Client Context

GrowthZone builds association management software for nonprofits, chambers of commerce, and trade associations. When a new customer signs on, the data team migrates that customer's records from their legacy system into the GrowthZone platform.

Every migration requires three specialist roles working in sequence: a data mapper, a pipeline engineer, and a target system architect. Source formats vary wildly. SQL databases, CSV, XML, JSON, image files, APIs. Each legacy system has its own structure, and each customer has opinions about what data to carry forward.

For years, GrowthZone ran about 10 migrations a year. Then they acquired MemberSuite, a legacy platform with a large, complex customer base. The projected migration volume jumped to 30-50 per year. The team size did not change.

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Problem Summary

Each migration took up to 100 days. The process ran in 20-day milestone cycles: GrowthZone's team prepared a new version of the data, the customer reviewed it, and the next round began. Five cycles per engagement, coordinated across three roles.

As volume tripled, the math broke. Leadership's answer: hire 2-3 more engineers at $300-450K per year.

Chandler Klose, Director of Data Services, saw a different problem.

"The ability to scale past that is only possible using AI and automation in general."

More people doing the same manual work would raise the ceiling slightly. It would not remove it. The bottleneck was the process, not the headcount.

The Decision

Klose had a background in AI and a specific question: could an AI agent handle the dense, context-heavy work of data engineering inside a real Snowflake production environment? He did not run a formal evaluation. He opened his laptop in an airport.

"I brought up my data warehouse concept and started typing to Genesis in natural language: here's the schema I have in mind, here's the GitHub repo, start building this. Genesis was rolling out commit after commit on that repo. Two hours later I was done. I had the prototype for the data warehouse. I said: this is the tool we're going to use."

The Deployment

Genesis runs natively inside Snowflake. For GrowthZone, that meant no new cloud infrastructure, no parallel system, no new vendor security audit.

As Klose put it: "Genesis sits in the Snowflake ecosystem and has access to everything within Snowflake. It is not at risk of anything broader than what is already the case for Snowflake as a cloud data warehouse provider."

Genesis now handles three workflows at GrowthZone:

The Transformation

What Changed

The core shift was in the milestone cycle. Customers still take time to review their data and decide what to keep. That conversation stays human. But the moment a customer confirms their data map, GrowthZone can deliver the full updated pipeline the next day.

"The instant they are ready to give us the data map, we can generate it at the touch of a button," Klose said. "Each of those milestones, which would have been 20 days each, can now be a single day from our perspective."

A 100-day engagement still runs 100 days on the customer's clock. On GrowthZone's side, five 20-day engineering cycles are now five one-day cycles.

The same four engineers who handled 10 migrations a year are now on a path to 30-50, with bandwidth freed up for AI development on Snowflake Cortex that previously had no time allocated to it.

ROI Summary

The $300-450K hiring plan would have added capacity inside the same process. The ceiling would have shifted, then needed revisiting again in a year.

Genesis costs $110K per year. The team did not grow.
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The investment replaced a headcount decision and removed the ceiling.