Recommendation Engine

What It Does

A recommendation engine powered by semantic similarity. Upload your product catalog, content library, or any item dataset — then ask for recommendations in natural language. The AI understands what the user wants and suggests the most relevant items with explanations.

No collaborative filtering setup. No training data required. Upload your catalog and it works immediately.

How It Works

1. Upload your catalog — product descriptions, content metadata, course listings, or any items you want to recommend
2. User describes what they want in natural language or provides a reference item
3. Vector similarity search finds the closest matches from your catalog based on meaning, not just keywords
4. AI explains why each recommendation is a good match, ranked by relevance

Use Cases

• E-commerce — "I need a waterproof jacket for hiking in cold weather" returns the best matches from your inventory
• Content platforms — "Articles about scaling PostgreSQL" finds semantically related content, not just keyword matches
• Course marketplaces — recommend learning paths based on a student's goals and current skill level
• Internal tools — help employees find the right document, template, or resource from thousands of options

vs. Keyword Search

Keyword search for "warm coat" misses "insulated parka." Vector search understands they mean the same thing. Your recommendations work on meaning, catching synonyms, related concepts, and user intent that keyword matching misses entirely.