Built by engineers tired of writing the same SQL.

In the data field, we are always striving for simplicity. I know this because I have spent almost 20 years working directly with data, and I have learned, slowly and sometimes painfully, that complexity compounds.

It compounds in query costs, in storage bills, in engineering time, and in the quiet accumulation of pipelines that nobody wants to touch.

When GA4 replaced Universal Analytics, it introduced one of the most structurally hostile exports I had seen pushed to production at scale. Nested arrays for every parameter. Evolving schemas. No consistent field access. To get page_location and page_title out of a page_view event, you write 18 lines of SQL. Which is an issue if you want to do anything useful with the data at all.

I had seen this pattern before. Complexity that feels manageable at first, until it isn't. Every team hitting the GA4 BigQuery export faces the same wall. It is not a unique problem. It just had no good solution.

So we built one.

"The goal was simple: resolve the complexity once, as close to the source as possible, and get out of the way."

The result is a pipeline that costs almost nothing when idle, scales linearly when active, and requires no maintenance after the initial deployment. Deploy once, run forever. That is not a marketing claim. It is a design constraint we held to throughout.

GA4 is the first connector. The pattern is repeatable: find a widely-used data source with a structurally complex export, resolve that complexity once, deploy it natively into the customer's environment. No platforms, no data extraction, no external dependencies. There are other data sources with the same problem. We plan to build connectors for those too.