How Lusha Uses Elasticsearch to Power AI-Driven B2B Sales Prospecting
Lusha is a B2B sales intelligence platform with 1.5 million users and a database of over 200 million business contacts. By deploying Elasticsearch as both a full-text search engine and a vector database for AI-powered lead recommendations, Lusha helps customers generate 300% more leads, achieve conversion rates up to 10x higher, and realize return on investment of up to 1,000%.
Impact
300%
Increase in outbound leads
10x
Sales conversion rate improvement
Up to 1,000%
Customer ROI
200 million+
Contacts in database
1.5 million+
Platform users
Challenge
Lusha’s legacy search infrastructure could not support semantic understanding of user intent or deliver personalized lead recommendations at scale, forcing sales teams to manually build prospect lists from keyword searches across a 200 million+ contact database.
Solution
Lusha deployed Elasticsearch as a combined full-text search engine and vector database, storing contact embeddings to enable semantic similarity search and powering AI Recommended Lists—a feature that proactively surfaces personalized prospect playlists based on user behavior and ICP criteria.
Tools & Technologies
What Leaders Say
“Prospect Playlists eliminate the need for manual lead searching by continuously surfacing high-value contacts and companies that match your targeting criteria. You can create multiple playlists for different ideal customer profiles, industries, or sales motions, ensuring each list stays relevant and filled with the right prospects.”
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Full Story
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.