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The Codest Value for the Client:

How The Codest Helped Bright Launch a Scalable EdTech Platform

Bright is a digital-only bookstore making education more affordable and eco-friendly with e-books and AI-powered study tools. Students can buy or rent books through a web app, and new AI features let them chat with a smart assistant about their books, generate flashcards, and study more effectively.

  • Industry
  • EdTech
  • Headquarters
  • Stockholm, Sweden
  • Tech stack
  • Next.js
  • Nest.js
  • AWS

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Project Description Challenges Approach & Results

Project Description

Bright’s platform is a large-scale e-learning web application that integrates a digital bookstore with AI-powered study tools. The system allows students to buy or rent books while including AI features such as interactive learning assistants and automated flashcard creation. To support its growing user base and ensure efficiency, we optimized AWS server costs, developed new frontend and backend functionalities, and implemented an automated book import system. 

Challenges

  • Scalability & Performance – Ensuring the platform remains responsive and efficient as the number of users and AI-driven interactions increase.
  • AWS Cost Optimization – Reducing infrastructure expenses while maintaining high availability, reliability, and performance.
  • Automated Book Import System – Developing a robust and scalable solution for importing books from multiple publishers while minimizing errors and ensuring data consistency.
  • Frontend & Backend Coordination – Establishing an efficient communication framework between the frontend and backend to support AI-powered functionalities without delays or inconsistencies.
  • Technical Expertise & Implementation – While Bright had a well-defined product vision, executing a scalable and fully functional platform required specialized technical knowledge and strategic development.

Approach & Results

We focused on developing a scalable and well-structured product that seamlessly aligned with our business goals. With The Codest’s technical know-how and product expertise, we ensured our solution was both functional and built for future growth.

We handled both frontend and backend development, creating a responsive and efficient platform tailored to our needs. Our backend was designed for scalability and performance, allowing it to support large volumes of users without compromising speed or stability. With our senior engineers and strategic advisory, we successfully transformed our vision into a high-quality, market-ready product.

Client’s opinion:

The Codest combines top-tier know-how and talent with a strong product background and senior developers who truly understand business challenges. Their expertise, reliability, and CTO advisory were crucial in building our scalable product.

GUSTAF STRAUSS

Co-Founder at Bright

Full Case Study

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FAQ
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How long did it take to implement the solution?

The Codest worked swiftly with Bright to launch the scalable EdTech MVP, typically delivered within 3 to 4 months, covering architecture, backend, frontend, and initial optimizations.

What specific results did the client achieve?

– A robust, scalable platform supporting large user volumes – Streamlined content management, including digital books and resources – Optimized AWS infrastructure, reducing hosting costs while improving reliability – Automated operational workflows for admins and educators – Positive user feedback on performance and usability

Can this solution be applied in other industries?

Yes – the same scalable architecture, AWS optimization, and user-focused design approach can be applied to other growth-driven sectors like E-commerce, HealthTech, and Marketplaces that require cost-efficient scaling and seamless platform management.

What technologies were used in this project?

– Backend stack (likely Node.js or Ruby on Rails) powering API and data services – React or Next.js frontend for responsive user experience – AWS cloud infrastructure with cost-optimization (auto-scaling, serverless) – CI/CD pipelines and automated testing – Monitoring and logging systems (e.g., CloudWatch, Elasticsearch)

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