Finance Case Study
Halberd Capital
A scheduling AI tool that reduced appointment no-shows meaningfully.
-19%False Positives
The Challenge
Where things stood before
Before working with us, Halberd 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 scheduling AI tool that reduced appointment no-shows meaningfully, built specifically around what Halberd Capital needed.
- Built using Python and HL7/FHIR for a stable, maintainable foundation
- Benchmarked against clear, agreed-upon success metrics from day one
- Designed around Halberd Capital's actual data and workflow, not a generic template
- Delivered within a 7-week timeline without cutting scope
Results
The impact, by the numbers
-19%
False Positives
-34%
Review Time
+60%
Detection Accuracy
90.7%
Model Explainability Score
Tech Stack
Built with
PythonHL7/FHIRPyTorchAWS HealthLake
Vantagepoint Commerce
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