Retail Case Study
Sablecrest Retail Group
A custom model that outperformed a general-purpose API on a specialized task.
+27%Conversion Rate
The Challenge
Where things stood before
When Sablecrest Retail Group first reached out, manual processes that couldn't scale with a growing catalog and customer base was the issue sitting at the top of their list.
It was the kind of issue that showed up in day-to-day frustration long before it showed up in a report.
The Solution
What we built
Our approach focused on the root cause rather than the most visible symptom.
From there, a custom model that outperformed a general-purpose API on a specialized task, built specifically around what Sablecrest Retail Group needed.
- Delivered within a 8-week timeline without cutting scope
- Built using Python and PyTorch for a stable, maintainable foundation
- Benchmarked against clear, agreed-upon success metrics from day one
- Structured so Sablecrest Retail Group's own team could monitor and maintain it going forward
Results
The impact, by the numbers
+27%
Conversion Rate
+34%
Forecast Accuracy
-21%
Manual Review Time
+25%
Revenue Lift
Tech Stack
Built with
PythonPyTorchscikit-learnMLflow
Trueline Software
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