Challenge
Axion Capital's legacy monolith was processing only 25K daily transactions with frequent latency spikes during peak hours. They needed to 3x throughput while meeting FINTRAC regulatory compliance, maintain audit trails on every transaction, and reduce infrastructure costs.
Solution
Mapletech Labs rebuilt the platform from ground up using event-driven microservices on AWS Fargate. We integrated a custom TensorFlow ML model for real-time trade signal generation, implemented PostgreSQL with row-level security, and built a dedicated compliance microservice. Result: sub-50ms latency, institutional-grade reliability.
Results
Platform now handles 75K+ daily transactions with 99.99% uptime, 50ms average response time, zero failed orders, and 40% lower infrastructure costs. Delivered in 5 months. Client went from infrastructure firefighting to 100% focus on product and growth.
The Challenge
Building for Speed, Scale, and Compliance
Axion Capital, a fast-growing Toronto-based trading firm, had outgrown their initial infrastructure. Their monolithic Node.js application, built in-house 4 years prior, could process only 25K transactions daily. During peak trading hours (9:30-10:30 AM EST), latency frequently spiked to 800ms-1000ms, causing trade orders to fail and eroding client confidence. The CEO was personally managing infrastructure crises instead of growing the business.
Beyond performance, regulatory compliance was a critical blocker. FINTRAC required complete audit trails on every transaction, with proof of compliance for every single trade. The old system had fragmented logs spread across multiple systems, making audit reporting a nightmare. Additionally, the platform needed to support real-time sentiment analysis and trade signal generation using ML — something their existing monolith couldn't handle without grinding to a halt.
The client needed to integrate with 5 third-party market data providers, 2 payment processors, and 3 banking APIs — all while maintaining a seamless experience for both retail and institutional traders. The architecture had to be cloud-native, horizontally scalable, and resilient to market data provider outages.
Our Solution
Event-Driven Architecture with AI at the Core
We designed an event-driven microservices architecture deployed on AWS ECS with Fargate for elastic scaling. The system is composed of 7 independent microservices: (1) Order Ingestion Service (Node.js/Express, handles all incoming orders), (2) Trade Execution Engine (Python, with microsecond-level precision), (3) Compliance Validator (Node.js, runs FINTRAC checks in parallel), (4) ML Model Service (Python/TensorFlow, generates trade signals), (5) Market Data Aggregator (Python, polls 5 external providers), (6) Audit Logger (PostgreSQL, immutable transaction log), and (7) WebSocket Gateway (Node.js, real-time client updates).
Communication between services happens via Apache Kafka for durability and Redis for ultra-fast caching. The ML model, trained on 10 years of historical market data, generates sentiment scores and predictive signals consumed by the execution engine — zero additional latency because the model runs asynchronously and results are cached. End-to-end latency from order placement to execution confirmation is 50ms — 95% faster than their previous system.
PostgreSQL is the single source of truth, with row-level security ensuring traders can only see their own data, full audit logging on every table, and point-in-time recovery capability. The compliance microservice runs FINTRAC validation checks in parallel with trade execution, adding zero latency to the critical path — every transaction is timestamped, signed, and immutably logged. We implemented automated daily compliance reports that export directly to FINTRAC systems.
Key Results
Measurable Impact, From Day One
“We went from 25K to 75K daily transactions without breaking a sweat. What impressed me most was the compliance layer — Mapletech Labs fundamentally understood the regulatory environment and built it in from day one, not as an afterthought. Latency went from 800ms to 50ms. We went from infrastructure firefighting to 100% focus on product. They didn't just deliver code — they gave us a 3x competitive advantage.”
Jessica Chen, VP Engineering
Axion Capital