How ZoomInfo Uses Pinecone to Deliver Real-Time Contact Recommendations at Scale
ZoomInfo, a B2B go-to-market intelligence platform with hundreds of millions of professional contact records, needed a vector database to power real-time personalized contact recommendations for sales and marketing teams. The company deployed Pinecone’s serverless vector database with Dedicated Read Nodes to run semantic search over 390 million contact embeddings with sub-second latency. The result was a 50% increase in user engagement, a 2x improvement in recommendation relevancy, and 50x more peak request capacity.
Impact
>50%
Increase in user engagement
2x
Improvement in relevancy and recall
50x
Increase in peak customer requests served
390 million+
Contact vectors in production system
~60ms
P50 query latency
3 weeks
Time to working proof of concept
Challenge
ZoomInfo needed to deliver real-time personalized contact recommendations over 390 million embeddings with sub-second latency, without adding the operational burden of managing distributed vector infrastructure.
Solution
ZoomInfo deployed Pinecone’s serverless vector database with Dedicated Read Nodes to run semantic search over 390 million contact embeddings, enabling instant recommendations with predictable low-latency performance as traffic scaled.
Tools & Technologies
What Leaders Say
“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.”
“Pinecone enabled us to build, scale, and optimize a real-time contact recommendation system that processes thousands of large-embedding-model vector search queries per second, which has driven a 2x improvement in relevancy and 50% boost to user engagement.”
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Full Story
ZoomInfo provides sales and marketing teams worldwide with access to hundreds of millions of professional contact records, enriched with firmographic data and AI-powered search capabilities. For its customers, the ability to quickly identify the right person inside a target account—and act on that information—is directly tied to pipeline and revenue. Even small improvements in how contacts are surfaced translate into significant time savings and better outcomes for go-to-market teams.
ZoomInfo’s existing contact discovery experience required users to manually search, filter, and navigate large volumes of contact data. Identifying the most relevant buyers inside a target account could take hours of manual work. The platform lacked a way to surface ranked, personalized recommendations the moment a user viewed a company profile—a gap the Applied AI team set out to close.
ZoomInfo’s Applied AI team built a real-time contact recommendation system using a ~400M-parameter text embedding model and Pinecone as the vector database. Within three weeks, the team had loaded millions of embeddings and validated sub-second latency without manual index configuration. Pinecone’s serverless slab architecture stores vectors in large contiguous units, ensuring writes and reads proceed in parallel without blocking. For production-scale traffic, the team deployed Pinecone Dedicated Read Nodes—isolated read replicas with warm memory and local SSD—delivering predictable low-latency performance under sustained high-QPS workloads. The system scaled to over 390 million vectors across more than 100,000 namespaces without re-architecting the underlying infrastructure.
The production system achieved a P50 latency of ~60ms at ~40 QPS, keeping end-to-end recommendation service latency under one second for standard loads and under five seconds for peak traffic above 100 RPS. Relevancy improved 2x, user engagement rose by more than 50%, and the system served 50x more peak customer requests than the prior implementation. What had required hours of manual research was reduced to a single click: the moment a user views a company profile, the platform surfaces the most relevant contacts instantly. As Carlos Nunez, VP of Engineering and Applied AI, described it: the team went from managing infrastructure to improving the recommendation model itself.
ZoomInfo plans to expand Pinecone-powered recommendations across additional products, customer segments, and internal applications. The deployment demonstrates a broader shift in B2B software: platforms that can surface the right information instantly, rather than after manual search, are redefining what go-to-market intelligence means. With Pinecone’s combination of serverless scale, slab-based storage, and Dedicated Read Nodes, ZoomInfo has a clear path to extend real-time semantic search to new surfaces without operational overhead.
AWS