LOADING_SYSTEM...
WORK / CASE STUDIES
WEB APP
HIGH-THROUGHPUT ASSET MANAGEMENT FOR INSTITUTIONAL TRADERS.
YEAR / 2024
180ms
LATENCY SPEED
0%
ERROR RATE
01 / THE BRIEF
What needed
to be built.
CLIENT
Aura Finance Inc.
LOCATION
Miami, FL
DURATION
4 months
YEAR
2024
Aura Finance required a complex architectural overhaul of their core asset management engine. The existing infrastructure struggled with legacy code integration, leading to systematic latency during market hours.
Our mandate was to design and engineer a platform capable of processing millions of transactions per second while maintaining a sub-100ms latency bar for institutional traders.
"THE SYSTEM THEY HAD WAS COSTING THEM TRADES. WE ARCHITECTED A SOLUTION THAT RECLAIMED THEIR EDGE."
02 / THE CHALLENGE
The system they had
was costing them trades.
Legacy monolithic architecture created a latency problem that cost traders critical milliseconds. Older data-tech infrastructure that created a bottleneck in system responsiveness.
- •
Implementation of a generalist event-driven architecture for multi-asset trading
- •
Real-time WebSocket integration for 2,000+ systems
- •
Multi-region API deployment for zero downtime
03 / THE SOLUTION
Rebuilt from the
query layer up.
We restructured the query layer with a specialized microservices approach. The platform was rebuilt ground-up with stream processing at the core.
- •
Sub-100ms end-to-end execution latency
- •
Real-time distributed streaming for 2,000 systems
- •
Multi-region API deployment for zero downtime
04 / THE ALPHA ADVANTAGE
The Alpha
Advantage.
180ms
LATENCY SPEED
0%
ERROR RATE
4 months
TIME TO MARKET
05 / ARCHITECTURE
Platform Architecture.
We engineered a high-performance distributed system using a custom message broker and a specialized query layer. The architecture is designed for zero downtime and sub-ms internal communication.
06 / INTERFACE
User Interface Design.
The interface was designed with a focus on cognitive load reduction. We used a brutalist design language to create a high-clarity environment for complex technical work, prioritizing data visibility and structural hierarchy.
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