> Case Study
Tertulia
> SERVICES
Backend Development / Dedicated Team
> TECH STACK
Java Spring Boot, Python (custom AI model), React.js, Next.js, React Native, MongoDB, PostgreSQL, Redis, BigQuery, Looker, Fivetran, DBT, ChatGPT API, Claude API, Shopify, Stripe, SendGrid, Segment.io, AWS (EKS for Kubernetes, Auto Scaling)
> TIME
3
years and ongoing
> RESOURCES
6
including Tech Lead, Architect, Senior Developers and Frontend Designer
> ARCHITECTURE
Scalable Backend Architecture
Built a backend capable of managing tens of millions of books and hundreds of millions of tweets, ensuring Tertulia's platform can scale as it grows, without compromising performance.
> INFRASTRUCTURE
AWS Infrastructure with EKS for Kubernetes and Auto Scaling
Deployed the platform on AWS, leveraging EKS for Kubernetes for container orchestration and Auto Scaling to ensure the platform dynamically adjusts to fluctuating traffic and data loads.
> AI
Proprietary AI Model
Developed a custom AI model in Python, providing Tertulia with enhanced control over user interactions and recommendations, complementing third-party AI services.
> AI
AI-Powered Book Recommendations
Integrated ChatGPT and Claude APIs to deliver real-time, AI-driven book recommendations, improving user retention and engagement.
> DATA
Efficient Data and Transaction Management
Built a robust system for real-time transaction management and efficient handling of user data, ensuring performance even during high traffic periods.
> PERFORMANCE
High-Performance Data Infrastructure
Optimized the platform using BigQuery for analytics and Redis for caching, ensuring the platform's consistent performance and responsiveness.
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01.
Project & Business Overview
Tertulia is a social-driven online bookstore, designed to help users discover books through vibrant community discussions. From the beginning, SixSides IT has played a crucial role in developing Tertulia's scalable backend architecture, ensuring the platform's growth while managing vast data volumes. We integrated advanced AI services like ChatGPT and Claude to enhance the user experience with personalized book recommendations and developed a custom AI model in Python. Furthermore, we implemented AWS infrastructure with EKS for Kubernetes and Auto Scaling, allowing the platform to dynamically adjust to user traffic and data processing needs.
02.
Challenges
Handling Large Data Volumes
Tertulia processes tens of millions of books and hundreds of millions of tweets, requiring a backend that supports massive data processing without compromising performance.
AI-Powered Recommendations
Integration of ChatGPT and Claude APIs to deliver personalized book recommendations, improving user experience and engagement.
Scalability and Performance
The platform needed a highly scalable system to manage rapid user growth, data flow, and unpredictable traffic spikes, requiring robust infrastructure with dynamic scaling.
Custom AI Model
Beyond third-party AI services, a custom Python AI model was built to analyze user interactions and deliver tailored content, offering a more personalized user experience.
03.
Work Approach
Agile Collaboration
We adopted an agile development process, collaborating closely with Tertulia's internal teams to ensure rapid feature delivery and alignment with business objectives.
Technical Implementation
Scalable Backend Architecture
Using Java Spring Boot for microservices, MongoDB for unstructured data, and PostgreSQL for relational data, we developed a flexible, scalable backend capable of supporting high volumes of data and user activity.
Infrastructure and Auto Scaling
Deployed the platform on AWS, utilizing EKS for Kubernetes for container orchestration and Auto Scaling to automatically adjust resources based on user traffic and data load, ensuring consistent performance and cost efficiency.
AI & DATA
AI Integration and Custom AI Development
Integrated ChatGPT and Claude APIs to power personalized recommendations while developing a custom AI model in Python to allow deeper control over recommendation strategies and user interactions.
Real-Time Data Management
Leveraged Redis for fast data caching and BigQuery for large-scale data analytics, with Fivetran and DBT automating data pipelines to ensure clean, structured data flow into BigQuery.
eCommerce and Payments Integration
Integrated Shopify for book sales management and Stripe for secure payment processing, providing users with an efficient shopping experience.