Adding one attribute to a 300M-item catalog took days of rebuilds. Unifying vector and product data unlocked real-time hybrid search.
A second-hand fashion search engine manages a massive, fragmented catalog of over 300 million unique listings from external platforms that refreshes by more than a million items daily.
Maintaining separate databases for vector embeddings and catalog attributes created severe synchronization issues within the data ingestion pipeline....
“We rely on vector search. We’ll use a combination of a reverse image search, or an image plus text. We then generate an embedding of the product information, add weights to it, and apply that to a catalog using vector search.”
Browser extension for searching and aggregating secondhand fashion deals.
Multi-cloud developer data platform for building and scaling applications.
Beni's Unified product search is part of this use case:
Related implementations across industries and use cases
A $2M deal evaporated in the 24 hours it took to follow up. Now reps answer technical questions live, in under a second.
Daily updates delayed new content. A unified database now runs twice-daily refreshes with zero downtime.
Fragmented pipelines slowed cross-site suggestions. A unified AI vector database cut latency 90%, processing 1,500 queries per second.
Fragmented pipelines slowed cross-site suggestions. A unified AI vector database cut latency 90%, processing 1,500 queries per second.
Scaling curation for 90M buyers was impossible. Now, experts build seed collections and Vertex AI expands them into millions of custom feeds.
Surging calls caused long holds and overtime. A 24/7 AI voice agent handles routine payroll, freeing 700 HR partners for advisory work.
Keyword bots bottlenecked 100 agents supporting millions. Now, AI resolves FAQs, freeing staff to mine chat logs for product feedback.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
Adding one attribute to a 300M-item catalog took days of rebuilds. Unifying vector and product data unlocked real-time hybrid search.
A second-hand fashion search engine manages a massive, fragmented catalog of over 300 million unique listings from external platforms that refreshes by more than a million items daily.
Maintaining separate databases for vector embeddings and catalog attributes created severe synchronization issues within the data ingestion pipeline....
“We rely on vector search. We’ll use a combination of a reverse image search, or an image plus text. We then generate an embedding of the product information, add weights to it, and apply that to a catalog using vector search.”
Browser extension for searching and aggregating secondhand fashion deals.
Multi-cloud developer data platform for building and scaling applications.
Beni's Unified product search is part of this use case:
Related implementations across industries and use cases
A $2M deal evaporated in the 24 hours it took to follow up. Now reps answer technical questions live, in under a second.
Daily updates delayed new content. A unified database now runs twice-daily refreshes with zero downtime.
Fragmented pipelines slowed cross-site suggestions. A unified AI vector database cut latency 90%, processing 1,500 queries per second.
Fragmented pipelines slowed cross-site suggestions. A unified AI vector database cut latency 90%, processing 1,500 queries per second.
Scaling curation for 90M buyers was impossible. Now, experts build seed collections and Vertex AI expands them into millions of custom feeds.
Surging calls caused long holds and overtime. A 24/7 AI voice agent handles routine payroll, freeing 700 HR partners for advisory work.
Keyword bots bottlenecked 100 agents supporting millions. Now, AI resolves FAQs, freeing staff to mine chat logs for product feedback.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.