Overview

ZoomInfo is a business-to-business (B2B) go-to-market and sales intelligence platform. It provides organizations with detailed data on companies and professionals—such as direct-dial phone numbers, email addresses, and organizational structures—to help sales, marketing, and recruiting teams find and connect with ideal customers.

Supernova Award Category

Data to Decisions

The Problem

ZoomInfo's manual search process required users to spend hours filtering through massive professional contact datasets, which slowed time-to-action and hindered sales teams from quickly identifying relevant buyers. To automate this, the Applied AI team needed to implement a real-time semantic search system capable of delivering personalized recommendations in under a second across hundreds of millions of embeddings. However, traditional database add-ons and open-source vector databases lacked the scalability or injected too much operational overhead and complex tuning to meet their strict latency and throughput demands.

The Solution

ZoomInfo solved their manual search bottleneck by adopting Pinecone's serverless vector database to power an automated, real-time semantic search system. By leveraging Pinecone's unique slab architecture and Dedicated Read Nodes (DRN), they eliminated operational overhead like manual index tuning while ensuring predictable, sub-second latency across hundreds of millions of embeddings. This architecture allowed ZoomInfo's engineers to focus on data pipelines and model quality, ultimately shifting their customers from a slow discovery process to an instant, recommendation-driven workflow.

The results

Before adopting Pinecone, ZoomInfo's platform required users to manually search, filter, and navigate through massive volumes of contact data to identify relevant buyers, a process that slowed down time-to-action. Implementing Pinecone fundamentally transformed operations by replacing this multi-step discovery process with an automated, recommendation-driven workflow that instantly surfaces personalized contact suggestions the moment a user views a company profile. Internally, this change eliminated heavy database infrastructure overhead and complex cluster management, shifting engineering operations away from routine maintenance and entirely toward modeling and product innovation. This scalable architecture now serves as the foundation for wide-scale, disruptive projects, allowing ZoomInfo to expand its real-time semantic search pipeline to power entirely new automated workflows and next-generation AI-driven insights across their entire enterprise platform.

Metrics

User Experience & Engagement Metrics Before: Users spent hours manually searching, filtering, and evaluating massive contact datasets to find relevant buyers. After: Time-to-action dropped from hours to minutes, allowing users to surface relevant people with a single click. Result: A >50% increase in user engagement. Recommendation Quality & Accuracy Metrics Before: Relied on traditional discovery and existing search algorithms that lacked the necessary accuracy to boost engagement. After: Achieved a 2x improvement in overall relevancy and recall, delivering highly accurate, personalized contact suggestions. Platform Performance & Scalability Metrics Before: Faced scalability limitations and struggled to meet production demands across hundreds of millions of embeddings. After: Scaled to over 390 million vectors across 100,000 namespaces. Result: Enabled a 50x increase in peak customer requests served, maintaining a predictable P50 latency of ~60ms at ~40 QPS.

The Technology

To power its real-time contact recommendation system, ZoomInfo implemented Pinecone’s next-generation serverless vector database architecture. At the core of this platform is Pinecone's unique slab architecture, an LSM-tree-based storage system that organizes data into immutable, contiguous units called slabs. By processing these self-contained slabs independently and in parallel, the database eliminates index fragmentation and allows fast writes to proceed without blocking search queries. As ZoomInfo's workloads scaled, they integrated Pinecone's Dedicated Read Nodes (DRN), which provision isolated read replicas with an always-hot data path utilizing memory and local SSDs. This combination ensures predictable, low-latency performance and high throughput without the operational overhead of manual cluster tuning. Ultimately, this managed infrastructure allows the system to seamlessly handle hundreds of millions of embeddings under sustained, high-QPS production demands.

Disruptive Factor

ZoomInfo previously struggled to scale its real-time recommendation system over 390 million professional contact embeddings, as traditional database add-ons and open-source alternatives injected too much operational overhead and complex tuning to meet their strict sub-second latency demands. By adopting Pinecone, ZoomInfo challenged the status quo of managing complex distributed clusters, instead leveraging a fully managed serverless environment that separates compute from storage automatically. Pinecone sets itself apart through its unique slab architecture and Dedicated Read Nodes, which provide a warm, non-blocking data path that allows parallel reads to scale independently from writes. This internal infrastructure disruption eliminated routine system maintenance, while externally driving a massive market disruption by dropping customer search time from hours to minutes and boosting user engagement by more than 50%.

Shining Moment

ZoomInfo is most proud of achieving a 2x improvement in recommendation relevancy and a 50% boost in user engagement by successfully building and scaling a real-time contact recommendation system. Pinecone’s shining moment is its ability to seamlessly process thousands of large-embedding-model vector search queries per second while maintaining the strict responsiveness and accuracy users depend on. This reliable performance eliminates operational bottlenecking, positioning ZoomInfo to confidently scale traffic volumes even higher. Ultimately, this success allows their engineering team to seamlessly expand automated, high-impact recommendations across more touchpoints in their enterprise product suite.