The Problem I Wanted to Solve
As a Boston Terrier owner, I was frustrated. Every search for dog food led to sponsored content, biased reviews, and paid placements. There was no single place to compare prices and ratings without wading through marketing noise.
I built PawsomeChoice to solve my own problem - a platform with complete transparency. No ads, no sponsored content, no paid placements. Just honest data.
AI-Powered Semantic Search
Traditional search matches keywords. PawsomeChoice understands meaning. Every product is vectorized using AI embeddings and stored in Pinecone - a vector database designed for similarity search.
When you search for 'best dry food for puppies with sensitive stomachs', the query is converted to a vector and matched against 40,000+ product vectors. The results aren't keyword matches - they're semantically relevant products that actually fit what you're looking for.
How it works: Vector databases store data as mathematical representations (embeddings) rather than text. This allows searching by meaning rather than exact words - finding 'grain-free puppy food' even if you searched for 'food for young dogs with allergies'.
Natural language search in action - no keyword tricks needed
Smart Article Generation
PawsomeChoice automatically generates helpful articles using a sophisticated AI pipeline. It starts with a query to the vectorized product database - finding relevant products based on meaning, not keywords.
Found products are scored using multiple factors: price competitiveness, aggregated ratings, number of reviews, and current availability. The top 5 products become the foundation for each article.
How it works: The scoring algorithm weighs: price (25%), rating (30%), review count (25%), and availability (20%). Products must meet minimum thresholds to qualify.
From Data to Published Content
With top products selected, AI generates the full article structure: an engaging intro, quick facts summary, detailed FAQ section, and comprehensive product breakdowns. Each piece is tailored to Swedish dog owners.
After content generation, a matching article image is created using AI. The complete article is then published automatically to PawsomeChoice and distributed to social networks - Instagram and Pinterest currently, with more platforms coming.
Auto-generated article with AI-created header image
Article Structure & SEO
Every article is optimized for both search engines (SEO) and AI answer engines (AEO). The structure includes schema markup, FAQ sections that AI assistants can parse, and recommended products with direct comparison links.
Articles aren't generic AI slop - each references actual products on the platform, includes real pricing, and provides genuine value to dog owners researching their next purchase.
The Product Page Experience
Product pages are designed for transparency. Descriptions are cleaned and normalized from messy affiliate feeds. Specifications are extracted and structured for easy scanning.
Price history is tracked and displayed as a chart - so you can see if that 'sale' is actually a good deal. Related products are found using vector similarity, not just category matching.
Clean product page with price history, specs, and aggregated ratings
Aggregated Ratings & Transparency
Ratings are collected from multiple stores and aggregated into a single trustworthy score. You see the breakdown - how many reviews from each source, the rating distribution, and any discrepancies between stores.
Everything is transparent. No hidden algorithms boosting certain products. No paid placements. Just data, clearly presented.
Built to Scale
While currently focused on Sweden with 16+ stores, the architecture supports international expansion. Adding a new country means configuring new affiliate feeds and translating the UI - the core platform handles everything else.
The niche focus on dog products is intentional. Rather than being a mediocre general comparison site, PawsomeChoice aims to be the best resource for dog owners specifically.
Technology Stack
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