CHICAGO, June 9, 2026 /PRNewswire/ — Convr® today unveiled the Convr Risk Context Engine (RCE), the industry’s first knowledge graph and semantic ontology built specifically for commercial property and casualty (P&C) underwriting. Calibrated on a decade of production data and more than 2,500 integrated sources, the RCE powers every AI capability across the Convr AI Underwriting Workbench and gives carriers and MGAs the grounded, explainable foundation that agentic and generative AI alone cannot provide.
Carriers, MGAs and brokers are evaluating agentic agents, generative assistants, and large language models at an unprecedented pace. Most of these tools share a critical weakness: they are built on general-purpose foundation models with limited understanding of commercial insurance and not calibrated on real underwriting environments. The result is AI that can be inaccurate, inconsistent, irreplicable, and unexplainable. Underwriters, chief underwriting officers, and regulators cannot accept those qualities in risk decisioning.
The Convr RCE solves that problem. Built and refined since Convr’s first founding, the RCE is a commercial P&C knowledge graph and semantic ontology that encodes the language, structures, exposures, classifications, and decision logic of underwriting into a unified, machine-readable model, calibrated against a decade of real submissions, real exposures, and real underwriter feedback from leading carriers in production. Every AI capability in the Convr workbench – from intake to business classification, risk scoring, data enrichment, and workflows, runs on top of the RCE. The result is AI that doesn’t just sound right. It is right, and can prove it: every classification, appetite call, and risk-score output traces back through the ontology to the source submission documents, loss data, and underwriter decisions that informed it.
The regulatory direction is reinforcing the value of RCE. The NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers, now adopted in some form by roughly half of US states, requires governance, transparency, and accountability for AI in regulated underwriting decisions, with parallel frameworks in New York, Colorado, and others. Those outcomes are not achievable on top of black-box inference; they require AI grounded in an inspectable knowledge graph and ontology of the underwriting domain. That is exactly what the RCE provides, with every decision traceable to the ontology and source data behind it.
“The industry is having the wrong conversation about AI in underwriting,” said John Stammen, Chief Executive Officer of Convr. “Everyone talks about models. The real question is what they’re grounded in. Without a commercial P&C knowledge graph and ontology underneath them, generative and agentic AI are confident guessers. The RCE supplies the missing context . . . what a submission means, what an exposure is, what an underwriter decides and turns outputs into decisions a carrier can defend. This is exactly what Convr has been building since 2016.”
Availability
The RCE powers all Convr AI Underwriting Workbench deployments today. Carriers, MGAs and brokers interested in learning more can visit convr.com or contact a Convr representative.
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SOURCE Convr
