Client Overview

Our client is a leading UK-based financial services organisation, providing a wide range of banking, lending and investment solutions to businesses and individuals. Known for its commitment to service excellence and innovation, the organisation has embraced modern cloud technologies to improve operational efficiency and customer experience.

The client had already standardised on Microsoft Azure as its core cloud platform, giving them a strong foundation to build and run enterprise-grade AI solutions.

The Challenge

The client was looking to improve efficiency and accuracy in managing high volumes of complex, unstructured customer queries that could not easily be automated using traditional rule-based solutions.

Their pre-project goal was ambitious but targeted:

To automate 50% of these customer queries using Azure OpenAI.

However, the challenge wasn’t just about deploying AI. The organisation needed:

An enterprise-grade Azure AI platform to support rapid experimentation and scaling.

Full security, compliance, and governance controls to meet strict regulatory requirements.

Operational automation to streamline deployment and management of AI resources.

Integration with Azure DevOps and existing enterprise architecture.

They knew that simply spinning up an OpenAI instance would not be enough. Without a secure, scalable and governed foundation, the AI initiative risked stalling or failing altogether.

The Solution

BlakYaks partnered closely with the client to deploy our Azure AI Foundations Accelerator Pack, providing a fully engineered, enterprise-ready AI platform in Azure.

The solution was designed and deployed using Infrastructure as Code (Terraform) to ensure repeatability, scalability and compliance, and integrated directly into the client’s existing Azure Landing Zone.

Deliverables

Our Azure AI Foundations Accelerator Pack delivered the following:

Enterprise-grade AI Platform
- Secure deployment of Azure OpenAI Services within dedicated, governed Azure resource groups.
- Ability to deploy multiple Foundry hubs for workload and subscription isolation.
- Support for multiple projects per hub via code, enabling structured expansion.
Security, Governance & Compliance
- Role-Based Access Control (RBAC) for projects and AI services.
- Integration with Azure Entra ID for identity management and security compliance.
- Controlled API access via Azure API Management with managed identities.
- Logging, auditing and monitoring via Azure Application Insights.
Automation & Integration
- Automated provisioning through Terraform.
- Integration with the client’s existing Azure DevOps pipelines for consistent gated releases.
- Fully automated deployment of multiple LLM models per Azure OpenAI service.
Operational Excellence
- Observability built-in with Azure Application Insights for performance monitoring and troubleshooting.
- Flexible, reusable architecture patterns to support future AI initiatives across the organisation.

Outcomes

“The platform is operating really well and exceeded expectations. This has given our team the ability to build a strong case for scaling AI across the business.”
Project Lead at customer organisation

The results far exceeded the original target. Against an initial automation goal of 50%, the solution is now achieving around 90% automation for these complex, unstructured queries, a significant leap in operational efficiency.

Key achievements:

Exceeded performance targets: 90% of complex queries now automated using OpenAI.

Customer reported no operational issues since go-live, confirming the stability of the platform.

The project’s success enabled strategic hiring by strengthening the business case for dedicated AI engineering resources.
Multiple AI use cases are now in the pipeline, setting the stage for scale by leveraging the same enterprise-grade foundations.

Why it worked

By deploying the Azure AI Foundations Accelerator Pack, the client avoided months of architecture and engineering effort, reduced risk, and ensured compliance from day one.

The solution provided: