Towards ROI based Systems
Self Hosted Architecture and Self Correcting Models
ROI Projections
Quality Improvements:
Error reduction: Target 80% reduction in production errors
User satisfaction: Increase from current baseline to 95% satisfaction
System reliability: 99.9% uptime with quality guarantees
Cost efficiency: Integration with $35k/month infrastructure savings
Current Investment
Infrastructure Costs:
Correction model hosting: Integrated with existing 48 NVL-72 infrastructure
Evaluation framework: LangFuse integration and custom tooling
Monitoring systems: Extension of existing observability stack
Development resources: Dedicated team for correction system development
Projected Savings:
Reduced human review: 70% reduction in manual quality assurance
Improved user satisfaction: 25% increase in user retention
Decreased support costs: 40% reduction in user-reported issues
Enhanced reliability: 50% reduction in critical system errors
LRMs as a Judge
ROI Projections
Quality Improvements:
Content quality increase: Target 40% improvement in average quality scores
Error reduction: 60% decrease in quality-related user complaints
Consistency improvement: 90% reduction in quality score variance
Time to market: 50% faster content approval and deployment cycles
Cost Savings:
Reduced human review costs: $200k annual savings in quality assurance labor
Decreased user support: 30% reduction in quality-related support tickets
Improved user retention: 15% increase attributable to higher content quality
Infrastructure efficiency: Integration with existing compute resources minimizing additional costs
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