Enterprise AI Deployment: 40% Infrastructure Cost Reduction, 20% Higher Inference Throughput, 30% Lower Latency & 65% Customer Service Automation

Industry: AI Infrastructure, Cloud Computing

Client

Innovative Enterprise AI Technology Client

Goal

To architect and deploy a robust, scalable AI model deployment system that supports large-scale distributed processing, optimizes resource allocation, and reduces operational costs.

Challenges

  • Innovating customer experience through AI-driven tools in a regulated environment.
  • Enhancing retail performance while ensuring compliance with industry regulations.
  • Balancing technological advancement with operational and regulatory constraints.

Solution

Designed and launched Data & AI training programs across MENA, Europe, and London for the FinTech sector.

Implemented an AI-powered customer insight platform using Edge technology to enhance service and retail performance.

Automated 65% of customer service inquiries with a hybrid statistical and GPT-4 solution, driving significant ROI and operational efficiency.

Impact:

Increased model inference throughput by 20%, improving overall system performance and response time compared to prior configurations.

Reduced infrastructure costs by 40% compared to traditional centralized models, optimising both financial and operational efficiency.

Reduced model inference latency by 30%, ensuring faster decision-making and processing, crucial for large-scale deployments.

Maintained 95% uptime, ensuring high availability and reliability for clients and internal users.

Developed and mentored a high-performing AI team, setting a strategic roadmap for future AI infrastructure advancements and execution.

*Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.