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Apex Fintech Solutions has introduced the Apex AI Suite, which includes an innovative agent-based development kit to improve developer engagement with its AscendOS platform. This tool allows users to utilise natural language commands, significantly shortening the time required to develop wealth platform prototypes from weeks to just days. This advancement is expected to boost growth for Apex Clearing, overseeing over $229 billion in assets across approximately 22 million client accounts.
The developer kit is designed to complement Ask Ascend, an AI assistant released in March 2025, built using Google Cloud’s Vertex AI platform, which also underpins the Apex AI Suite. According to Bill Capuzzi, CEO of Apex Fintech Solutions, this transformation democratises the ability to build in financial services, enabling innovators to realise their ideas without an engineering team.
Despite enthusiasm for agentic AI, the industry is cautious. A recent report by NVIDIA indicated that while 42% of financial services firms are exploring agentic AI, only 21% have deployed it. The newly launched toolkit allows business stakeholders to prototype features independently, potentially speeding up development for both technical and non-technical users. AscendOS aims to serve as a comprehensive, cloud-native platform that consolidates trading, custody, and compliance functions, thereby improving operational efficiency.
Industry experts recognise the potential benefits of the rollout, with ARQA’s CEO, Haik Sahakyan, praising the emphasis on enabling non-technical teams to build new prototypes leveraging Google’s AI infrastructure. However, he points out a significant limitation—the development kit is largely confined to the AscendOS platform. Many registered investment advisors (RIAs) operate using multiple custodians and a range of systems. This limitation may confine innovation to isolated environments, undermining broader operational efficiencies.
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Managing Traditional and Alternative Investments with AscendOS
Apex’s AscendOS was introduced as a cloud-native trading, clearing, and custody platform with the potential to manage both traditional and alternative investments. The agentic development kit packages essential resources such as API references, coding samples, and documentation directly into developers’ environments, facilitating the generation of production-ready code in various programming languages through natural language prompts.
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Uğur Hamaloglu from EY emphasises that while agentic AI can enhance client acquisition and operational efficiency, it is vital for firms to maintain strong governance practices to safeguard against the risks posed by unregulated AI recommendations. The importance of establishing clear fiduciary responsibilities and oversight mechanisms is highlighted as essential to mitigating potential adverse outcomes from AI-driven decisions.
Research from IBM and the Ponemon Institute reveals that many organisations lack robust policies governing AI, with 63% of surveyed companies acknowledging the absence of governance frameworks to oversee AI. Alois Pirker stresses that as agentic AI grows, wealth management firms will need to implement quality control measures, as managing multiple AI agents introduces complexities and risks of task overlap that could lead to faulty outcomes.
Radically Transforming Wealth Management
Sahakyan argues that a well-structured implementation of agentic AI could radically transform wealth management. However, without proper orchestration and clear communication across departments that use AI simultaneously, inefficiencies may arise. Although the potential advantages of empowering non-developers are substantial, the importance of maintaining a cohesive approach to AI deployment cannot be overlooked. Overall, while the technology promises significant changes to the industry. However, careful management and governance will be critical for its success.
