ZoomInfo and Pinecone Deliver Real-Time AI Contact Recommendations for Go-to-Market Teams

Pinecone, a vector database technology leader, is excited to announce that its latest serverless slab architecture and its new feature of Dedicated Read Nodes (DRN) have become integral parts of the real-time, AI-powered contact recommendations for ZoomInfo’s sales and marketing teams. This implementation signifies the importance of adopting AI for efficient buyer discovery and enhanced user interaction.

As per their claims, this collaboration has brought about a 50% rise in user engagement, allowing users to find appropriate contacts within minutes, as opposed to hours.

This collaboration reflects the increasing need for robust AI infrastructure required for building scalable recommendation systems, semantic search engines, and high-performance enterprise workloads.

Built for enterprise-scale AI performance

As businesses accelerate AI adoption across customer intelligence, search, and recommendation use cases, infrastructure scalability has emerged as a critical challenge.

Traditional databases with added vector capabilities often struggle to support the performance requirements of modern AI workloads, particularly around:

• latency
• concurrency
• throughput
• cost efficiency
• workload isolation

Open-source vector databases, while flexible, frequently require complex tuning, ANN optimization, and ongoing cluster management.

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Pinecone’s serverless architecture addresses these challenges through:

• On-Demand indexes, enabling elastic storage scaling and usage-based pricing
• Dedicated Read Nodes, built for sustained high-QPS, low-latency environments
• slab architecture, which stores vectors in contiguous units to improve performance consistency

The result is a more predictable and scalable infrastructure layer for enterprise AI applications.

Powering ZoomInfo’s next-generation recommendation engine

ZoomInfo selected Pinecone to support its new real-time contact recommendation engine, which now delivers personalized suggestions across:

more than 390 million high-dimensional embeddings over 100,000 namespaces

The deployment enabled the company’s Applied AI team to move from prototype to production within weeks while maintaining strict sub-second latency requirements.

Performance gains include:

• 50% increase in user engagement
• 2x improvement in relevancy and recall
• support for 50x higher peak request volume
• predictable low-latency performance at scale

These improvements have significantly reduced customer time-to-action by surfacing relevant buyers instantly.

“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.” — Carlos Nunez, Vice President of Engineering and Applied AI at ZoomInfo.

Supporting multiple AI workloads from a single platform

Pinecone also emphasized the broader enterprise implications of the deployment.

“Every company today has multiple AI applications, and each one has different performance and cost profiles. Pinecone is the only vector database that lets customers run all of these workloads in one place, with exceptional speed, accuracy, and cost efficiency at scale. Our serverless slab architecture and Dedicated Read Nodes deliver trusted knowledge to our customers and make it possible to deploy production-grade RAG, search, recommendation systems, and agents without compromise.”

This positions Pinecone as a strong infrastructure layer for organizations deploying:

• retrieval-augmented generation (RAG)
• AI search systems
• recommendation engines
• autonomous agents
• semantic knowledge applications

A strategic example of enterprise AI in action

The ZoomInfo deployment demonstrates how enterprises can accelerate AI product launches while maintaining reliability and scale.

By removing the tradeoffs between performance, cost, and scalability, the architecture enables teams to focus on innovation and user outcomes instead of infrastructure complexity.

For go-to-market teams, the result is faster lead discovery, stronger buyer relevance, and significantly improved workflow efficiency.

As enterprise AI adoption continues to grow, the collaboration between ZoomInfo and Pinecone serves as a strong example of how purpose-built vector infrastructure is enabling next-generation revenue intelligence platforms.