How a Media Firm Cut 8 Months of Data Work to 8 Weeks

A healthcare media firm's data was scattered and duplicative. Claude Code wrote 99% of a new Snowflake stack — shipping 140 data models in eight weeks instead of eight months.

8 weeks

Deployed in 8 weeks, not 8 months

< 4 weeks

Implementation Time

Jonathan Hansing

Co-Founder

Wallabi

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The Challenge

A healthcare media and events company came to Wallabi requesting a simple email automation workflow. Through iterative discovery, the real problem emerged: data was dirty, scattered across disconnected systems, and highly duplicative. The company had no unified concept of a "person" or "company" in their data architecture. Without a coherent data foundation, any workflow built on top would be unreliable — and their audience engagement and event recruitment strategy had no data bedrock to operate from.

What They Built

Wallabi scrapped the original scope and rebuilt the client's entire data architecture from the ground up — Snowflake as the warehouse, dbt for transformation, Fivetran for ingestion, and the Wallabi platform for the application layer. They implemented proper dimensional modeling and a medallion architecture. Claude Code wrote approximately 99% of the code and documentation, enabling the team to roll out close to 140 independent data models in six weeks — work that would normally take six to nine months. On top of the warehouse, they built roughly 10 intelligent data applications. Reverse ETL pushes clean, harmonized data back into the client's CRM and marketing automation tools for activation.

Wallabi's original mandate was a simple email automation workflow. Through discovery, the real problem surfaced: the client's data was dirty, scattered across disconnected systems, and highly duplicative, with no unified concept of a person or company in the architecture. Wallabi scrapped the original scope and rebuilt from the ground up. The warehouse foundation was Snowflake, with Fivetran handling data ingestion and dbt managing transformation. Proper dimensional modeling and a medallion architecture were implemented to create a clean, reliable data layer. A CI/CD process was built around Claude Code, which wrote approximately 99% of the code and documentation throughout. Prior session context was stacked directly into the repository, enabling cumulative learnings to benefit all team members across sessions. Nearly 140 independent data models were rolled out in six weeks — a project that would normally take six to nine months in traditional development. On top of the warehouse, Wallabi built roughly ten intelligent data applications. Snowflake Cortex and Snowflake Intelligence added natural language query capabilities. Reverse ETL pipelines pushed clean, harmonized data back into the client's CRM and marketing automation tools for activation.

AI Role

Not shared

Infrastructure

Snowflake (cloud data warehouse) • dbt (transformation and dimensional modeling) • Fivetran (data ingestion/ELT) • Wallabi platform (application layer) • Snowflake Intelligence (natural language query layer) • Snowflake Cortex (AI/ML features within Snowflake) • Tableau (dashboards and reporting) • Claude Code (AI-assisted development — ~99% of code)

Integration Points

Source systems → Fivetran ingestion → Snowflake raw layer • Raw layer → dbt transformation → medallion architecture (bronze/silver/gold) • Gold layer → Tableau reporting + Snowflake Intelligence NL queries + Snowflake Cortex ML • Gold layer → reverse ETL → CRM and marketing automation tools for activation

Impact

8 Weeks vs. 8 Months

A data infrastructure project that would typically take six to nine months in traditional development was completed in eight weeks, with approximately 140 data models rolled out — all powered by Claude Code writing nearly all code and documentation.

Weeks-Long Wait for Reports Eliminated

Business teams that previously waited weeks for a single data report now have real-time access. The client gained an instantaneous view of their data and operational metrics — a capability they had never had before.

Data Foundation Reshapes 2026 Board Growth Strategy

The project directly contributed to the company's board narrative for 2026 growth, enabling them to target new audiences and recalibrate their entire brand strategy. The CEO had not anticipated needing this level of strategic visibility — it emerged as a consequence of having clean, structured data.

Technology Utilized

Implementation Complexity

Time Savings

Cost Reduction

Customer Experience

Not shared

Best Fit For

Growth-oriented CEOs, COOs, and CIOs at companies between $75M–$750M in revenue that are feeling competitive pressure — growth has plateaued, they're losing deals to AI-native competitors, or clients are threatening to leave. Industries: services & consulting, media & events, supply chain & logistics.

Jonathan Hansing

Co-Founder

Wallabi

Co-founder of Wallabi, a data, technology, and AI strategy firm helping growth-oriented mid-market companies go AI-native. He's also a Partner at The AI Lab, an advisory community for CEOs.

Connect with the Expert

Industry:

Healthcare & Life Sciences

Media & Entertainment

Business Function:

Operations

Finance & Accounting

Marketing

Company Size:

51-250

Project Cost:

Cost not disclosed

Ownership:

Venture-Backed

Organization Type:

Private Company

AI Pattern:

AI-Accelerated Custom Software

Data Synthesis & Reporting

Value Type:

Time Savings

Cost Reduction

Customer Experience

AI Model:

Frequently Asked Questions

How did a mid-sized healthcare media company cut eight months of data work to eight weeks with AI-accelerated software?

The mid-sized healthcare media company scrapped an initial email-automation request and rebuilt its entire data architecture after discovery revealed dirty, scattered, duplicative data with no unified concept of a person or company. Using Snowflake, dbt, and Fivetran with dimensional and medallion modeling, Claude Code wrote roughly 99% of the code and documentation, rolling out close to 140 data models in eight weeks instead of the usual six to nine months.

What AI tools and models did the healthcare media company use?

The work combined AI-accelerated custom software with data synthesis and reporting. Claude Code wrote approximately 99% of the code and documentation within a CI/CD process, and the stack included Snowflake, dbt, Fivetran, the Wallabi platform, Snowflake Intelligence, Snowflake Cortex, and Tableau, with Cortex and Snowflake Intelligence adding natural-language query.

What results did the healthcare media company achieve?

Three outcomes: roughly 140 data models delivered in eight weeks versus a typical six to nine months; the elimination of weeks-long waits for reports, giving business teams real-time access; and a clean data foundation that reshaped the company's 2026 board growth strategy and brand recalibration.

How long did the engagement take?

Time to results was in the 4–8 week range; the core rebuild of nearly 140 data models was completed in eight weeks.

Who is this AI-accelerated data approach best for?

Growth-oriented CEOs, COOs, and CIOs at companies between $75M and $750M in revenue feeling competitive pressure — plateaued growth, losing deals to AI-native competitors, or clients threatening to leave — particularly in services and consulting, media and events, and supply chain and logistics.