LEXXARI by CCG Software Solutions uses IBM technology to simplify policy interpretation
Insurance is complicated. When disaster strikes, fast, accurate decision-making is critical—and complexity comes with consequences. Decoding dense policy language can be a major struggle for both policyholders and insurance professionals. Uncertainty puts everyone at risk. Insurance adjusters can’t make confident, consistent claim decisions, agents waste hours re-reading lengthy policies just to answer basic questions, and customers grow increasingly frustrated with their providers.
Claims Connection Group (CCG), a Managed Repair Program provider and IBM Business Partner, sought to bring clarity back to the claims process.
CCG collaborated with IBM to build LEXXARI™, an intelligent policy interpretation platform that reads, summarizes, and interprets dense policy documents.
LEXXARI was co-developed in just 14 weeks. By combining their unique areas of expertise, IBM and CCG were able to design workflows that mirror how carriers, agents, and policyholders interact with policy data.
IBM watsonx® was trained on real-world insurance policies and AI agents from IBM® watsonx Orchestrate® were integrated into the platform to provide users with actionable, step-by-step guidance.
The result was a seamless, AI-driven platform that turns hours of manual review into immediate insights.
LEXXARI speeds up policy reviews by 70%, reducing manual review time from hours to just minutes. Policyholders and insurance professionals now receive consistent, accurate information, improving communication and confidence during the claims process. Clearer coverage understanding has led to a 40% reduction in no-pay claims and 60% fewer post-storm calls.
In the future, CCG plans to expand LEXXARI into additional industries, including auto, legal, government, and education—bringing clarity wherever dense documentation affects critical decision-making.
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Examples presented as illustrative only. Actual results will vary based on client configurations and conditions and, therefore, generally expected results cannot be provided.