Over the last year I have been building two tools for ecommerce researchers and founders who want faster insights without spending hours digging through apps, themes, ads, or product signals. The tools are Store Inspector and ProductLair, and both came from my own frustration trying to understand what actually makes a store or product perform well.
This post explains the technical thinking behind both projects and some of the challenges I ran into while building them.
Store Inspector: Browser First Architecture
Store Inspector is a free Chrome extension that analyzes any Shopify store. It detects themes, apps, pixels, and a basic lead score. Most tools rely on servers to scrape stores, which is slow and often blocked. I wanted something that runs fully in the browser to avoid rate limits and speed issues.
How detection works
- Load the page HTML directly from the active tab
- Parse scripts, linked files, and HTML identifiers
- Match patterns for themes, apps, and pixels
- Score based on tech stack, tracking depth, and store structure
Everything happens locally. Nothing is sent to my servers unless the user opens the detailed view. This makes detection extremely fast and keeps privacy simple.
Challenges
- Some apps hide their scripts behind dynamic loaders
- Themes often change naming conventions
- Pixels can appear under custom wrappers
I solved these problems by building a pattern matcher that is constantly updated and by using weighted scoring instead of binary detection.
ProductLair: Data Consolidation for Product Research
ProductLair focuses on the product side rather than the store side. The goal is to collect signals from social platforms, ads, stores, and search trends and combine them into a single product profile.
Tech stack behind ProductLair
- Next.js for the frontend
- Supabase as the database and auth layer
- Serverless functions for scraping and enrichment
- A lightweight scoring engine for market assessment
- Recharts for performance summaries
Why build it
There are many product finder tools, but almost all of them are either outdated, low quality, or only work on TikTok. I wanted something that gives real analysis, not random product dumps. That meant manually curating products, building comparison tools, and designing an interface that feels fast.
Interesting issues I ran into
- TikTok creative links expire faster than expected
- Store data needed normalization because everyone structures their titles differently
- Search trend scoring required smoothing to avoid spikes from small regions
What I learned building both tools
- Browser based detection is far faster than remote scraping for anything involving Shopify
- Data is useless without normalization and scoring
- Users prefer one click insights over dashboards with too many metrics
- Speed matters more than features for research tools
- Clear visual hierarchy makes or breaks a research page
What is next
I am currently working on automated tracking for Store Inspector and deeper funnel metrics for ProductLair. Both tools are early but they already help thousands of users research faster.
If you are working on ecommerce related data tools, browser extensions, or store analysis systems, I would love to hear what you are building.
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