Private AI, in Production: Trusting AI with a Client's IP

AWS's Visual Computing team and EngineLab, an AWS Media & Entertainment Specialist Partner, tackle the dilemma facing studios today: trying AI is easy, but trusting it with a client's IP isn't. Unapproved models, unstable workflows, hardware scarcity and untraceable outputs all stand between a promising test and real client work. Together, they'll show how EngineLab AI, a managed generative AI platform built on AWS, closes that gap. It's deployed in the studio's own cloud, never trained on studio content and fully compatible with ComfyUI, giving every artist its full power so they can spend their time being creative, not fixing tools.

This session will unpack what running AI in production really demands. Every model needs a checked, approved licence before it reaches client work. Workflows have to stay stable, so what ran last month still runs today. GPU capacity must be there on demand, when a client bid needs an answer this week and new hardware means long lead times and a premium. AI has to fit the pipeline and tools the studio already runs. And when a client asks which model made a frame, the studio needs to be able to say, and make it again.

They'll then demo EngineLab AI as it runs in production with studios today. Every model is licensed and approved per project before it reaches an artist. GPUs run and scale in the studio's own AWS account, drawing on AWS capacity, with nothing to buy or maintain. Workflows are fully compatible with ComfyUI and can be built, improved and fixed. Every output is tracked by model, prompt and user, and spend is reported against each project. The session also covers how AI compute plugs into the studio's render pipeline through AWS Deadline Cloud.