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.
Exact-match search missed synonyms. AI now maps intent—linking "cooling goods" to inventory—turning vague queries into sales.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
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.
Exact-match search missed synonyms. AI now maps intent—linking "cooling goods" to inventory—turning vague queries into sales.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.