Fintech Case Study
Brightgate Financial
A model monitoring setup that caught silent drift before it affected customers.
-32%False Positives
The Challenge
Where things stood before
Brightgate Financial came to us with a problem that had been building for a while: manual review processes that were both slow and inconsistent.
Competitors and peers seemed to have this figured out already, which only made it more frustrating.
The Solution
What we built
Rather than jumping straight to a model, we spent time understanding the specific shape of the problem first.
The result was straightforward: a model monitoring setup that caught silent drift before it affected customers, built specifically around what Brightgate Financial needed.
- Validated thoroughly before deployment to avoid production surprises
- Structured so Brightgate Financial's own team could monitor and maintain it going forward
- Delivered within a 9-week timeline without cutting scope
- Built using MLflow and Kubernetes for a stable, maintainable foundation
Results
The impact, by the numbers
-32%
False Positives
-34%
Review Time
+20%
Detection Accuracy
99.1%
Model Explainability Score
Tech Stack
Built with
MLflowKubernetesDockerAWS SageMaker
Copperline Retail Group
Want results like this for your business?
Let's talk about your project — no pressure, just a straightforward conversation about what you need.
Talk to an AI Expert