Get in touch
A well-established UK asset management firm had built something very useful – their own Generative AI chatbot that helped both customers and staff get answers fast. But as their client portfolio grew 40% year-over-year, critical infrastructure questions emerged. Their current system was hitting CPU limits during market volatility periods, API response times were climbing above 3 seconds, and compliance auditors were raising flags about data retention policies. Master of Code Global stepped in to conduct a comprehensive AI audit, transforming their functional prototype into an enterprise-grade system ready for regulated financial operations.
The asset management firm had achieved real results with their AI assistant. The system was processing over 2,000 client queries daily and had cut their support ticket backlog by 75%. Portfolio managers were using it to quickly access research reports, compliance documentation, and market analysis during volatile trading sessions.
But rapid business growth was exposing infrastructure limits. Market volatility periods were maxing out the server capacity, causing 15-20 second delays when portfolio managers needed instant access to critical information. Their data storage costs had tripled in six months as conversation logs piled up.
The leadership team faced a crucial decision point. They could either invest heavily in infrastructure upgrades without knowing if they’d solve the right problems, or they could get expert analysis first. They needed someone to identify exactly where their bottlenecks were, what security gaps existed, and how to scale efficiently without overspending on unnecessary upgrades.
Master of Code Global designed a comprehensive AI architecture evaluation framework for the client’s specific needs.
Think of it like a medical exam for AI systems, but much more thorough. We created detailed assessment protocols that examined every component of their GenAI ecosystem – from data flow mechanisms to security vulnerabilities. The framework included performance benchmarking tools specifically calibrated for Generative AI workloads.
Our approach covered all the critical areas. We conducted deep-dive technical reviews of their system architecture, mapping out exactly how components interacted and where optimization opportunities existed. We ran stress testing scenarios to simulate real-world usage spikes and established baseline performance metrics that actually meant something.
But we didn’t stop at finding problems. We built them a customized optimization roadmap that prioritized improvements based on business impact and implementation complexity. Every recommendation came with clear steps, realistic timelines, and honest assessments of what it would take to implement. The result was a strategic blueprint that transformed their functional AI system into something truly enterprise-ready.
We mapped out the entire system design, spotted bottlenecks in component interactions, and identified data flow optimization opportunities
Put their system through rigorous testing protocols, measuring response times and resource utilization efficiency under realistic load conditions
Conducted extensive penetration testing, checked access control mechanisms, and verified data privacy compliance like hackers would
Designed growth-ready infrastructure recommendations with capacity planning that wouldn't break during traffic spikes
Created a prioritized improvement plan with quick wins first, then strategic changes that would pay off long-term
Made sure the company was meeting all industry regulations with established governance protocols for ongoing monitoring
