Bringing AI model sprawl under enterprise control.

The challenge

As engineering teams introduce AI into different projects, model access can become fragmented. Individual applications establish their own integrations, while the organisation loses a consistent view of which models are available and how teams are using them.

The client needed to rein in this AI sprawl without preventing its engineering teams from building. The objective was to create a shared operating layer: a place to discover models, register their use and route access through managed endpoints rather than allowing every project to maintain an independent model-access pattern.

The solution

The work delivered a custom LLMOps platform on Azure, bringing together a model registry, discoverability and catalogue capabilities with a managed endpoint gateway.

The registry created a structured place to organise the model estate. The catalogue made that information usable for engineers looking for an approved model or existing capability. The gateway provided a managed access point between applications and model services, giving the organisation a place to apply access and operating policies consistently.

This separates the developer's application integration from the details of the underlying model service. Instead of treating every new AI project as a separate infrastructure decision, teams can work through a common platform while the organisation manages the model estate as a shared capability.