Prevalent AI is building around a problem that becomes more dangerous as AI gains more autonomy inside large organizations: fragmented data can turn a fast decision into a wrong one. The London company continuously cleans, connects, and contextualizes information from across complex enterprise systems into a sovereign knowledge graph, giving both human teams and AI agents a more reliable picture of assets, identities, vulnerabilities, controls, and operational relationships.
The company has raised $22 million from Integrity Growth Partners, marking the first primary capital in its nine-year history. Prevalent AI plans to use the investment to formalize its global go-to-market organization, expand further into the U.S., strengthen its leadership team, and push the platform beyond cybersecurity into broader enterprise risk and AI use cases.
Prevalent AI was founded in 2017 by Paul Stokes and Arun Raj, alongside a team with deep intelligence-community experience, including former GCHQ leadership. The company remained bootstrapped and profitable from its first customer, while Istari became a minority shareholder through a secondary transaction in 2021.
Its technology starts with a data problem rather than a model problem. The platform ingests information from hundreds of enterprise sources, resolves inconsistencies, maps relationships, and maintains a continuously updated knowledge graph. That foundation already supports cybersecurity, and Prevalent AI says customers have extended it into financial crime, operational intelligence, compliance, and AI-driven workflows.
The timing matters because AI agents are only as reliable as the context they receive. Prevalent AI says its annual recurring revenue has more than doubled in the past year, while one insurance customer cut executive security-report generation time by 95% and a banking group improved incident detection by more than 80%. Those results are company-reported.
The larger opportunity is clear: as enterprises automate more decisions, trusted context becomes infrastructure. Prevalent AI is betting that the winning AI systems will depend as much on continuously accurate enterprise memory as on the models interpreting it.
