01
Structure
Turn ambiguity into an analytical frame
Clarify the business question, identify the real decision, define KPI logic, and separate signal from noise before building anything.
Business Intelligence & AI Systems Designer
I design systems for better business decisions.
I help teams turn ambiguous business problems into structured decision systems through analytics, automation, and AI.
My work sits between business, data, technology, and execution: defining the right question, building KPI and reporting frameworks, standardizing messy workflows, and applying AI where it can become part of real operating systems.
01
Structure, Standardize, Systemize
The common pattern across my work: define the real business question, create shared operating language, then turn repeatable analysis into durable systems.
02
Standardize
Turn fragmented work into shared operating language
Create consistent metrics, reporting logic, data validation, and review processes so teams can compare performance and communicate clearly.
03
Systemize
Turn repeated analysis into durable workflows
Use automation and AI to make reporting, knowledge work, and decision support repeatable instead of one-off analyst effort.
02
Selected Work
Three case patterns that show the same operating model across different contexts: ambiguity to structure, fragmentation to standards, repeated work to systems.
01
Structure
Organization
Artalaxies
Business Analytics Framework for Digital Asset Markets
Multi-source market, user, and operational data integrated into a unified analytics framework for business and executive decision support.
Challenge
Business questions were reactive, KPI definitions lacked consistency, and analysis centered on isolated metrics instead of a repeatable decision framework.
Framework
Market PerformanceUser BehaviorCollection HealthOperational Effectiveness
Before
Reactive metrics, ad-hoc reporting, and questions that stayed at the level of what happened.
After
Standardized KPI logic and repeatable analysis across market, user, collection, and operational dimensions.
Core Contributions
Business problem decomposition
KPI framework and metrics definition
SQL reporting layer
Python ETL and validation workflow
Collection comparison and campaign analysis
Executive decision support
02
Standardize
Organization
GOFO Express
Operational Intelligence System for Multi-site Operations
Multi-site operational data consolidated into standardized reporting for performance monitoring, exception review, and management visibility.
Challenge
Sites used different KPI definitions, manual Excel processes, and inconsistent reporting logic, making cross-site comparison and operational review difficult.
Framework
Unified KPI LanguageOperational VisibilityException TrackingReusable Reporting
Before
Fragmented operations, manual reporting, and limited ability to compare site performance consistently.
After
Standardized reporting workflows, comparable operating metrics, and faster identification of operational issues.
Core Contributions
Operational KPI framework
Reporting standardization
Data cleaning and validation workflow
Automated reporting process
Exception tracking logic
Cross-site performance review
03
Systemize
Organization
Independent research and builder work
AI Workflow & Knowledge Systems
Workflow-first exploration of AI for knowledge organization, information processing, private infrastructure, and decision support.
Challenge
AI often remains isolated from daily work: useful in conversation, but disconnected from memory, documents, recurring tasks, and operating processes.
Framework
Knowledge SystemsWorkflow AutomationPrivate AI WorkspaceHuman-AI Collaboration
Before
Scattered information, repeated manual work, and AI tools used as standalone utilities.
After
AI-assisted workflows designed around persistent context, structured knowledge, and repeatable execution.
Core Contributions
AI email and archive workflow
Personal knowledge system
Local AI workspace
AI-assisted research environment
Long-term memory concepts
Document intelligence workflows
03
Thinking
Short operating principles behind how I approach analytics, business intelligence, and AI workflow design.
Metrics are a business language before they are a calculation
A KPI only becomes useful when teams agree what it means, why it matters, who owns it, and which decision it should influence.
Dashboards do not fix unclear decisions
A reporting layer can show movement. A decision system clarifies the question, the threshold, the owner, and the next action.
AI needs workflow, not theater
The strongest AI use cases are practical: organizing knowledge, reducing repetitive work, validating evidence, and helping teams move from messy inputs to useful action.
The bridge is the work
Business, operations, data, and engineering teams often speak different languages. The leverage comes from translating them into one executable system.
04
About
The throughline is business and technology translation: connecting operators, analysts, builders, and decision makers around one executable system.
Background
I work from the business question backward, then build the analytical and technical system around it.
Trained across applied mathematics, data science, public policy, machine learning, statistics, and econometrics through UC Berkeley and the University of Chicago, with professional exposure across analytics, operations, finance, audit, risk, and AI workflow design.
Business problem structuring
Decision-oriented analytics
KPI and metrics framework design
Data strategy and BI architecture
Operational intelligence
Workflow automation
AI solution design and integration
Executive communication
Cross-functional translation
05
Decision Diagnostic
A small interactive diagnostic for how an ambiguous business issue should move from question to metric, workflow, and decision.
Decision diagnostic
Choose a messy business problem
The point is not to produce a quick answer. It is to show how the question should move from ambiguity to metrics, workflow, and decision support.
Problem frame
The issue is too broad to analyze directly. It needs to be separated into channel, customer, pricing, conversion, product, and external-market hypotheses.
01
structure
Define the actual decision: diagnose where performance changed and which driver is controllable.
02
standardize
Create a KPI frame across traffic, conversion, retention, order value, product mix, and campaign windows.
03
systemize
Build a recurring review workflow that compares driver movement before leaders ask for another ad-hoc report.
Outcome
Ambiguous revenue discussion becomes driver-based decision support.
06
Contact
For thoughtful conversations around business intelligence, decision systems, operational analytics, or AI workflow design.