Sales teams spent hours manually filtering 390M+ contacts. Engineers deployed real-time AI search to surface ideal buyers instantly.
A go-to-market intelligence platform managing hundreds of millions of professional contact records and firmographic data across more than 100,000 namespaces.
Sales teams spent hours manually searching and filtering through large datasets to identify the right buyers within a target account. To replace this...
“Pinecone’s slab architecture and Dedicated Read Nodes gave us the speed, consistency, and isolation we needed to run real-time recommendations at scale. Instead of managing infrastructure, we spend our time improving our recommendation model and the product itself. That has reduced the time our customers spend researching, filtering, and evaluating contacts—from hours to minutes—by giving them the right people to reach out to with a single click.”
ZoomInfo's Contact recommendations is part of this use case:
B2B intelligence and go-to-market platform for sales and marketing teams.
Managed vector database for AI search, retrieval, and recommendation systems.
Related implementations across industries and use cases
Sales reps manually sifted 200M contacts. Vector search now creates dynamic "playlists" of high-value leads based on intent.
Keywords failed to link siloed defense data. Hybrid search now scans 120M+ vectors to surface critical funding relationships.
Messy ingredient data broke text search, capping matches at 20%. AI semantic search now maps free-form inputs to unlock recipe imports.
Sales reps manually sifted 200M contacts. Vector search now creates dynamic "playlists" of high-value leads based on intent.
Processing trillion-token datasets took months. A native vector engine cut deduplication costs 5x and doubled processing speed.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Accountants manually scoured mailboxes to assemble 15 subsidiary workbooks. Now, staff-built AI agents pull invoice data for instant review.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.
Sales teams spent hours manually filtering 390M+ contacts. Engineers deployed real-time AI search to surface ideal buyers instantly.
A go-to-market intelligence platform managing hundreds of millions of professional contact records and firmographic data across more than 100,000 namespaces.
Sales teams spent hours manually searching and filtering through large datasets to identify the right buyers within a target account. To replace this...
“Pinecone’s slab architecture and Dedicated Read Nodes gave us the speed, consistency, and isolation we needed to run real-time recommendations at scale. Instead of managing infrastructure, we spend our time improving our recommendation model and the product itself. That has reduced the time our customers spend researching, filtering, and evaluating contacts—from hours to minutes—by giving them the right people to reach out to with a single click.”
ZoomInfo's Contact recommendations is part of this use case:
B2B intelligence and go-to-market platform for sales and marketing teams.
Managed vector database for AI search, retrieval, and recommendation systems.
Related implementations across industries and use cases
Sales reps manually sifted 200M contacts. Vector search now creates dynamic "playlists" of high-value leads based on intent.
Keywords failed to link siloed defense data. Hybrid search now scans 120M+ vectors to surface critical funding relationships.
Messy ingredient data broke text search, capping matches at 20%. AI semantic search now maps free-form inputs to unlock recipe imports.
Sales reps manually sifted 200M contacts. Vector search now creates dynamic "playlists" of high-value leads based on intent.
Processing trillion-token datasets took months. A native vector engine cut deduplication costs 5x and doubled processing speed.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Accountants manually scoured mailboxes to assemble 15 subsidiary workbooks. Now, staff-built AI agents pull invoice data for instant review.
Repetitive coding slowed R&D. Now 80% of engineers use agentic tools to automate work, saving up to 2 hours weekly per person.
Manual testing consumed 20% of developer time. Now, 1,500 engineers use AI agents to auto-generate tests and prototype solutions.