Finance Case Study
Vertexa Capital
A content recommendation engine that improved course completion rates.
-28%False Positives
The Challenge
Where things stood before
Before working with us, Vertexa Capital was dealing with manual review processes that were both slow and inconsistent, and it was starting to cost them real business.
Competitors and peers seemed to have this figured out already, which only made it more frustrating.
The Solution
What we built
Our approach focused on the root cause rather than the most visible symptom.
What we ultimately delivered was a content recommendation engine that improved course completion rates, built specifically around what Vertexa Capital needed.
- Validated thoroughly before deployment to avoid production surprises
- Delivered within a 10-week timeline without cutting scope
- Benchmarked against clear, agreed-upon success metrics from day one
- Designed around Vertexa Capital's actual data and workflow, not a generic template
Results
The impact, by the numbers
-28%
False Positives
-48%
Review Time
+25%
Detection Accuracy
97.1%
Model Explainability Score
Tech Stack
Built with
PythonPyTorchLMS APIsSQL
Sablecrest Commerce
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