From 20+ systems to one warehouse
How Rudder Analytics helped Grunt Style — a veteran-owned apparel brand — automate the reporting across the business, move planning from a monthly cycle to a daily one, and hold 100% reporting accuracy for 12 straight months.
The brief
Grunt Style is a veteran-owned apparel brand. At the start of the engagement it was selling direct on Shopify, on Amazon, and through a growing retail channel, with Fulfil running the ERP. Order, marketing, catalog and fulfillment data each sat in a different system.
Rudder Analytics was engaged to rebuild all of it: how data was collected, where it was stored, how it became a report, and what AI did with it.
How the numbers were produced
Every morning, someone copied the previous day's figures out of each system into a spreadsheet, and that spreadsheet was the daily sales report. Nothing in the process checked the total against the systems it came from.
That worked while there were fewer places to look. Each new channel and tool added another one. The report took hours, and the person putting it together spent the morning gathering numbers instead of using them.
How the data moves now
Every system now feeds one warehouse. Airbyte pulls the data in, Airflow controls when that happens, and dbt cleans and shapes it. The data passes through five stages, starting as an untouched copy of whatever each system sent and ending as the tables the business reports from. Because that first copy is kept, any figure in a report can be traced back to what the source actually delivered.
Sources
Ingestion & ELT
BigQuery warehouse
Consumption
The six workstreams
The work was organized into six workstreams: infrastructure, marketing, automation, reporting, customer data and AI.
Data infrastructure and ELT
The legacy pipelines and warehouse were replaced with automated ingestion and a five-layer dbt build, and the paid ETL licence went with them.
Marketing and data accuracy
Spend, conversions and revenue now come straight from Meta, TikTok, Google, Pinterest, Bing, Klaviyo and Criteo, with GA4 through Elevar as the reference point for measurement. Nothing sits between them and the warehouse.
Automation of manual work
Four forecast spreadsheets and the manual daily report were replaced by scheduled jobs. Nobody assembles anything by hand now.
Reporting and dashboards
Reports across seven areas of the business: MIS, sales, marketing, operations, finance, products and customers. Each one runs on schedule and ties back to the same warehouse figures.
Customer insight and CLTV
Duplicate customers were matched and merged into a single record. RFM segments now feed Klaviyo, and lifetime value can be tracked for each customer.
AI SQL agent and self-serve
An AI agent sits over the Fulfil data. Ask it a question in plain English and it writes the SQL, checks it, runs it, and returns a chart.
Before and after
Finance has posted 12 consecutive months of 100% accurate reporting. Marketing and operations now work from the same figures. The hours that used to go into assembling the sales report every morning are gone.
| Metric | Before | After | Impact |
|---|---|---|---|
| ETL licensing | Legacy subscription | None | −100% |
| Reports and dashboards | 1 daily report | 35+ | 35× more |
| Data processing time | Baseline | 60% faster | −60% |
| Forecast refresh | Monthly | Daily | ~30× more often |
| Duplicate customer records | ~7% | 0% | −7 pts |
| Marketing data variance | ~38% | ~0% | −38 pts |
Running the warehouse also costs less than the old setup did, with no duplicate tables and data loading tuned to the queries people actually run.
For the first time, marketing, finance and operations are all looking at the same numbers — and those numbers are right.
Grunt Style LeadershipVeteran-owned apparel brand · United States
The roadmap from here
With reporting stable, the roadmap moves to forecasting, self-serve analytics and catalog automation.
Query and visualization agent
The agent is being extended beyond the ERP to Shopify, Klaviyo, Meta and Google, so it can answer questions about any of them.
Demand planning tool
SKU-level demand forecasting and automated purchase ordering, built on statistical and machine-learning models, to reduce stockouts and free up cash held in stock.
AI product classification
Image and document models to categorize the catalog automatically, with a person checking the results.
Data and AI for brands outgrowing their stack
Rudder Analytics builds that platform, from the first pipeline to reporting a board can act on.
