Retail Case Study
Vireline Retail Group
A recommendation engine that noticeably lifted engagement on product pages.
+59%Conversion Rate
The Challenge
Where things stood before
Vireline Retail Group's biggest obstacle was manual processes that couldn't scale with a growing catalog and customer base — something their team had tried to patch more than once without lasting success.
Their team suspected AI could help but weren't sure where to actually start.
The Solution
What we built
We started by getting a clear picture of exactly where things were breaking down before proposing anything.
The result was straightforward: a recommendation engine that noticeably lifted engagement on product pages, built specifically around what Vireline Retail Group needed.
- Validated thoroughly before deployment to avoid production surprises
- Benchmarked against clear, agreed-upon success metrics from day one
- Built using Python and PyTorch for a stable, maintainable foundation
- Structured so Vireline Retail Group's own team could monitor and maintain it going forward
Results
The impact, by the numbers
+59%
Conversion Rate
+31%
Forecast Accuracy
-29%
Manual Review Time
+51%
Revenue Lift
Tech Stack
Built with
PythonPyTorchRedisSQL
Westbound Software
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