Prevalent AI Secures 2M Growth Round to Build Knowledge Graph Context Layer for AI Agents

London-based enterprise data architecture startup Prevalent AI has secured $22 million in growth capital from Integrity Growth Partners (IGP). The investment represents the first primary institutional capital raised by the company since its founding in 2017. Prevalent AI was co-founded by CEO Paul Stokes and COO Arun Raj, both alumni of the UK’s Government Communications Headquarters (GCHQ). The company had previously operated as a bootstrapped, profitable business focused on resolving complex

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Prevalent AI Secures 2M Growth Round to Build Knowledge Graph Context Layer for AI Agents

London-based enterprise data architecture startup Prevalent AI has secured $22 million in growth capital from Integrity Growth Partners (IGP). The investment represents the first primary institutional capital raised by the company since its founding in 2017.

Prevalent AI was co-founded by CEO Paul Stokes and COO Arun Raj, both alumni of the UK’s Government Communications Headquarters (GCHQ). The company had previously operated as a bootstrapped, profitable business focused on resolving complex entity data for major financial institutions and government organizations. According to the company, annual recurring revenue more than doubled over the past twelve months.

The capital infusion will accelerate Prevalent AI's commercial expansion into the United States and fund the scaling of its knowledge graph architecture from cybersecurity exposure management into foundational context infrastructure for autonomous AI agents.

Enterprise Data Fabric Architecture

The Context Bottleneck in Enterprise AI

As enterprises transition from conversational chatbots to autonomous agents authorized to execute actions across corporate infrastructure, the primary point of failure shifts from model reasoning capabilities to data accuracy and organizational context.

In standard enterprise environments, critical operational data is fragmented across dozens of disparate platforms, including configuration management databases (CMDBs), endpoint monitoring tools, cloud asset registries, identity providers, and SaaS applications. Disconnected records and contradictory naming conventions frequently lead autonomous models to misinterpret organizational hierarchy, permissions, and infrastructure topology.

Prevalent AI addresses this challenge through its Security Data Fabric:

  • Continuous Ingestion: The company's DataBridge ingestion engine integrates with on-premises, cloud, and SaaS environments.
  • Entity Resolution: Automated reconciliation algorithms identify duplicate assets, resolve conflicting records, and align disparate identifiers into a single source of truth.
  • Dynamic Knowledge Graph: Assets, identities, vulnerabilities, security policies, and organizational workflows are mapped into a continuously updated semantic graph.

Sovereign Deployment and Agent Interoperability

Rather than requiring companies to upload proprietary data to shared multi-tenant SaaS environments or specific model APIs, Prevalent AI’s architecture is deployed directly within customer-controlled infrastructure.

The platform includes two primary AI integration layers:

  • Navigator: A natural language retrieval interface allowing human analysts and autonomous agent swarms to query the structured knowledge graph.
  • DataForge: A synthetic data engine that generates synchronized test datasets mirroring real-world organizational state for LLM evaluation and pipeline fine-tuning without exposing sensitive operational records.

The company plans to expand its integration surface across IT operations, regulatory compliance, fraud detection, and automated supply chain workflows.

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