A solution designed to modernize and scale data infrastructure, unlocking better decisions across the organization.
As companies grow, so does the complexity of their data. Fragmented sources, inconsistent pipelines, and legacy systems often lead to slow, manual reporting and limited visibility. This solution was created to solve that challenge—by establishing strong, AI-enabled data foundations built for scale.
The challenge
Many organizations operate with outdated or incomplete data ecosystems, making it difficult to extract value from their data. Teams waste time managing fragmented datasets, face reporting inconsistencies, and lack the agility to adapt their data systems to new business needs.
The solution
This solution delivers a three-step approach to modern data architecture:
- Assessment of current infrastructure: A technical and functional evaluation identifies inefficiencies, bottlenecks, and missed opportunities.
- Implementation of cloud-native data platforms: A centralized Data Lake and a serverless Data Warehouse on Amazon Redshift provide scalable, secure storage and analytics capabilities.
- AI-driven data classification: Using Amazon SageMaker, the solution deploys a machine learning model to classify and organize supplier data, enabling better purchasing decisions and vendor management.
The architecture
Built entirely on AWS, the solution includes:
- Amazon S3 and AWS Glue for data lake storage and preparation
- Amazon Redshift Serverless for fast, scalable querying
- Amazon SageMaker for custom model training and deployment
- AWS Lambda and Step Functions for automated workflows
The results
- 360° data visibility across departments
- Up to 50% reduction in time spent preparing and querying data
- Smarter decision-making through AI-enabled supplier insights
- A flexible, future-proof architecture ready for advanced analytics and AI
By combining cloud scalability with machine learning, this solution transforms data into a strategic asset—no matter the size or industry.
