About a-team Marketing Services
The knowledge platform for the financial technology industry
The knowledge platform for the financial technology industry

A-Team Insight Blogs

New Zealand’s Chelmer Adds Azul JVM to CSS Trading Platform

Subscribe to our newsletter

By Zoe Schiff

Azul Systems’ resale agreement with Chelmer will allow the Auckland, New Zealand-based trading systems supplier to offer Azul’s Zing Java Virtual Machine (JVM) to add consistency and scalability to clients’ low-latency infrastructures. The deal also expands Azul’s presence in Asia / Pacific.

The relationship stemmed from Chelmer’s desire to address performance issues with the legacy JVM used by its Chelmer Software Suite, an integrated, memory-intensive, customizable Java-based solution for broking, wealth management and private banking firms or custodians.

The CSS software manages the entire life cycle of investing, from order origination onwards, in a multi-currency, multi-asset, multi-market environment.

Because CSS was originally implemented using a legacy JVM, when used in situations involving more than 50,000 investor portfolios, certain routines were unable to complete due to random Garbage Collection pauses, a common problem for applications that rely on legacy JVMs.

To address the problem, Chelmer deployed its application on Zing. The result was a reduction in maximum pauses and an enhanced ability to consistently execute at higher loads.

Zing’s integration into Chelmer’s Software Suite is expected to enhance the overall application functionality and increase the processing of large investor portfolios. It will also reduce application downtime and achieve greater consistency of performance.

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: Navigating the Build vs Buy Dilemma: Cloud Strategies for Accelerating Quantitative Research

For many quantitative trading firms and asset managers, building a self-provisioned historical market data environment remains one of the most time-consuming and resource-intensive steps in establishing a new research capability. Sourcing data, normalising symbologies, handling corporate actions and maintaining infrastructure can take months and absorb significant budget before a single model is tested. At the...

BLOG

The Matching Engine Was Never the Hard Part: What 24/7 Really Demands of Exchange Architecture

The framing has become familiar. Digital asset exchanges, prediction markets and retail-driven platforms have normalised continuous trading. Traditional venues, with their nightly batch cycles and weekly maintenance windows, are now playing catch-up as they extend hours, tokenise assets and reach for new distribution models. The conventional answer is to point at the matching engine and...

EVENT

AI in Capital Markets Summit London

Now in its 3rd year, the AI in Capital Markets Summit returns with a focus on the practicalities of onboarding AI enterprise wide for business value creation. Whilst AI offers huge potential to revolutionise capital markets operations many are struggling to move beyond pilot phase to generate substantial value from AI.

GUIDE

AI in Capital Markets Handbook 2026

AI adoption in capital markets has moved into a more disciplined phase. The priority is now controlled deployment: where AI can be used safely, where it can deliver measurable value, and how outputs can be governed, monitored and evidenced. The 2026 edition of the AI in Capital Markets Handbook examines how AI is being applied...