One challenge many teams are hitting right now is the gap between AI demos and production AI systems.

This AWS case study covers some of the work GoDaddy has been doing to operationalize AI workflows using AWS infrastructure.

One example highlighted is Lighthouse, a system built using Amazon Bedrock to analyze large volumes of customer support interactions and extract patterns that can improve customer experience and operational efficiency.

What’s interesting from a systems perspective isn’t just the model usage, but the broader shift toward treating AI as part of production infrastructure:

data pipelines that continuously feed models

orchestration layers around LLM workflows

recursive learning platforms that allow real-world signals to continuously improve systems

integration into existing operational systems

The biggest challenge for many organizations right now isn’t building models — it’s building reliable systems around them.

Curious how others here are approaching AI infrastructure vs experimentation.