Toppp Case Study

Toppp is a prototype shopping answer engine for people who want a fast, trusted answer to: "What should I buy?"

Instead of showing a marketplace grid, sponsored placements, or a long review article, Toppp returns three opinionated recommendations for a product query:

1. Best Budget
2. Best for Most People
3. Best Upgrade

The project explores how AI can improve consumer shopping when it is paired with structured product data, transparent evidence, affiliate disclosure, and human editorial judgment.

Why This Exists

Shopping online is noisy. Search results are filled with ads, marketplaces optimize for conversion, and review content is often hard to trust. AI can help, but only if it is grounded in fresh product data, clear tradeoffs, and honest limits.

Toppp is designed as an answer layer before checkout:

• Search once.
• See the top three options.
• Understand why each pick won.
• See pros, cons, price, retailer, and affiliate disclosure.
• Click out to a retailer only after the decision is clear.

Public Demo

Live prototype: toppp.shop

Example route: Best Gaming Desktops

Note: The current public demo uses sample editorial data to demonstrate the product experience and data model. Production recommendations would require a formal evidence and review workflow before publication.

What This Case Study Includes

• Product Thesis
• Architecture
• Trust And Editorial Policy
• AI Commerce Readiness
• AI Shopping Experience Teardown
• Recommendation Evaluation Framework
• Merchant Ecosystem Memo
• Product Principles For AI Shopping
• Personal Shopping Agent Prototype
• 20-Journey Browser Research Log
• Sample Public Schema
• Demo Script
• Sample Recommendation JSON

AI Shopping Teardown

This repo also includes a public product teardown of ChatGPT, Google Shopping, Amazon, Perplexity, and TikTok Shop across 20 browser shopping journeys.

The teardown covers:

• 10 shopping queries across gifts, electronics, apparel, travel gear, meal prep, home goods, comparison shopping, and high-consideration purchases
• Screenshots and browser evidence
• Ranking quality
• Personalization quality
• Trust and transparency gaps
• Missing merchant data
• Conversion friction
• What ChatGPT should uniquely own in shopping

Start here: AI Shopping Experience Teardown

Prototype

Run the housewarming-gift shopping agent demo:

node prototype/shopping-agent.js --demo

Or run the interactive version:

node prototype/shopping-agent.js

Product Principles

• No sponsored rankings.
• No ads.
• Affiliate links never change the winner.
• Unsupported searches should say "we have not researched this yet."
• AI can assist research, classification, and summarization, but humans approve live recommendations.
• Product data should be structured enough to support future agentic shopping and checkout surfaces.

Status

This is a portfolio case study for agentic commerce, trusted shopping, and AI-assisted product decision-making.

It is not the full private implementation repo.