The Challenge
Arabesque AI needed elastic scaling to meet rapidly increasing demand for AI technology solutions in financial markets. Their hybrid infrastructure model created capacity and maintenance issues that would consume too many resources as they scaled. Researchers were spending substantial time on DevOps management instead of core AI research.
The Solution
Arabesque AI migrated its entire infrastructure to Google Cloud, leveraging Google Kubernetes Engine for orchestration, Cloud Run for serverless computing, BigQuery as a scalable data warehouse, and various other Google Cloud services. The platform processes data from multiple sources including ESG metrics and market data to power their proprietary AI engine.
Results
- Increased data streaming and analysis capability by 10x
- Reduced server costs by approximately 75% using preemptible instances and pay-per-use services
- Reduced new employee training time by half while doubling team size in less than a year
- Redirected most resources from operations to research and development
As a growing company, research is a core business objective for us. With Google Cloud, most of our resources go toward research and only minimal effort is spent on operations. That's the opposite of what we had before and means we can improve our platform at a much faster rate.
Nikolaos Kaplis, CTO, Arabesque AI
AI Engine and Data Processing
Arabesque AI's proprietary artificial intelligence engine runs on GKE nodes, scaling up to use thousands of cores when training new models and scaling back down afterward. Preemptible node pools make this process cost-effective and easy to manage. The AI engine creates new signals from market information stored in BigQuery, often expanding data to several times its previous size. This combination of proprietary signals and existing market data constructs new investment strategies.
Partnership with DoiT International
After completing the migration, Arabesque AI worked with Google Cloud Premier Partner DoiT International to optimize their infrastructure. DoiT simplified the billing process and provided excellent guidance on the migration to BigQuery, saving two days of work. The partnership provided access to additional support and engineering expertise when needed.
Results and Business Impact
The migration enabled Arabesque AI to redirect team focus from operations to research objectives. They increased data streaming and analysis capability by 10 times while reducing server costs by approximately 75%. The company more than doubled in size in less than a year while reducing training time by half. Using open source technology on Google Cloud made it easier to hire and train new talent in a developer-friendly environment.
Future Innovation
Arabesque AI is developing Knowledge Graph, a project to extract insights from unstructured information like news and social media feeds. Running on Dataflow, it will automatically extract relevant information from text sources. The goal is to take unstructured text and identify companies, relationships, and signals necessary to improve predictions, building on natural language processing algorithms to create natural language understanding.
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