How Lusha Uses Elasticsearch to Power AI-Driven B2B Sales Prospecting

The shift from keyword-based prospecting to intent-driven discovery is one of the more consequential changes in B2B sales technology. Lusha, founded in Israel and now serving over 1.5 million sales professionals worldwide, has built its platform around that transition. Its database of more than 200 million business contacts powers outbound prospecting for go-to-market teams at companies ranging from startups to global enterprises.

Lusha’s previous search infrastructure hit its limits as the platform scaled. The system could not handle the semantic complexity of understanding user intent—matching a sales rep’s targeting criteria to the most relevant contacts based on meaning, not just keywords. Manual lead list building was slow and imprecise, and the platform lacked the ability to surface personalized recommendations proactively.

The company rebuilt its search and recommendation infrastructure around Elasticsearch, using it not only as a distributed full-text search engine but also as a vector database. Embeddings are stored in Elasticsearch to enable semantic similarity search, allowing the platform to understand what a user is actually looking for and surface contacts that match by intent rather than literal keyword match. The system is fully compliant with GDPR and CCPA.

The most visible product outcome is Lusha’s AI Recommended Lists feature—also called Prospect Playlists—which surfaces high-value leads automatically based on a user’s behavior, ICP criteria, and targeting preferences. Rather than asking a sales rep to run manual searches, the platform continuously refreshes lists of matching prospects across multiple ICPs and sales motions. The experience is modeled on music streaming: curated, personalized, and constantly updated without manual intervention.

Customers who have adopted the AI-powered features report generating 300% more outbound leads, conversion rates up to 10 times higher than without the platform, and ROI figures reaching 1,000%. These outcomes reflect both the accuracy of Elasticsearch’s vector search and the quality of Lusha’s underlying contact data. As AI-assisted prospecting becomes a competitive baseline across enterprise sales, Lusha’s architecture positions it to deliver increasingly sophisticated recommendations as model capabilities and data quality continue to improve.