works

When a student records their practice session, the Moveally AI dance app processes the video through a multi-stage analysis pipeline. YOLO detects and isolates the dancer in the frame. MediaPipe extracts skeletal keypoints for every joint. OpenCV handles frame processing and timing. The AI server then compares the student’s time-series joint data against the instructor’s pre-computed reference keypoints, applies L2 normalization to remove scale and position bias, and generates a similarity score with per-joint coaching tips. 

The result is delivered back to the student as a clear, actionable AI dance tutor response, not a generic score, but specific guidance on which joints to correct and how.

Key Capabilities Built

Dance Motion Tezeract

AI Dance Tutor Engine

Rule-based coaching messages mapped to specific joint errors and movement patterns, acts as a reliable AI dance tutor for every style

Dance Motion Tezeract

Pose Detection & Similarity Scoring

MediaPipe skeletal keypoint extraction + time-series comparison across joints to compute similarity scores

Dance Motion Tezeract

Style-Aware Checks

Per-style thresholds and joint weighting for Ballet, two-step, hip hop, and custom dance styles.

Dance Motion Tezeract

Student Web App

Per-style thresholds and joint weighting for Ballet, two-step, hip hop, and custom dance styles.

Dance Motion Tezeract

Instructor Console

Batch review queue, student score breakdown, note fields

Dance Motion Tezeract

Progress Dashboards

Trend lines, completion states, and accuracy scores per move and per routine give clear visibility into online dance training progress

The Data Flow

Dance Motion Tezeract

Bring Real-Time Dance Feedback to Your Platform

Give your learners instant corrections, clear scores, and structured progress tracking. We build AI dance tutor systems that make online dance training engaging, measurable, and easy to scale.

Let's create my app

Phases wise Deployment

We rolled out the Moveally AI dance app in four clear phases, with a tight feedback loop between Tezeract and ACK Technologies at every stage.

01

Proof of Accuracy

Validated the core AI dance tutor pipeline on sample Ballet, two-step, and hip hop clips. Tested pose accuracy across common webcams and phones. Confirmed similarity scoring stability across varied lighting and home environments.

Key milestone: POC passed on core styles. Pose detection accuracy confirmed across target devices.

Dance Motion Tezeract

02

MVP Build & Pilot Launch

Built the student web app with upload, capture, scoring, and basic feedback. Deployed the AI server with the full analysis pipeline. Ran a pilot with a small cohort of home learners across devices.

Key milestone: MVP live to pilot. First real student recordings processed and scored by the AI dance app.

03

Instructor Console

Built the instructor console for batch review, per-student score breakdown, note fields, and personalized response video rendering. Integrated progress dashboards for students and instructors.

Key milestone: Instructor console in place. Dance teacher workload management tools live and tested.

Dance Motion Tezeract

04

Scale, Harden & Content Workflows

Added monitoring, caching, and content workflows to support growing student volumes. Optimized async processing for low-latency feedback on typical home networks. Finalized style-aware checks for all supported dance styles.

Key milestone: Beta at scale with content workflows. Platform ready for paid growth and new style onboarding.

Dance Motion Tezeract

Obstacles Countered and Resolved

Obstacles

Real-time video processing with low latency on typical home networks

Feedback accuracy and clarity for beginners who had never used a scoring app

Noisy inputs from varied home environments (lighting, camera angle, background clutter)

Instructor adoption of the new console workflow

Adapting AI scoring to different dance styles with distinct movement rules

Dance Motion Tezeract

Resolution

Async processing and progressive feedback delivery, student receives first tip within seconds of upload, full analysis follows

Clear, short coaching tips mapped to specific joints and moves, paired with example clips, no technical jargon

Smoothing, L2 normalization, and lighting checks applied at the preprocessing stage to stabilize keypoint extraction

In-app walkthroughs, quick-start guides, and a simple rubric for instructors

Style-aware checks with per-style thresholds and joint weighting. Ballet scoring logic is separate from hip hop scoring logic

Dance Motion Tezeract

The Results

The Moveally AI dance app moved manual review to automated, reference-based scoring. Instructors upload one master video. Students record at home. The AI dance tutor compares both and gives clear tips per joint and move. Instructors add a short, personalized response or a follow-up video when needed. 

This raised completion rates and kept learners engaged across the platform.

70%

Of student performance analysis and feedback fully automated

40%

Improvement in student learning outcomes, measured by accuracy scores and lesson completion rates

30%

Reduction in instructor time spent on repeat corrections and admin

Before Moveally, dance students learning online had no way to know if their posture was correct, their timing was off, or their form needed work. Instructors had no tools to monitor progress between live sessions.

That gap is closed.

For Students

1

Submit a practice video and receive posture and movement feedback immediately

2

Understand exactly what to correct before the next session

3

Progress through choreography at their own pace with AI guidance

4

Build technique consistently, not just during live classes

For Instructors

1

Monitor student submissions and performance without reviewing every video manually

2

Identify which students need extra attention before the next class

3

Spend live session time on creative direction, not basic corrections

4

Manage a larger student base without increasing personal workload

For Studio Owners

1

Offer structured online training without being limited by physical space or geography

2

Retain students longer through visible, measurable progress

3

Scale enrollment without hiring additional instructors for every new cohort

4

Build a reputation for quality online dance education backed by real results

Build Your Own AI Dance App That Scales Coaching

If your instructors are spending hours reviewing videos and students still lack clear feedback, it is time to rethink your model. We help you build an AI dance app with pose detection, similarity scoring, and real-time feedback so your platform can grow without increasing instructor workload.

Build My AI Dance Platform
sub heading component

Tech stack used AI-based app case study?

Building Moveally with Our Cutting-Edge AI Tech Stack

React , React Native cross-platform framework icon, React JavaScript library logo

React js

Python programming language for AI development

Python

Next.js React framework icon

NodeJs

Flask Python microframework icon

Flask

FastAPI modern Python framework logo

FastAPI

EasyOCR Icon

EasyOCR

Docker - open-source platform for deployment

Docker

MongoDB NoSQL database logo

MongoDB

Tools & Technologies

Description

Webapp Development

  • The user and admin web apps have both been built on React.

Backend Development

  • Implemented using NodeJS, ensuring robust and modular service management.

AI Server

  • Python with Flask was used to develop the AI server.
  • Utilized OpenCV for video processing and frame reading
  • Applied person detection using pretrained YOLO model.
  • Utilized the MediaPipe pose detection module to analyze the body posture
  • Apply L2 normalization to standardize the detected points
  • Handled the data in a time series to compare and calculate similarity between each body joint point

Database Management

  • Used MongoDB for reliable, scalable, and efficient data storage.

Cloud Infrastructure

  • Deployed on Digital Ocean, providing a secure, scalable, and cost-effective cloud environment.

Development Tools

  • Leveraged Visual Studio Code (VS Code) as the primary IDE for collaborative development, debugging, and version control.

Key Features

Dance Motion Tezeract

01

Expert-Led Virtual Dance Lessons

Learn in a virtual dance academy with pre-recorded classes from top instructors. The AI dance app removes travel and scheduling conflicts, making expert online dance training accessible to home learners worldwide.

Dance Motion Tezeract

02

Real-Time AI Dance Correction

The AI dance app compares your moves to the instructor video and acts as a real-time dance correction app. It runs on an AI dance feedback platform that gives clear similarity scores even with home dance practice difficulties and typical virtual dance class issues.

Dance Motion Tezeract

03

Personalized Feedback from Your AI Dance Tutor

Get simple, targeted advice from an AI dance tutor and a human dance coach. This reduces dance teacher workload management and improves student retention by showing progress in plain scores and weekly goals.

Dance Motion Tezeract

04

Custom Dance Styles and Skills

Style-aware checks for Ballet, two-step, hip hop, and custom dance styles. The AI choreographer layer flags dance choreography learning problems by joint and timing.
sub heading component

What potential use cases of dance app?

Benefits of AI-based dance coaching

Let's create my app

Scalable expert guidance

One master video supports many students. Less manual review and fewer repeat sessions for instructors.

01

Faster, clearer progress

Real-time scores and simple tips raise engagement and reduce drop off. Better visibility improves student retention in dance studios.

02

Lower workload and cost

Automation cuts repetitive corrections and admin. Teams reallocate time to new content and growth.

03

Flexible for busy schedules

Learn at home on your own time. No travel, fewer live call dependencies, fewer scheduling conflicts.

04

Consistent and objective feedback

The AI dance app compares to the reference video and removes guesswork. This reduces online dance learning challenges and keeps goals clear.

05

Ready for growth

Works across styles and devices with room to add LMS links if needed. This supports scale without adding operational risk, as shown in this AI dance app case study.

06

Launch a Custom AI Dance App with Tezeract

Moveally shows what’s possible when AI is built around your specific learning model, not retrofitted from a generic tool. If you’re running a dance studio, fitness platform, or any movement-based learning product and want to reduce instructor workload, improve student retention, and scale your online dance training without scaling your team, let’s talk.

Book my free call
sub heading component

Your questions answered here

Frequently Asked Questions

Most studios track learner progress speed, session completion, and retention. In pilots we set a baseline and aim for faster choreography mastery, higher weekly practice time, and lower churn. A typical 8 to 12 week pilot targets a lift in tracked metrics, for example a reduction in repeat errors, an increase in completed lessons, and better attendance for live sessions once learners see progress. Instructors report fewer manual corrections and more time for creative work. Studio owners track lower support tickets and leaner operations tied to scheduling and reporting.

The system reads body keypoints from video and compares motion against reference moves. It flags timing, angle, and sequence errors. We tune sensitivity by style and level to avoid noise. Accuracy depends on lighting, camera placement, and device capability. In practice, the goal is consistent guidance that reduces repeat mistakes, not perfect judging. We pair automated corrections with short human reviews for edge cases. This balance cuts teacher workload while keeping quality high.

Yes. The tutor suggests warmups, step difficulty, and rest intervals based on recent strain and session duration. It flags risky ranges for knees and back and recommends form checks. Home learners get a safer path with guided pacing, which reduces frustration and supports steady gains. Studios receive alert trends to refine content.

Recommendations use three inputs. First, learner skill gaps based on corrected errors. Second, engagement signals such as watch time and replays. Third, instructor goals for each course. The engine proposes the next lesson, a drill, or a slower tempo track. Leaders can set rules for business goals such as promoting new styles. This improves practice consistency and reduces time spent choosing what to learn next.

We log error types, correction acceptance, tempo control, and completion of drills. Progress is shown as a simple score per move and per routine. Learners see small wins each week. Instructors can compare cohorts and update lesson design. Studios tie these metrics to retention goals and offers for re-engagement.

Multi-location studios use it to extend reach without new venues. Smaller schools add advanced styles taught by guest pros. Companies run group wellness tracks for employees with set schedules and reports. These use cases raise attendance by removing travel and scheduling barriers and by showing clear progress.

We integrate with video libraries and bring in metadata like level and style. The system reads previous completions and proposes an updated path. Learners can remain on the current platform for playback while the tutor layer adds feedback and metrics. This reduces change management and keeps your existing content valuable.

Short feedback loops and weekly targets keep attention high. The app celebrates small wins, suggests the next drill, and recommends content that matches recent progress. Group challenges and light leaderboards add social energy without pressure. This helps shy learners stay active and reduces drop-offs.

Tezeract Logo White

We help businesses by automating their processes and developing customized end-to-end AI solutions that deliver proven ROI.

Let's Talk

Services

Industries

Portfolio

Quick Links

Explore AI Summary

ChatGPT Perplexity Claude Gemini Google AI Mode Grok

Find us on

Facebook Instagram Linkedin Youtube Reddit Pinterest Medium Slideshare
DMCA.com Protection Status

©2026 Tezeract | All Rights Reserved.

WhatsApp