Wordego and Sortext now have more scalable, cost-effective and reliable environments on AWS

Wordego & Sortext


Wordego and Sortext now have more scalable, cost-effective and reliable environments on AWS

Wordego & Sortext

About the customer

Sortext and Wordego are sister start-up companies in e-commerce industry. Sortext is an AI-based ecommerce conversion platform that provides online retailers to double the conversion of their first-time visitors with no additional investment. Wordego is an intelligent customer classification solution for ecommerce websites. Wordego, being an Advertisement Analytics Platform, segments all visitors by their spending power and conversion probability (Customer Score), such as “Gold”, “Silver”, or “Bronze” visitors, who can be further used in advertising platforms as a target audience for paid search or remarketing campaigns. 

Parallel to each other, two products exchange data to calculate customer value, website visiting patterns, calculate customer score, display dynamic campaigns to keep customers on the site, apply discounts, and track customers. 

Customer challenge

The prediction models of the companies’ products were running on Windows instances and were serving all customers’ websites together as a load balanced solution. Yet, the structure was not flexible enough for code development purposes and was not secure enough to protect company IP and prevent external attacks. It was required to revise current IT architecture by considering the following requirements:

  • Replacing existing MS SQL database with Aurora RDS to secure data access and enhance backup needs.
  • Separating servers used for Sortext and Wordego and making them run on independent AWS infrastructures.
  • Rebuilding code structure and make prediction models accessible as micro-services running behind AWS Lambda (or any other) functions and API gateway.
  • Enabling the access to micro-services from different applications to expand our services’ usage in mobile apps in the future.
  • Monitoring system health for Quality Assurance purposes. (Replacing Site24 setup with AWS Cloud Watch)
  • Running stateless application on ECS which provides better scalability and cost optimized solution based on the workload.
  • Securing system to prevent DDOS attacks or unauthorized access by internal human sources. (IAM roles, Firewall setup, Security Groups, etc.)
  • Modifying DynamoDB data flow (added Kinesis streams triggered on DynamoDB object entry) to be able to both satisfy data processing speed requirements and analysis of acquired data.
  • Automating and scaling the update of prediction models by replacing SageMaker notebooks with more sophisticated product (EMR) that can correspond to scalability and automation needs.

How the solution was deployed to meet the challenge

The delivery of the solution was planned in two main phases: discovery and implementation. The findings were documented with architectural recommendations during the discovery phase. Implementation phase as the name implies, was the phase when the agreed solution was deployed. 

The solution’s components were: 

  1. Preparation of current and new architecture diagrams with AWS Services decided to be used during discovery phase.  
  2. Migration of current MS SQL Database with AWS Aurora RDS to meet functional and non-functional requirements using Amazon DMS 
  3. Moving some of the services / functions of Sortext and Wordego applications to serverless and auto scalable compute (AWS Lambda and Fargate) environments. 
  4. Changing CD processes to deploy API services on AWS ECS 
  5. Setting up monitoring and alarm systems for new architecture 
  6. Making suggestions for convenient AWS Security Services and/or TrendMicro products to mitigate security threads and audit requirements with cost information 
  7. Addressing current performance issues encountered in DynamoDB batch processes. 
  8. Analyzing and making suggestions for automating the update of production models by triggering different serverless compute functions that are linked to SageMaker notebooks. 

Third party applications or solutions used




AWS Services used as part of the solution

Database Migration Service (DMS), Virtual Private Cloud (VPC), Identity & Access Management (IAM), Elastic Container Service (ECS), Elastic Container Registry (ECR), Elastic Load Balancing (ELB), DynamoDB, Route 53, EC2, Key Management Service (KMS), Secrets Manager, CloudWatch, Simple Queue Service (SQS), Simple Storage Service (S3), AWS Certificate Manager (ACM), CodePipeline, CodeBuild, CodeDeploy, Lambda, Sagemaker, Relational Database Service (RDS), Glue, Kinesis, CloudFront 

Architecture Diagram of the specific customer deployment

Technical Requirements

High level requirements were defined as:

  • Transferring Microsoft SQL Server database to Amazon RDS PostgreSQL
  • Modernizing existing application components from classical server based to microservices architecture with serverless computing services (Lambda, ECS, …)
  • Build a CI/CD pipeline for redesigned architecture
  • Enhance security and observability


Following project delivery, Wordego and Sortext AWS environments have been replaced with scalable, cost efficient, and reliable AWS serverless compute services. In addition, CI/CD pipelines with AWS services are established, and the database is changed to AWS RDS PostgreSQL.

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