EdTech Platform Optimization: 70% Fewer Bugs and 40% AI Cost Reduction

+40%
Performance
−40%
AI Cost
−70%
Bugs
−60%
Downtime

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6 to 8
Dedicated Engineers
Dedicated Team
Engagement Model
Main Technologies
PHP Laravel React JS Java Angular
Ed-Tech
Industry

The Context: The Before State

We helped Language Academy fix a slow and unstable learning platform while rebuilding it for the future, without disrupting their day to day operations.

Language Academy is an Australia based education company focused on language learning. With a growing user base and increasing reliance on digital delivery, their website had become the core of their business. 

For them, the platform was not just a website. 

It was the classroom, the assessment system, and the customer experience all rolled into one. 

The Clients Problem

Over time, the existing system began to show cracks. 

Multiple bugs were affecting daily usage. 

Several features were not behaving as expected. 

API responses were slow, frustrating both users and internal teams.

On the backend, the application was executing duplicate database queries for common operations. This caused unnecessary load and poor performance at scale. 

At the same time, the platform relied heavily on external AI APIs. These APIs were powerful, but expensive. Costs were rising quickly and replacing them with in house AI models was not realistic without compromising accuracy. 

To make things more complex, Language Academy was planning a full website renovation. While the new platform was being built, the old one still needed to stay stable and usable. 

The Goal 

Success was defined very clearly. 

Keep the existing platform running smoothly without disruption. Fix critical bugs and performance issues. 

Reduce AI related operational costs. Deliver core learning modules for the new platform on time. Ensure the team could confidently launch before the event. 

client

Anthea Strezze

Reliable partner with broad technical and design expertise!

warning
The Challenges

A growing language learning platform struggled with bugs, slow API performance, rising AI costs, and a tight deadline for a complete website renovation.

setting
The Solution

A dedicated development team stabilized the existing platform, optimized backend performance, reduced third party AI dependency, and delivered core modules in parallel with the new website build.

result
Result

40 percent performance improvement, 40 percent cost savings, 70 percent fewer bugs, and a stable platform ready for a major public facing event.

The Hardest Challenge

The platform was central to the business. It handled learning, assessments, and user experience across a growing user base.

Over time, the system began to show strain.

  • Multiple bugs disrupted daily operations
  • API response times slowed down user workflows
  • Duplicate database queries increased system load
  • AI API dependency caused rising and unpredictable costs
  • System stability became unreliable under scale

At the same time, a full website rebuild was already planned. The problem was not just technical. The existing platform had to stay stable while the new one was being built. There was no room for disruption.

Success Was Clearly Defined
  • Keep the existing platform stable without downtime
  • Fix critical bugs affecting user experience
  • Optimize backend performance and database efficiency
  • Reduce AI related operational costs
  • Deliver core modules for the new platform before the event
  • Ensure confidence in launch readiness

The Solution

“Stability before rebuild.”

Instead of jumping into a full rewrite, the focus was on strengthening what already existed.

Execution followed a dual track approach.

One track stabilized and optimized the current platform
The other delivered core modules for the new website

Work was executed in controlled phases, ensuring every improvement was validated before moving forward.

Laravel
React JS
React Native
MySQL
REST APIs

The Outcome

The platform moved from instability to controlled performance.

  • Performance improved across user workflows
  • Bug frequency dropped significantly
  • AI costs became predictable and controlled
  • System downtime reduced noticeably
  • Core modules were delivered on time
40%
Performance Improvement
40%
AI Cost Reduction
70%
Reduction in Bugs
60%
Reduction in Downtime
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