Infrastructure costs cut by 40%.
65% of enquiries automated.
A hybrid statistical and GPT-4 system now handles two-thirds of customer service enquiries automatically, while a distributed, Edge-based deployment architecture cuts infrastructure costs and speeds up every response.
reduction
throughput
latency
automated
Client
An enterprise AI technology client running large-scale, distributed model deployment for FinTech operations across MENA, Europe and London.
Goal
To architect and deploy a robust, scalable AI model deployment system that supports large-scale distributed processing, optimises resource allocation, and reduces operational costs.
Compliance moved slower than the technology.
The client wanted to bring AI-driven customer experience tools into a heavily regulated FinTech environment spanning MENA, Europe and London. Every improvement to retail performance had to clear compliance requirements first, and the business couldn’t afford to trade one off against the other.
The existing centralised model deployment infrastructure wasn’t built for this. It was too rigid to adapt to shifting regulatory constraints and too expensive to run at the scale the business needed.
Three constraints were non-negotiable:
- AI-driven customer tools inside a regulated FinTech environment
- Retail performance goals set against compliance requirements
- Technological advancement balanced against operational constraints
Edge infrastructure and a hybrid AI model, working together.
Data & AI training programmes were designed and launched across MENA, Europe and London for the FinTech sector, building internal capability alongside the new technology.
A hybrid of statistical models and GPT-4 did the heavy lifting, automating two in three enquiries without losing the human option.
An AI-powered customer insight platform was built on Edge technology, moving processing closer to the customer instead of routing everything through a centralised model.
A hybrid statistical and GPT-4 solution was deployed to handle customer service enquiries, automating 65% of them while keeping the underlying model deployment system distributed, scalable and cost-efficient.
Faster, cheaper, and still 95% available.
Uptime held at 95% throughout, so none of these gains came at the cost of reliability. Alongside the technology, the client built a high-performing AI team and a strategic roadmap to keep advancing the infrastructure.
* 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.
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*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.