Insurers process large volumes of complex, unstructured information every day:
The challenge is not simply extracting information. The challenge is interpreting it correctly.
Before AI supports an insurance decision, it needs to determine:
This is the role of semantic control.
Raedan AI applies semantic control across three levels.
Identify where better interpretation of information improves insurance decisions, reduces operational effort or lowers risk.
We focus on the decisions, information dependencies and controls required before selecting technology.
Define the business meaning behind important insurance concepts, relationships and decision rules.
This includes:
Implement these controls through Hybrid AI workflows using proven enterprise technology.
Hybrid AI combines language understanding, knowledge models, machine learning, LLMs and deterministic business logic where each approach adds value.
An enterprise semantic control layer establishes shared meaning, relationships, rules and authoritative sources for every AI application. Instead of each use case interpreting information independently, multiple solutions operate from the same governed foundation. This improves consistency, reduces duplicated effort and turns each implementation into part of a reusable enterprise capability.
Interpret submissions, risk reports and policy information using shared insurance concepts, underwriting guidelines and risk definitions.
Examples of applications
Semantic control ensures underwriters and AI interpret risks, exposures and policy information consistently across brokers, products and channels. It accelerates processing while retaining professional oversight for complex risk decisions.
Expert.ai reports submission-processing improvements of 50–60%. Its risk-engineering case study recorded a 400% increase in review capacity and four hours saved per property review. EIX-Underwriting and Enhancing Risk Engineering Processes with AI
Connect claim events, policy coverage, medical and legal information, supporting evidence and decision rules throughout the claims lifecycle.
Examples of applications
Takeaway
Semantic control links each claim to the applicable policy, exclusions, conditions, evidence and settlement rules. It supports faster decisions while reducing inconsistent assessments, erroneous payments and claims leakage.
Expert.ai reports reductions of more than 90% in document review time. One published implementation achieved a 58% reduction in claim review time and saved eight hours per review. EIX-Claims Automation and Automating Claims Management with AI
Interpret policy wording, coverage, exclusions, endorsements and obligations consistently across products, versions and servicing transactions.
Examples of applications
Semantic control preserves the meaning of policy terms as products, endorsements and regulatory requirements change. It reduces servicing effort while improving coverage certainty, compliance and decision traceability.
Expert.ai reports 95% accuracy in automated policy review and an 80% reduction in policy review times. Its policy-servicing solution embeds business rules, compliance controls, human verification and audit trails across policy transactions. EIX-Policy Servicing and Expert.ai Insurance
Interpret requests and correspondence accurately, route work intelligently and provide responses grounded in approved policy and service information.
Examples of applications
Semantic control gives customer and broker interactions the correct policy, product and operational context. Routine requests move faster, while exceptions reach staff with the authority and expertise required.
An expert.ai insurance implementation analyses and routes more than 5,000 tickets each day, representing over one million requests annually. Making Customer Service Request Management More Effective
Generative AI is useful for analysing and generating language.
Insurance operations require additional controls.
A policy clause, claim event or risk statement rarely has meaning in isolation. Its interpretation depends on definitions, relationships, exclusions, conditions, jurisdiction, authority and supporting evidence.
Raedan AI provides the control layer between enterprise information and AI.
This helps organisations establish:
Raedan AI partners with expert.ai to deliver enterprise Hybrid AI solutions.
The expert.ai EidenAI Suite combines natural language understanding, knowledge models, machine learning, LLMs and orchestration capabilities within an enterprise AI platform.
Raedan AI provides the strategy, governance and semantic architecture required to apply these capabilities within your insurance environment.
Together, these capabilities move AI from document processing toward governed decision support.
The starting point is not another AI pilot.
It is identifying where inconsistent interpretation, unclear business rules or weak evidence controls constrain underwriting, claims or policy operations.
Raedan AI helps you define the semantic controls first, then operationalise them through Hybrid AI.
Discuss an Insurance Use Case
Explore: expert.ai Insurance Services | case studies
