Intelligence Built Into Every Layer
Catalist isn't AI-enabled—it's AI-native. Intelligence is embedded in every interaction, from product discovery to fulfillment documentation.
1,200+
Premium Brands
40K+
Products Indexed
<200ms
Search Latency
24/7
AI Assistance
Four Pillars of AI-Native Commerce
Every feature is designed to help you find better products, save time, and grow your business smarter.
Semantic Search
Stop searching by keywords. Our AI understands what you're looking for—even when you don't know the exact product name. Search "trending toys" and get results based on market velocity, not just title matches.
<200ms
Search latency
40%
Better relevance
Smart Recommendations
AI that knows your business. Our recommendation engine learns from your purchases, browsing, and market signals to surface products likely to perform in your store. Discover opportunities you'd never find manually.
3x
Discovery rate
25%
Higher AOV
Ungating Assistant
Claude-powered guidance for Amazon and Walmart brand approval. Upload documents, get instant validation, and receive specific tips to improve your approval odds. Real-time success prediction based on 50+ brand requirements.
52+
Brands mapped
85%
Avg success rate
Predictive Health Scoring
AI that identifies at-risk customers before they churn. We track 6 weighted factors including recency, frequency, monetary value, and engagement to calculate health scores and prioritize retention efforts.
6
Health factors
30 days
Early warning
How Our AI Works
Powered by vector embeddings, Claude AI, and real-time market signals.
Every Product Gets Embedded
Using OpenAI's text-embedding-3-large model, we convert every product's title, description, brand, and category into a 1536-dimensional vector. This captures semantic meaning—"wireless earbuds" and "Bluetooth headphones" become neighbors in vector space.
Search Understands Intent
When you search, your query gets the same embedding treatment. We use pgvector's cosine similarity to find products that match your intent—not just your keywords. Hybrid search combines vector similarity with traditional text matching for the best of both worlds.
Claude Validates Documents
Our ungating assistant uses Claude's vision capabilities to analyze invoices and authorization letters. It extracts supplier names, dates, and product lists—then cross-references against brand requirements to identify issues before you submit.
Continuous Learning
Every search, click, and purchase improves the system. Recommendations adapt to your behavior. Health scores update as new signals arrive. The AI gets smarter the more you use it—and the more our entire buyer network uses it.
Frequently Asked Questions
How does AI semantic search differ from regular search?
Traditional B2B search matches exact keywords—search "kitchen tools" and miss results labeled "culinary utensils." Our AI semantic search understands meaning. It finds relevant products regardless of terminology, surfaces trending items you didn't know to search for, and learns from your behavior to improve results over time.
What is AI-powered ungating assistance?
Our ungating assistant uses Claude AI to guide sellers through Amazon and Walmart brand approval. It validates your invoices and authorization letters in real-time, predicts success probability based on historical data, and provides specific guidance on missing or problematic documentation.
How does the recommendation engine work?
Our AI analyzes your purchase history, browsing patterns, current inventory velocity, and market trends to suggest products likely to sell well in your store. It considers seasonality, complementary products, and real-time demand signals from across our buyer network.
What is customer health scoring?
Health scoring predicts which customers are thriving and which need attention. We analyze purchase frequency, recency, order values, engagement metrics, and sentiment signals to calculate a 0-100 score. This helps us proactively support at-risk customers before they churn.
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