PHASE 01
Inventory the training estate
Every feature table, bucket, and partner feed feeding ML was mapped and classified — sensitive fields flagged in week one.
Case Studies / Transport & AI
Atlas Logistics: laying the data foundation behind trusted AI
3 pipelines gated in month one Legal review: 4 months → days First governed model shipped
The challenge
Ambitious AI, ungoverned data
Atlas Logistics moves freight across three continents and wanted demand forecasting powered by machine learning. But its training data mixed customer contracts, driver records, and partner feeds with no classification — and legal blocked the first model after a four-month review found unapproved personal data in the training set.
The ML team needed a way to prove, field by field, that training data was approved — without slowing experimentation to a crawl.
The approach
Gate the pipeline, certify the dataset
PHASE 02
Inline gating
Eligibility rules now sit between stores and pipelines: unapproved fields are masked or blocked at ingestion, with data scientists seeing exactly why.
PHASE 03
Certified launch
The forecasting model shipped with lineage, bias audit results, and an approval record — clearing legal review in days.
"We gated three training pipelines in the first month. Our first governed forecasting model passed legal review in days — the previous one took four months."
VP of Data Platform, Atlas Logistics