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Duration: 3 months
End Users: Brokers within the participating brokerage
Policy Types in Scope: Commercial policies of sufficient size; currently selling specialty products
Data Sources: Client submissions, current policy documents, industry standards information
Hosting & Deployment: Supported by the vendor in a secure environment and integrated with the broker’s systems
Broker IT support and environment readiness: Required to deploy and integrate within the broker’s system
Access to accurate and up-to-date policy documents
Availability of typical coverage patterns and industry guidance
Engaged users and timely feedback loops
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Licensing Costs: Licensing cost of the vendor solution
Integration Costs: API configuration with BMS and ARS
Data Costs: Access to historical client data for training/validation
Hypercare / Ongoing Support: Vendor training, implementation support, and ongoing maintenance during the pilot
Scalability Within Brokerage: Can be scaled across different teams and policy types within an individual brokerage
Scalability Across Brokerages: Can be deployed and scaled across other brokerages
Change Management & Training: Brokers may need targeted training sessions and ongoing support to ensure adoption
Deploy an AI-powered personal lines onboarding agent to streamline client intake. The agent will collect client information, answer onboarding questions, and guide users through the process. It will also process renewal documents from selected carriers, extract key data, and integrate with the BMS to enable faster, more accurate quoting and reduce manual effort.
Sell opportunities profiling and cross-analytics; improves internal data quality and reduces risk.
Reduces back-and-forth communication during intake.
Frees up broker capacity, allowing more time for high-value client engagement.
A structured, guided chatbot interaction with dynamic prompts adapts based on prior inputs to simplify data intake and ensure completeness.
Minimizes manual parsing and transcription time while enabling quick extraction of relevant information.
AI detects inconsistencies, missing fields, or abnormal values, reducing back-and-forth communication and ensuring cleaner, verified data early in the process.
Final validated data is auto-fed into internal systems Generate accurate, timely quotes across selected carriers
All data and interactions are logged centrally for traceability
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Duration: 3 months
End Users: New clients and brokers within the participating brokerage
Business Lines in Scope: Personal lines (home and automobile)
Data Scope: Renewal documents from 5-6 carriers selected by the broker
Hosting & Deployment: Supported by the vendor in a secure environment and integrated into the broker’s systems
Metrics / KPIs: Efficiency improvements, customer satisfaction enhancements, etc.
Accuracy and completeness of onboarding templates and internal rules
Broker system readiness for chatbot integration and validation logic
Availability of onboarding records for pilot calibration
Client consent for use of AI in onboarding process
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
Licensing Costs: Licensing cost of the vendor solution
Integration Costs: API configuration with BMS and ARS
Data Costs: Access to historical onboarding data for training/validation
Hypercare / Ongoing Support: Vendor training, implementation support, and ongoing maintenance during the pilot
Workflow Fit: Onboarding workflows, data validation rules, and exception categories should be mapped to align with the broker’s existing intake processes
Client Experience: Tone, accessibility, and usability of the chatbot should algin with client expectations and brand standards to ensure adoption
Fallback Plan: Manual intake processes should remain available if the chatbot fails / clients opts out
Future Scalability: For broader adoption post-PoC, a dynamic webform can be introduced to deliver a more professional and polished client interface
Speeds up discovery conversations by highlighting key gaps upfront, reducing prep time for brokers
Creates a scalable foundation for broader AI adoption across policy types and lines of business
Mitigates
underinsurance risk and drives revenue through cross-sell and up-sell insights
Enhances quality of coverage
recommendations through data-driven analysis
Facilitates more tailored and information
conversations with clients
AI models with context on industry benchmarks and policy structures to interpret existing policy terms, endorsements, and clauses.
Integration with BMS to retrieve client profiles, exposure information, and historical policy data.
Data ingestion and continuous updates to ensure alignment with typical coverage patterns and industry guidance.
Data security and compliance features to protect client information.
Analyze client-submitted data, including exposures, business context, and other relevant information.
Extract and interpret existing policy terms (e.g., endorsements, exclusions, clauses).
Benchmark against typical coverage patterns and industry guidance to identify coverage gaps.
Prioritize identified coverage gaps and provide rationale based on considerations such as industry standards and risk.
Recommend relevant products and coverage options tailored to the client profile, and generate summaries for client discussions.
BMS Integration: Integration with BMS for secure data capture and storage.
Applied ARS Integration: Integration with Applied ARS to enable automated quote generation.
Dedicated Parsers for Renewal: Document parsing capability for 5–6 carrier renewal documents with high accuracy.
Extensibility Framework: Modular architecture to support future enhancements and additional automation.
AI Chatbot for Client Onboarding: Collect client information, answer onboarding questions, and guide users through the onboarding process.
Data Collection & Storage: Capture and store collected data directly in the broker’s BMS.
Document Processing: Enable clients to upload renewal documents and extract key data to accelerate the onboarding process.
Quotes Generation: Generate quotes based on collected information.
