The Challenge
A client needed to build a large-scale, AI-enabled arbitrage algorithm capable of aggregating data from global sources, enriching that data intelligently, and making decisions autonomously — all while handling highly variable workload patterns that could spike unpredictably.
The core technical challenges were:
- Data volume and variety: Aggregating data from diverse global sources in different formats, at different frequencies, with varying reliability
- Intelligent enrichment: Raw data needed to be enriched using generative AI, image processing, and pattern recognition before it could be used for decision-making
- Autonomous learning: The system needed to improve its own decision-making over time, not just follow static rules
- Cost efficiency: The workload was inherently bursty — idle for periods, then spiking to high throughput. Traditional always-on infrastructure would be prohibitively expensive
Our Approach
Data Aggregation Layer
We designed a global data aggregation system that pulls from multiple source types — APIs, web endpoints, and file-based feeds. Each source has its own ingestion adapter with retry logic, rate limiting, and data quality validation. The system normalises incoming data into a unified schema regardless of source format.
AI Enrichment Pipeline
The enrichment pipeline chains multiple AI techniques:
- Generative AI processes unstructured data and extracts structured metadata
- Image processing analyses visual data for classification and feature extraction
- Data clustering identifies patterns across seemingly unrelated datasets
Each enrichment step is modular and independently deployable, so individual components can be updated without redeploying the entire pipeline.
Reinforcement Learning Engine
Rather than relying on hard-coded rules, we implemented a reinforcement learning system that learns optimal strategies from outcomes. The RL agent continuously refines its approach based on reward signals from actual results, adapting to changing market conditions without manual intervention.
Burstable AWS Architecture
We designed the infrastructure around AWS services that scale to zero when idle and burst to high capacity on demand:
- Compute scales dynamically based on queue depth and processing demand
- Storage and data transfer are optimised for cost at variable volumes
- Monitoring and alerting track both system health and cost thresholds
This approach keeps infrastructure costs proportional to actual workload rather than peak capacity.
The Outcome
The system is in production, processing high-volume global data streams with generative AI enrichment and autonomous decision-making via reinforcement learning. The burstable architecture handles variable load patterns efficiently, keeping infrastructure costs aligned with actual demand rather than provisioned capacity.
Technologies Used
- AI/ML: Reinforcement learning, generative AI, image processing, data clustering
- Infrastructure: AWS (burstable compute, managed services), Docker
- Languages: Python
- Architecture: Event-driven, modular pipeline, independently deployable components