From Foundation to Independence: How Cosmo Laser runs an AI-Powered data pipeline on their own

Cosmo Laser wanted to move beyond being a consumer of data to becoming the owner of their own data infrastructure; capable of building, running, and evolving their pipelines without relying on external consultants for every change.

Our engagement with Cosmo Laser has evolved. The early work was about building capability from scratch: designing data models, establishing architecture, and getting reliable pipelines in place. That foundation made everything that followed possible.

In this case, our role was more advisory. We helped Cosmo understand the right tools for the job; why dlt and dbt made sense for his scale and use case, how to think about data modelling for a multi-clinic operation, and how to structure a warehouse that would grow with the business rather than become a liability.

When they encountered problems or wanted to expand the platform, we were there to help them think through the approach.

• Operational independence: Cosmo Laser's CMO builds and maintains production pipelines without engineering support
• Seven integrated sources feeding a centralised BigQuery warehouse
• Modern stack: dltHub + dbt + Tower - scalable and team-owned
• AI-accelerated development via Claude Code - no deep technical expertise required
• Scalability to additional use cases thanks to solid foundation built on architectural best practices provided by Pyne

Cosmo Laser’s story reflects a broader shift Pyne is seeing across clients: business operators who didn’t previously consider themselves engineers are now building and running real data systems. As AI handles more of the coding, the question has changed from “Can I implement this?” to “Can I run this reliably in production?”

If you’re interested in transforming your business operations through data consolidation and automation, contact us for more information or sign up for a free consultation here.