Use Cases

Real-world applications of data and AI that deliver measurable results.

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Case Study — Retail

+12% Forecast accuracy

AI-driven demand planning.

8 weeks · 120 stores · CEE
MLOps Demand planning
Use Case — Platform

6× faster time-to-insight

Streaming ETL, self-service BI.

DataOps · Cloud migration
DataOps Cloud migration
Case Study — CPG

−18% Stockouts

Measurable business outcomes.

8 weeks · Omnichannel
Omnichannel Inventory

Retail

Retail

Forecast accuracy

  • Problem: Manual demand forecasting with 15% error rate
  • Solution: ML models with real-time data integration
  • Impact: 12% improvement in forecast accuracy
↑ forecast accuracy ↓ manual work
8 weeks · CEE · 120 stores
MLOps Demand planning Feature Store
Retail

Demand sensing

  • Problem: Reactive inventory management
  • Solution: Real-time demand signals and ML predictions
  • Impact: 25% reduction in stockouts
↑ demand visibility ↓ stockouts
6 weeks · Global · 500 stores
MLOps Real-time Lakehouse
Retail

Price optimization

  • Problem: Static pricing across channels
  • Solution: Dynamic pricing algorithms
  • Impact: 8% increase in margins
↑ margins ↑ competitiveness
10 weeks · CEE · 200 stores
MLOps Pricing Analytics

CPG

CPG

Stockouts reduction

  • Problem: 18% stockout rate across channels
  • Solution: Predictive inventory management
  • Impact: 18% reduction in stockouts
↓ stockouts ↑ availability
8 weeks · Global · Omnichannel
MLOps Inventory Omnichannel
CPG

Promo effectiveness

  • Problem: Low ROI on promotional campaigns
  • Solution: ML-driven promo optimization
  • Impact: 35% improvement in promo ROI
↑ promo ROI ↑ engagement
6 weeks · CEE · 50 campaigns
MLOps Promotions Analytics

Financial Services

Financial Services

Credit decisioning TAT

  • Problem: 48-hour credit approval process
  • Solution: Automated decisioning workflows
  • Impact: 6× faster time-to-decision
↓ time-to-decision ↑ customer satisfaction
12 weeks · CEE · 10K applications/month
ADF Decisioning Automation
Financial Services

AML false positives

  • Problem: 80% false positive rate in AML alerts
  • Solution: ML-powered risk scoring
  • Impact: 60% reduction in false positives
↓ false positives ↑ efficiency
16 weeks · Global · 1M transactions/day
AML MLOps Risk
Financial Services

Fraud detection

  • Problem: Increasing fraud losses
  • Solution: Real-time fraud scoring
  • Impact: 25% reduction in fraud losses
↓ fraud loss ↑ security
8 weeks · CEE · 500K transactions/day
Fraud Real-time MLOps

Telecom

Telecom

Churn reduction

  • Problem: 15% monthly churn rate
  • Solution: Predictive churn models
  • Impact: 40% reduction in churn
↓ churn ↑ retention
10 weeks · CEE · 2M subscribers
MLOps Churn Predictive
Telecom

Next-best-offer

  • Problem: Low offer acceptance rates
  • Solution: ML-driven offer optimization
  • Impact: 3× improvement in offer acceptance
↑ offer acceptance ↑ revenue
8 weeks · CEE · 1M customers
MLOps Offers Personalization

Manufacturing

Manufacturing

Predictive maintenance

  • Problem: Unplanned equipment downtime
  • Solution: IoT data + ML predictions
  • Impact: 30% reduction in downtime
↓ downtime ↑ efficiency
12 weeks · Global · 50 production lines
IoT MLOps Predictive
Manufacturing

Quality inspection (CV)

  • Problem: Manual quality inspection
  • Solution: Computer vision automation
  • Impact: 95% accuracy in defect detection
↑ accuracy ↓ manual work
8 weeks · Global · 24/7 operation
Computer Vision Automation Quality
Manufacturing

Throughput optimization

  • Problem: Production bottlenecks
  • Solution: ML-optimized production scheduling
  • Impact: 20% increase in throughput
↑ throughput ↓ bottlenecks
10 weeks · Global · 100 machines
Optimization MLOps Scheduling

Accelerators & IP

  • ADF Decisioning Flow
  • Databorn.AI pipeline
  • Lakehouse blueprint
  • Feature Store solution
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