Measure
Build reproducible datasets and metrics for AI visibility, citations, retrieval and competitive analysis.
Data Analytics · AI Search · GEO · AEO · SEO
I make complex information easier to understand, measure and use.
I work across data analytics and AI Search: using SQL and Python to investigate data, and structured research to understand how search and generative systems retrieve, cite and recommend information.
Independent research and reproducible portfolio projects · Methods, evidence and limitations documented.
Current focus
Diagnose
Find content, entity, evidence and data-quality gaps instead of reducing a problem to a single score.
Improve
Turn findings into testable changes, then keep proposed interventions separate from measured outcomes.
Selected work
Projects built around evidence
AI Search · Independent case studyData · Snowflake · dbt Data · PostgreSQL · Tableau
SEO/AEO/GEO Optimization
An independent Notion case study moving from public baseline and query fan-out to diagnosis, proposed optimization and controlled measurement design.
Read case study →AI Search · Benchmark protocolAI Search Competitive Intelligence
A controlled benchmark across Notion, Asana, ClickUp and monday.com using 35 frozen prompts, first-party evidence and auditable scoring rules. AI response observations are pending.
Explore benchmark →E-Commerce Revenue & Operations Analytics
Nine related datasets audited with Python, modelled through Snowflake and dbt, then investigated for lifecycle anomalies and financial reconciliation.
Browse data portfolio ↗Insurance Claims Analytics
Analysis of financial exposure, claim severity, portfolio concentration and data quality with an executive reporting layer.
Browse data portfolio ↗Working principle
Evidence first. Claims second.
I keep observed facts, analysis, proposed changes and measured outcomes separate. That matters in data work, and it matters just as much when evaluating visibility in AI-generated answers.