This case study describes how Firemind designed and delivered an enterprise AI platform for a client in the financial services sector. The client required a secure, high-performance AI environment that met stringent regulatory, data sovereignty, and governance requirements, while avoiding the limitations of fully managed, “black-box” AI services.
Firemind built the Blueprint for NVIDIA AI on AWS to meet these needs combining AWS cloud infrastructure, NVIDIA NIM microservices, and Firemind’s managed platform expertise to enable scalable, sovereign AI while significantly reducing operational burden.
Situation
As enterprise AI adoption accelerates, organisations increasingly rely on SaaS tools or fully managed hyperscaler AI services to move quickly. While easy to adopt, these services often introduce significant trade-offs: limited customisation, vendor lock-in, opaque consumption-based costs, and growing concerns around data residency, sovereignty, and long-term ownership of intellectual property.
At the same time, building and operating a GPU-accelerated AI platform in-house requires specialist skills across Kubernetes, cloud security, and GPU operations drawing focus away from delivering business value.
Task
Firemind was engaged to design an AI platform that balances flexibility, control, and performance with operational simplicity. The objective was to give the client the benefits of a self-managed AI stack, full ownership of data, models, and infrastructure, without requiring internal teams to deploy and operate complex GPU and Kubernetes environments themselves.
Success meant enabling teams to focus on real-world AI use cases and model consumption, rather than platform engineering and day-to-day operations.
Action
Firemind developed the Blueprint for NVIDIA AI on AWS, an enterprise-grade AI platform built on three integrated layers:
- AWS Foundation
A secure and compliant AWS environment providing the core infrastructure required for enterprise AI, including:
- Amazon EKS for Kubernetes orchestration
- GPU-enabled EC2 instances for inference workloads
- Private networking, identity and access management, encryption, logging, and monitoring
This foundation aligns with financial-services security and compliance expectations, including strong isolation, encryption at rest and in transit, and least-privilege access controls.
- NVIDIA AI Enterprise Software Stack
The platform uses NVIDIA NIM microservices to deploy and manage AI models as Kubernetes-native containers running on NVIDIA GPUs. NIMs provide a consistent, production-ready approach to GPU-accelerated inference, with predictable performance and controlled scaling.
By using NVIDIA NIMs rather than generic hyperscaler AI APIs, the solution gives the client clearer cost control, stronger governance, and tighter integration with existing security and operational processes, well suited to sustained, regulated workloads.
- Firemind Managed AI Platform
Firemind designs and deploys the AI landing zone, provisions Kubernetes clusters and GPU resources, integrates NVIDIA NIM services, and, where required, operates the platform day to day.
This includes platform monitoring, security operations, cost governance, compliance reporting, and controlled model deployment and versioning. Firemind acts as the operational layer, ensuring the platform remains secure, stable, and production-ready.
From the client’s perspective, the platform presents a clean, governed environment where teams interact with applications, APIs, and services without needing to manage Kubernetes, GPUs, or underlying infrastructure.
Sovereignty and long-term flexibility are embedded by design. All data and models remain within the client’s own AWS accounts, under their governance and audit controls. The container-based architecture also supports future portability, should the organisation choose to extend to other environments over time.
The blueprint supports two deployment approaches:
- Self-hosted, where the client retains maximum control over the platform within their AWS environment.
- Firemind-managed, where Firemind provides ongoing operational management of the Kubernetes and GPU infrastructure.
Both approaches prioritise flexibility, cost-performance, and sovereignty, while removing the burden of running an enterprise AI platform alone. For production use, the blueprint supports a multi-cluster architecture that separates application services from GPU-accelerated inference workloads, while also enabling single-cluster deployments for development and testing, ensuring scalability, workload isolation, and operational resilience.
The Results
The client gained an enterprise-grade AI platform that delivers both immediate operational value and long-term strategic control, including:
- High-performance, cost-efficient AI inference, optimised for sustained GPU workloads using NVIDIA NIMs on AWS.
- Full data and model sovereignty, avoiding vendor lock-in and opaque consumption-based pricing common to black-box AI services.
- Improved operational efficiency, with Firemind managing the AI platform end to end, reducing internal complexity and operational overhead.
- Designed to support regulatory and audit requirements
- Long-term architectural flexibility, enabled by a container-native, Kubernetes-based design.
As a result, internal teams can focus on what truly differentiates the business, their data, their models, and the intelligence they create, while Firemind ensures the platform remains secure, compliant, and production-ready.
Conclusion & Key Takeaways
Firemind’s collaboration with MoneySuperMarket demonstrates the power of AI agents and GenAI orchestration in enhancing customer engagement and transparency.
Trust drives conversion
Speed and scalability matter
Modular AI workflows
This project has laid the foundation for MSM’s future in intelligent, explainable price comparison, blending advanced AI with a customer-first philosophy.
