DX & AI Project Case Studies

Selected engagements across business phases.

Development & ImplementationAutomotive

Marketing Data Platform Build

Built a marketing data platform for a leading automotive group, unifying fragmented sources into an analytics-ready environment.

Challenges

  • • Data sources were fragmented across multiple systems
  • • Reporting cycles were slow and delayed decision-making
  • • Performance needed to scale with growing data volume

What we delivered

  • • Data architecture design
  • • ETL pipeline build and optimization
  • • Data integration platform implementation
  • • Latency reduction and sync optimization

Outcomes

  • • Unified data sources and achieved holistic visibility
  • • Improved performance and sync frequency
  • • Transitioned to stable operations
TreasureDataSQLData Pipeline
Role: Data Engineer (requirements, design, implementation, testing, rollout)
Strategy & PlanningAutomotive

New Business Development with Data

Supported new business ideation by leveraging existing data and bridging business design with analytics.

Challenges

  • • No clear direction for business use of existing data
  • • Ideation process was not structured
  • • Lack of cross-functional bridge between analysis and planning

What we delivered

  • • Data asset assessment and opportunity analysis
  • • Data-driven ideation workshops
  • • Business concept formulation and evaluation
  • • Hypothesis validation planning

Outcomes

  • • Generated multiple new business candidates
  • • Improved decision accuracy with structured validation
  • • Strengthened cross-functional collaboration
Data AnalysisBI ToolsWorkshops
Role: Data Engineer / Analyst / Business Designer
Strategy & PlanningEnergy

Generative AI for Next-Gen Business

Defined AI use cases and execution plans to support next-phase decision-making for a major energy company.

Challenges

  • • AI interest existed but use cases were unclear
  • • No framework to assess feasibility and business value
  • • Needed concrete next-phase execution plan

What we delivered

  • • AI use-case ideation and selection
  • • Requirement structuring and prioritization
  • • Feasibility assessment
  • • Next-phase execution planning

Outcomes

  • • Assessed multiple AI use cases
  • • Clarified priorities for decision-making
  • • Supported go/no-go decisions
Generative AIPrompt EngineeringRequirement Analysis
Role: Acting PM / Business Designer / AI Engineer
Dev / OperationsEducation

CRM Build and Continuous Improvement

Built a core CRM system and delivered phased enhancements with ongoing operational support.

Challenges

  • • No scalable CRM process for growing business
  • • Needed domain-specific requirements for operations
  • • Continuous improvements required after launch

What we delivered

  • • Requirements definition and base design
  • • Detailed design, implementation, testing
  • • Phased feature expansion (Phase 2, 3)
  • • Operations and maintenance setup

Outcomes

  • • Established a stable core system
  • • Expanded features in phases to support growth
  • • Improved team capability through technical leadership
JavaWeb AppsDB Design
Role: SE / Development Lead (end-to-end delivery)