Overview
A controlled model-improvement study evaluates private checkpoints only against approved company data and a fixed quality gate.
The problem
Generic models repeatedly learned company terminology at inference time, increasing prompt size and producing inconsistent domain output.
The solution
Approved datasets trained private open-weight checkpoints. Each checkpoint was compared with the baseline before promotion.
The results
Each result below is modeled against the defined baseline and quality gate. It is not yet an independently verified customer claim.
24% fewer tokens
Company terminology no longer required repeated explanation.
10% higher quality
Domain evaluation improved after the measured checkpoint.
30% lower cost
The tailored model completed more work locally.
Conclusion
This study provides a transparent deployment hypothesis for the workload. Flo
cta validates the same path against real company data before any saving is presented as realized.