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FIX Verdict
Fintech
8 weeks

From Stalled to Stellar

PayStream AI

Ding Total Advantage Framework™ Journey

Phase 1: Meritocratic Triage

3 weeks

FIX verdict with clear remediation roadmap

Phase 2: Advantage Validation

2 weeks

PoC demonstrated 73% cost reduction potential

Phase 3: Total Acceleration

6 weeks

Live system processing 2M+ transactions, Series A secured

From Stalled to Stellar
73%
Cost Reduction
Monthly cloud spend decreased from $40K to $11K
-89%
False Positives
Dramatic improvement in detection accuracy
6 weeks
Time to Production
From stalled project to live system
$12M
Funding Raised
Series A secured post-launch

The Framework Journey

Phase 1: Meritocratic Triage

3 weeks

Meritocratic Triage uncovered data preprocessing flaws and over-engineering

Outcome: FIX verdict with clear remediation roadmap

Phase 2: Advantage Validation

2 weeks

Advantage Validation proved 10x cost efficiency with simplified architecture

Outcome: PoC demonstrated 73% cost reduction potential

Phase 3: Total Acceleration

6 weeks

Total Acceleration delivered production-grade system with competitive moat

Outcome: Live system processing 2M+ transactions, Series A secured

The Challenge

PayStream's AI-powered fraud detection system was burning $40K/month in cloud costs while generating excessive false positives. After 18 months of development, the system was still in "perpetual beta" with no clear path to production. The founding team was considering shutting down.

Triage Analysis

Scorecard Progress

38
Initial Score
82
Final Score

Key Findings

  • Data preprocessing pipeline introducing systematic bias
  • Over-engineered microservices architecture causing latency issues
  • Model architecture fundamentally sound but poorly tuned
  • Team capabilities strong but misdirected

Critical Actions

  • Rebuild data preprocessing with proper validation
  • Consolidate to simpler monolithic architecture
  • Implement modern RAG-based approach for context
  • Optimize inference pipeline for cost efficiency

The Solution

Our Deep Dive Triage revealed critical issues: the model architecture was fundamentally sound, but data preprocessing was flawed and the inference pipeline was massively over-engineered. We provided a detailed Fix roadmap focusing on data quality improvements and infrastructure simplification. Post-triage acceleration included LLM reengineering using modern RAG techniques and cost-optimized deployment.

The Results

  • 73% reduction in cloud infrastructure costs
  • 89% decrease in false positive rates
  • Production deployment achieved in 6 weeks
  • Series A funding secured at $12M valuation
  • Processing 2M+ transactions monthly

"Ding Ventures saved our company. Their triage was brutally honest—they told us exactly what was broken and gave us a clear path to fix it. Six weeks later, we were in production. Three months after that, we closed our Series A."

Marcus Chen

CEO, PayStream AI

Tags
AI Triage
Fraud Detection
Cost Optimization
LLM
Fintech

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