Bubo.AI delivers AI-driven pricing optimisation solutions, using value-based pricing and machine learning to help wholesalers and distributors boost net profits by over 20%. Their platform combines intelligent cost analysis, predictive modelling, and market responsiveness to deliver smarter pricing strategies.
Challenge
Bubo.AI wanted to migrate their Plotly open‑source graphing libraries from Microsoft Azure to Amazon Web Services (AWS) to take advantage of better scalability, cost efficiency, and performance. As a company that uses advanced AI and machine learning for dynamic pricing optimisation, they needed a cloud environment that could keep pace with their complex data processing demands.
Their services span AI‑driven cost analysis, market trend prediction, and pricing strategy adjustments for wholesalers and distributors. This created a high volume of complex data requirements, meaning the migration needed to ensure minimal downtime, optimised infrastructure performance, and a scalable foundation for future market expansion.
Solution
Using its Migration.core framework, Firemind migrated Bubo.AI’s application to AWS in just 12 days. The process included setting up an AWS Organisation, configuring accounts and security policies, and building a CI/CD pipeline for smooth deployment. AWS IAM Identity Center, Amazon GuardDuty, and AWS Secrets Manager ensured secure access and threat detection, while Amazon Fargate simplified container management.
To optimise performance and reliability, Amazon CloudWatch, AWS Trusted Advisor, and AWS CloudTrail were implemented for monitoring, best-practice guidance, and auditing. The staged migration, starting with test data, ensured minimal disruption and resulted in faster graph rendering, improved scalability, and reduced operational costs.
Services used
- AWS IAM Identity Center
- Amazon Fargate
- Amazon GuardDuty
- AWS Secrets Manager
The Results
- Migration completed in 12 days
- Scalable AWS infrastructure established
- Faster graph rendering and improved performance
- Lower operational costs with ongoing optimisation
