Global risk intelligence platform for sanctions, compliance, and financial crime detection
Kharon operates a knowledge-graph-based risk platform built on a decade of expert research, serving compliance and investigation teams at large financial and enterprise organizations. The tech stack reveals a data-intensive operation: Neo4j and Neptune for graph storage, Kafka and Airflow for pipeline orchestration, Snowflake and Databricks for analytics, with React/Vue on the frontend and Python/Java for backend services. Active hiring across data (6 roles), engineering (5), and sales (7)—plus new LLM integrations in flight—signals both product expansion and go-to-market acceleration in a market where compliance teams are stretched thin managing sanctions, export controls, and supply chain exposure across jurisdictions and hidden networks.
Kharon provides risk intelligence and screening data to financial services, enterprise, and government organizations managing sanctions compliance, export controls, investment risk, and supply chain exposure. The core product is a knowledge graph called Kharon Core, a verified intelligence layer tracking connections to high-risk actors—sanctioned parties, restricted entities, forced labor supply chains, and military end users—across ownership structures, jurisdictions, and languages. The platform underpins compliance workflows in sanctions, FOCI, third-party due diligence, and Know Your Customer processes. Founded in 2016, the company operates out of Los Angeles and currently employs 51–200 staff, with research, data science, and sales distributed across the US, UK, and Spain.
Kharon uses Neo4j and Neptune for knowledge graphs, Kafka and Apache Airflow for data pipelines, Snowflake and Databricks for analytics, Docker and Kubernetes for orchestration, and React/Vue on the frontend. Backend services run Python, Java, and FastAPI.
Kharon is building large-scale distributed data processing systems, workflow orchestration, data quality monitoring, data lake infrastructure, and LLM integrations into the product interface, alongside infrastructure standardization and production-readiness improvements.
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