People search engine with 12B records powering identity verification and fraud detection
Spokeo operates a people-search platform serving 18M+ monthly visitors and enterprise customers through identity verification and background research. The tech stack reveals a data-intensive, ML-forward operation: Elasticsearch + MySQL for search, Databricks + Spark + TensorFlow for analytics and modeling, with emerging RAG adoption signaling a shift toward agentic search. Active projects around entity resolution, ML data pipelines, and agentic AI systems, paired with hiring concentrated in engineering and data roles, indicate Spokeo is transitioning from keyword-based search toward learned entity matching and AI-driven automation.
Spokeo is a people search engine founded in 2006 that aggregates 12 billion records into 250+ million unique profiles. The platform serves two distinct markets: consumers (18M+ monthly users reconnecting with contacts and managing fraud risk) and enterprises (B2B professionals conducting background research, criminal investigation support, and asset location). The company operates as a remote-first organization with 51–200 employees, primarily based in the United States, with an average tenure of 4.5 years. Core revenue drivers span consumer subscriptions and B2B licensing of search and identity verification APIs.
Spokeo's production stack includes React + Node.js + Ruby on Rails for the frontend and API layer, Elasticsearch + MySQL for search and storage, and Databricks + Apache Spark + TensorFlow for data pipelines and ML. AWS is the primary cloud infrastructure provider.
Current projects include entity resolution system migration to ML, retrieval-augmented generation (RAG) systems, agentic AI automation tools, and data pipeline scaling. These reflect a strategic shift from rule-based search toward learned entity matching and autonomous research agents.
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