GenAI Marketing Assistant for InsurTech

Overview

A leading InsurTech services provider partnered with NeenOpal to build a GenAI-powered Marketing Assistant that helps prospects, existing customers, and internal teams understand service offerings and identify relevant solutions through natural language interaction. The goal was to replace static service pages and manual discovery with an intelligent, conversational, and visual experience that enables users to explore services, understand coverage, and receive personalized recommendations based on their profile, usage context, and permissions. NeenOpal delivered a secure, Azure-native Retrieval-Augmented Generation (RAG) solution that provides accurate, grounded, and tenant-safe responses at scale.

95%+

Response Grounding Accuracy (RAG-based)

80–90%

Hallucination Reduction vs LLM-only Responses

<2s

End-to-End AI Response Latency (P95)

100%

Tenant-Isolated AI Responses

85%+

Reduction in Manual Service Explanation Effort

<5s

Knowledge Retrieval and Context Assembly Latency

Customer Challenges

The client identified multiple challenges in how services were marketed and discovered across different customer segments.

Fragmented Service Discovery

Service information was spread across multiple pages and documents, making it difficult for users to understand which services were relevant to their needs.

Limited Personalization

Prospects and customers received generic information, with no visibility into how services related to their existing usage or operational context.

Lack of Visual Service Mapping

Users could not easily visualize relationships between services, current coverage, and potential expansion opportunities.

Risk of Inconsistent or Ungrounded Responses

Manual explanations and static content increased the risk of outdated or inconsistent messaging across teams.

Multi-Tenant Security Requirements

As a multi-tenant InsurTech platform, the solution needed strict enforcement of tenant and role-based access—ensuring users could only view services and recommendations they were authorized to see.

GenAI Marketing Assistant Architecture

An Azure-native RAG architecture that integrates data sources, retrieves relevant knowledge, and generates secure, context-aware responses with personalized recommendations and visual service mapping.

Solutions

NeenOpal designed and implemented a GenAI Marketing Assistant that provides conversational service discovery, grounded explanations, and visual service mapping—while enforcing strong security and governance controls.

01.

Conversational Service Discovery

Users interact with the assistant through an embedded website chat interface, asking natural-language questions about services, coverage, and capabilities. The backend validates each query, identifies intent, and retrieves relevant service information.

02.

Retrieval-Augmented Generation (RAG)

Service definitions, SOPs, and documentation are centrally managed and indexed for semantic retrieval. At runtime, the system retrieves only the most relevant content and injects it into the language model to ensure accurate and grounded responses.

03.

Personalized Recommendations

The platform dynamically incorporates customer-specific context—such as existing services, profile attributes, and usage indicators—to recommend relevant and adjacent services aligned with the user’s needs.

04.

Visual Service Mapping

The system goes beyond plain text responses by generating structured outputs that enable a map-like visualization of services. It presents the services currently availed, highlights related and complementary offerings, and suggests recommended next services. This approach helps users clearly understand their current coverage while visually identifying opportunities for expansion.

05.

Secure Multi-Tenant Architecture

Tenant and role resolution is enforced at the backend level. All retrieval, recommendations, and visual outputs are filtered to ensure users can only access data and service views permitted to their tenant and role.

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Services

Azure API Management

Azure OpenAI

Azure AI Search

Azure Blob Storage

Azure Cosmos DB

Azure Monitor & Application Insights

React (Web)

Benefits

Intuitive Service Discovery

Enabled natural, conversational interactions, making it easier for users to explore and understand services.

Personalized Recommendations

Delivered context-aware service suggestions based on user profile, usage, and permissions.

Enhanced Visual Clarity

Map-based service visualization improved understanding of current coverage and expansion opportunities.

Accurate and Reliable Responses

RAG-based grounding minimized hallucinations and ensured consistent, trustworthy outputs.

Secure Multi-Tenant Access

Enforced strict tenant and role-based controls to maintain data privacy and compliance.

Scalable Cloud Architecture

Built on Azure to support high performance, flexibility, and future growth.

Conclusion

With NeenOpal’s support, the client successfully launched a GenAI-powered Marketing Assistant that transforms how services are discovered, explained, and recommended. By combining conversational AI, retrieval-based grounding, and visual service mapping, the solution delivers a modern, personalized, and secure experience—enabling better customer understanding, stronger engagement, and scalable growth across the InsurTech platform.

FAQ

Here are some common questions about building a GenAI-powered marketing assistant for service discovery:

How does a GenAI marketing assistant improve service discovery?

It allows users to ask natural language questions and receive contextual, relevant service information, replacing static pages with an interactive and personalized experience.

What role does RAG play in this solution?

Retrieval-Augmented Generation ensures responses are grounded in verified documents and service data, improving accuracy and reducing hallucinations compared to standalone LLMs.

How are recommendations personalized for each user?

The system uses user context such as existing services, profile attributes, and permissions to suggest relevant and complementary services tailored to their needs.

How is data security managed in a multi-tenant environment?

Strict tenant and role-based access controls are enforced at the backend, ensuring users only see data and recommendations they are authorized to access.