Improvement in claim processing rate through predictive analytics
25+ use cases across insurance carriers and brokers.
3x faster delivery with prebuilt agents, playbooks, and reusable assets.
Cross-functional squads trained in GenAI, prompt and agent engineering, and insurance.
GenAI copilots, modular agent libraries, and AI-first SDLC infused with observability.
Embedded AI across Salesforce, Snowflake, Epic, Adobe, ServiceNow, and Databricks.
Insurance industry-safe architecture, audit trails, and agent lifecycle governance.
Deep focus on insurers, brokers, and carriers, tuned to claims, underwriting, customer acquisition, and retention.
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Brillio offers 25+ prebuilt agentic AI use cases purpose-built for insurance carriers and brokers, spanning risk scoring and dynamic pricing, real-time quoting, intelligent form filling, hyper personalization, KYC/AML digital onboarding, and copilots for agent and broker enablement. Each accelerator is designed for specific insurance functions, including claims, underwriting, customer acquisition, and retention, allowing insurers to activate AI across the full policy lifecycle without starting from scratch.
Brillio’s Claims Transformation accelerator uses agentic workflows to automate first notice of loss (FNOL), streamline claims adjudication, and reduce manual errors throughout the claims lifecycle. By applying predictive analytics across the process, Brillio has helped insurers achieve a 60% improvement in claim processing rate while simultaneously lowering operational costs and improving the customer experience.
Brillio’s agentic AI accelerator for insurance is a modular, AI-native framework built on its ADAM platform that combines prebuilt agent libraries, reusable assets, and GenAI copilots to fast-track transformation across core insurance operations. It is architected with observability and agent lifecycle governance built in, and it embeds AI across enterprise platforms like Salesforce, Snowflake, ServiceNow, and Databricks so insurers can operationalize agentic intelligence without rebuilding their technology stack.
Brillio’s Agentic Underwriting and Fraud Detection solution uses structured insights extracted from documents to power analytics-based underwriting, while agentic workflows handle automated risk assessment, risk adjustment, and fraud detection with built-in financial integrity controls. The result is a measurable reduction in underwriting time and a 4% increase in top-line revenue per deal cycle through a real-time AI and analytics underwriting model.
Insurers working with Brillio have achieved outcomes including a 60% improvement in claim processing rate through predictive analytics, a 4% increase in top-line revenue per deal cycle via real-time underwriting, and a 3x improvement in API response rate for policy administration through data transformation and functional migration. These results reflect Brillio’s deep vertical specialization in insurance and its ability to embed production-grade AI into existing carrier and broker operations.
Brillio’s Compliance and Risk Management accelerator automates risk assessment workflows and integrates financial crime and SCMR modules, helping insurance organizations achieve a 30% enhancement in regulatory compliance. The platform also supports KYC and AML digital onboarding processes, ensuring that compliance requirements are embedded directly into operational workflows rather than managed as a separate, manual overhead.
Brillio embeds its agentic AI capabilities directly into the enterprise platforms insurers already rely on, including Salesforce, Snowflake, Epic, Adobe, ServiceNow, and Databricks. This platform-native approach means insurance carriers and brokers can deploy AI agents within their existing technology environments, reducing integration complexity and accelerating time to value across underwriting, claims, and customer engagement functions.
Brillio delivers 3x faster deployment than traditional approaches by combining prebuilt insurance-specific agents, playbooks, and reusable assets with AI-native cross-functional squads trained in both GenAI engineering and insurance domain knowledge. This speed-to-value model means insurers can move from evaluation to production-grade agentic AI without the extended build cycles typical of custom development engagements.
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