About
A major commercial bank wanted to give its data scientists a secure, GPU-accelerated space to experiment with generative AI, without touching production systems or regulated data. Firemind designed and delivered a greenfield, governed AI Lab on AWS, accelerated with NVIDIA AI Enterprise, with a clear path for proven models to move into the bank’s own on-premise environment.
Customer name withheld for confidentiality.
Challenge
The bank’s Innovation Centre and AI Lab had a clear mandate: find out where AI, machine learning and generative AI could create real value across the business, from fraud analytics and compliance automation to GenAI assistants.
As a heavily regulated institution with strict data-sovereignty requirements, the bank could not use production systems as the testing ground. It also had no existing cloud environment to build on. The data science team needed room to experiment freely, while three things stayed true:
- Experimentation stays fully separated from live banking systems.
- Governance, identity and audit controls are in place from the first notebook.
- Anything that proves its worth can move into the bank’s own on-premise environment, not stay stuck in a pilot.
Solution
Firemind designed and delivered a greenfield AI Sandbox on AWS, accelerated with NVIDIA AI Enterprise. It sits as its own governed layer: fully isolated from production, fed only with approved data, and designed from day one with a route out for the models that prove their value.
Governance by default
A multi-account AWS Landing Zone isolates the sandbox from everything else. Access runs through the bank’s existing identity provider with enforced MFA. Guardrails, continuous threat detection, posture management and encryption were active from day one, and only synthetic or anonymised data is used.
Enterprise-grade AI tooling
Data scientists get self-service, GPU-backed notebooks on Amazon SageMaker and the NVIDIA AI Enterprise stack (NeMo, NIM and RAPIDS) for fine-tuning, retrieval-augmented generation and model evaluation. A central model registry and experiment tracking make every result reproducible and auditable.
Built to be trusted and repeated
The whole environment is delivered as infrastructure-as-code, so it is consistent, auditable and self-documenting. Automated idle-shutdown keeps GPU spend under control as usage grows.
Designed for promotion, not just proof
Models validated in the sandbox can be compiled for deployment to the bank’s own data centre. Experiments become a pipeline toward production rather than a collection of isolated pilots.
From kick-off to a lab that keeps growing
The sandbox was delivered in four milestone-gated phases, each formally accepted by the bank. The relationship has since moved into the next stages.
Initiation
Kick-off, governance and discovery.
High-level design
Workshops, reviewed and signed off by the bank.
Build
Landing Zone and AI Sandbox environment.
Handover
User acceptance, documentation and onboarding.
Enablement (under way)
Training and professional services for the data science team.
AI Operations (under way)
Managed AI services for ongoing support.
On-premise production (in planning)
Dedicated NVIDIA GPU infrastructure for validated use cases.
Results
- Live and accepted. The bank signed off the high-level design, and the sandbox passed user acceptance testing through an end-to-end use-case walkthrough. Every agreed success criterion was met.
- A self-service lab for the bank’s data scientists. A team of 15 to 20 data scientists can now provision their own GPU-backed environments on demand, with no tickets and no waiting on infrastructure.
- Innovation with zero compliance exposure. Experimentation happens in a governed, isolated space. Production systems and customer data stay untouched.
- A foundation, not a one-off. Training and managed AI services are already under way, and the bank is planning dedicated on-premise NVIDIA GPU infrastructure to take validated use cases into production.
Many banks get stuck between moving fast on AI and keeping absolute control over risk. A governed AI Lab lets teams prove value quickly, gives leadership full oversight, and creates a structured route from sandbox to production.
