LeadGenius is an intelligent data services and sales intelligence provider focused on delivering bespoke B2B contact and account data for go-to-market teams. Rather than selling access to a static database, LeadGenius uses a blend of machine learning, large language models, and a global network of human researchers to source, verify, and enrich data on demand. This approach is designed to give marketing, sales, and revenue operations teams the precise contacts and insights they need for complex account-based and multi-threaded selling.
Founded in 2011 out of Y Combinator under the original name MobileWorks, the company rebranded to LeadGenius as it shifted from generic crowdsourcing to specialized sales and marketing data services. Headquartered in Berkeley, California, LeadGenius has raised over $20M in venture funding from firms including Sierra Ventures, Lumia Capital, Javelin Venture Partners, SJF Ventures, and others, and today serves hundreds of mid-market and enterprise customers worldwide across more than 30–40 countries.
The LeadGenius platform centers on what it calls “Precision Data” — highly specific, verified datasets tailored to each customer’s ICP, territories, and workflows. Core capabilities include global contact discovery, advanced contact tagging and technographics, behavioral and social signals, contact monitoring for job changes, and privacy-by-design sourcing that supports GDPR, LGPD, and CCPA compliance. Data is delivered via a SaaS dashboard, browser extension, native integrations with major CRMs and marketing automation tools, and a public REST API.
In the crowded sales intelligence and B2B data market, LeadGenius positions itself between self-serve databases like ZoomInfo or Apollo and fully manual research agencies. Its differentiation lies in combining live, campaign-specific data sourcing with white-glove delivery pods and tight integration into systems like Salesforce, HubSpot, Marketo, and Outreach. This makes it especially attractive for teams that need high-quality, niche, or international data at scale, and are willing to pay more and engage in structured implementations to get it.