I am a Data Analyst focused on ecommerce analytics, product discovery, customer behavior, and business intelligence.
I like building projects that answer real business questions, not just dashboards.
Currently, I am exploring AI Commerce Readiness and preparing my upcoming project: Agent Ready.
My focus: Data → Insights → Product Discovery → Better Shopping Decisions
I am inspired by what Constructor.io is building in ecommerce search and product discovery.
Constructor.io focuses on helping ecommerce companies improve how shoppers search, discover, compare, and buy products using AI, personalization, behavioral data, and product discovery intelligence.
Their work connects strongly with the kind of projects I am building:
- Ecommerce search analytics
- Product discovery intelligence
- Personalized recommendations
- Search and browse optimization
- Product ranking and conversion improvement
- AI-powered shopping experiences
This inspired me to think about a future problem:
Ecommerce brands may want AI shopping agents,
But first they need to know if their product data, reviews, FAQs, claims, and ranking systems are ready for AI-driven shopping.
That thinking became the base for my upcoming idea: Agent Ready.
DiscoverIQ is an ecommerce analytics project focused on understanding how users move through product discovery, search, cart, and purchase journeys.
Business Focus
- Search and discovery analytics
- Funnel drop-off analysis
- Revenue and conversion KPIs
- Product-level performance
- Brand and category analysis
- Customer segmentation
- Month-over-month comparison
Tech Used
A business intelligence project focused on analyzing ecommerce sales, product performance, customer behavior, and business KPIs for a sportswear brand use case.
Business Focus
- Sales performance analysis
- Product and category insights
- Customer behavior analysis
- Revenue and order trends
- KPI dashboard storytelling
- Business recommendation building
Tech Used
A business intelligence project built to understand ecommerce performance, omnichannel behavior, product/category performance, and customer insights.
Business Focus
- Omnichannel ecommerce analysis
- Product performance tracking
- Category and brand insights
- Customer behavior analysis
- KPI reporting
- Dashboard storytelling
Tech Used
My personal portfolio website where I present my projects, skills, and work as a data analyst.
Focus
- Personal branding
- Project presentation
- Professional profile
- Portfolio showcase
Tech Used
Agent Ready is my upcoming idea inspired by the future of AI commerce and platforms like Constructor.io.
It is an AI Commerce Trust Readiness Test for ecommerce companies that want to use AI shopping agents, but may not know whether their product data, reviews, FAQs, claims, and ranking systems are ready.
Tagline: Search finds products. AI agents must recommend the right ones.
Ecommerce companies want AI shopping agents, but their product data, product claims, reviews, FAQs, and recommendation systems may not be ready for trustworthy AI-driven shopping.
If the data is weak, confusing, biased, or incomplete, the AI agent may recommend the wrong product, compare products badly, or fail to explain why a product is suitable.
- Can the AI agent understand the product clearly?
- Can the AI agent trust the product claims?
- Can the AI agent compare products correctly?
- Can the AI agent avoid biased recommendations?
- Can the AI agent explain recommendations clearly?
- Is the product data ready for AI search and discovery?
- Are reviews and FAQs useful enough for AI-based shopping decisions?
- Is the ranking system ready for agent-based recommendations?
As ecommerce moves toward AI shopping agents, brands will need a simple way to answer one important question:
Are we ready for AI shopping?
Agent Ready will help ecommerce brands test whether their product experience is ready for AI-powered search, discovery, and recommendation.
- AI commerce readiness
- Product data quality
- Search and discovery intelligence
- Shopping agent trust testing
- Product comparison logic
- Recommendation explanation quality
- Bias detection in recommendations
- Ecommerce AI audit scoring
Expected Tech
I don’t complain. I build.
I don’t just copy dashboards. I try to understand the business.
I don’t stop at charts. I write insights.
I don’t chase shortcuts. I improve step by step.
My rules
- Learn deeply
- Build practically
- Think like a business
- Explain simply
- Improve every project
Most used in my learning and projects
| Skill | Where I Use It |
|---|---|
| SQL | Business analysis, KPIs, views, joins, segmentation |
| Python | Cleaning, EDA, automation, data processing |
| Power BI | Dashboards, storytelling, executive reporting |
| JavaScript | Portfolio and frontend projects |
- SQL for analytics and interviews
- Power BI dashboard storytelling
- Python for data analysis
- Ecommerce product analytics
- Search and recommendation analytics
- AI commerce readiness testing
- Agentic AI workflows
- Professional communication